text_analytics_assignments.ipynb

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      "source": [
        "# 1: Pluralize nouns\n",
        "Purpose: convert a singular noun (supplied by the user)\n",
        "to its plural form.\n",
        "\n",
        "Reference: https://www.grammarly.com/blog/plural-nouns/"
      ],
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          "text": [
            "Welcome to the noun pluralizer!\n",
            "This program returns the plural form of a singular noun you enter.\n",
            "\n",
            "Type a noun in its singular form: analysis\n",
            "analyses\n"
          ]
        }
      ],
      "source": [
        "# Assignment 01, LING 360, Text Analytics in Python\n",
        "# Updated on: 13-Jan-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# =========================\n",
        "# Noun pluralizer\n",
        "# =========================\n",
        "'''\n",
        "Purpose: convert a singular noun (supplied by the user)\n",
        "to its plural form.\n",
        "Reference: https://www.grammarly.com/blog/plural-nouns/\n",
        "'''\n",
        "# Add'l references, used in code for Rule 5:\n",
        "# https://en.wikipedia.org/wiki/List_of_Unicode_characters\n",
        "# https://www.w3schools.com/python/ref_func_chr.asp\n",
        "\n",
        "print('Welcome to the noun pluralizer!',\n",
        "'\\nThis program returns the plural form of a singular noun you enter.\\n', sep='')\n",
        "singular_noun = input('Type a noun in its singular form: ')\n",
        "singular_noun = singular_noun.lower()\n",
        "\n",
        "# Create a list of consonants; used in Rule 5\n",
        "consonants = [chr(code) for code in range(97,123) if chr(code) not in ['a', 'e', 'i', 'o', 'u']]\n",
        "\n",
        "# Irregulars\n",
        "irregular_nouns = {\n",
        "    'child': 'children',\n",
        "    'goose': 'geese',\n",
        "    'woman': 'women',\n",
        "    'man': 'men',\n",
        "    'tooth': 'teeth',\n",
        "    'foot': 'feet',\n",
        "    'mouse': 'mice',\n",
        "    'person': 'people'\n",
        "}\n",
        "\n",
        "# Determine plural form by examining the word ending\n",
        "try:\n",
        "    last_2_chars = singular_noun[-2:]\n",
        "except IndexError:\n",
        "    # word has fewer than 2 letters\n",
        "    last_2_chars = None\n",
        "last_1_char = singular_noun[-1]\n",
        "\n",
        "# Rule 11: plural form is same as singular\n",
        "if singular_noun in ['fish', 'deer', 'sheep', 'series', 'species']:\n",
        "    plural_noun = singular_noun\n",
        "# Check for irregulars\n",
        "elif singular_noun in irregular_nouns.keys():\n",
        "    plural_noun = irregular_nouns[singular_noun]\n",
        "# Rule 8: change '-us' to '-i'\n",
        "elif last_2_chars == 'us':\n",
        "    plural_noun = singular_noun[:-2] + 'i'\n",
        "# Rule 9: change '-is' to '-es'\n",
        "elif last_2_chars == 'is':\n",
        "    plural_noun = singular_noun[:-2] + 'es'\n",
        "# Rules 2 and 7: add -es to make noun plural\n",
        "elif last_1_char in ['s', 'x', 'z', 'o'] or last_2_chars in ['ss', 'sh', 'ch']:\n",
        "    # check for exceptions\n",
        "    if singular_noun in ['photo', 'piano', 'halo']:\n",
        "        plural_noun = singular_noun + 's'\n",
        "    else:\n",
        "        plural_noun = singular_noun + 'es'\n",
        "# Rule 4: change an '-f' or an '-fe' to 'v' prior to pluralizing\n",
        "elif last_1_char == 'f' or last_2_chars == 'fe':\n",
        "    # check for exceptions\n",
        "    if singular_noun in ['roof', 'belief', 'chef', 'chief']:\n",
        "        plural_noun = singular_noun + 's'\n",
        "    else:\n",
        "        if last_1_char == 'f':\n",
        "            plural_noun = singular_noun[:-1] + 'ves'\n",
        "        elif last_2_chars == 'fe':\n",
        "            plural_noun = singular_noun[:-2] + 'ves'\n",
        "# Rule 5: change '-y' to '-ies' if the letter before the '-y' is a consonant\n",
        "elif last_2_chars in [char + 'y' for char in consonants]:\n",
        "    plural_noun = singular_noun[:-1] + 'ies'\n",
        "# Rule 10: change '-on' to '-a'\n",
        "elif last_2_chars == 'on':\n",
        "    plural_noun = singular_noun[:-2] + 'a'\n",
        "# No strange rules used, so go with rule 1: just add '-s' to the end\n",
        "else:\n",
        "    plural_noun = singular_noun + 's'\n",
        "\n",
        "# Return the result\n",
        "print(plural_noun)\n",
        "\n",
        "# Optional, if running from the Python terminal\n",
        "# rather than Visual Studio Code.\n",
        "# input('\\nPress the [''Enter''] key to exit. ')"
      ]
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        "# 2: Find nominalizations\n",
        "Purpose: count all occurrences of a nomilzation, then return accuracy metrics (precision and recall).\n",
        "\n",
        "Reference: http://www.dailywritingtips.com/nominalized-verbs/\n",
        "\n",
        "## Precision and Recall\n",
        "\n",
        "**[From Wikipedia:](https://en.wikipedia.org/wiki/Precision_and_recall)** \"_Precision_ can be thought of as a measure of quality, and _recall_ as a measure of quantity. Higher precision means that an algorithm returns more relevant results than irrelevant ones, and high recall means that an algorithm returns most of the relevant results (whether or not irrelevant ones are also returned).\"\n",
        "* **Type I error, or False positive:** classifying something as _X_ when it really was _not X_. That is, you guessed _X_ (positive) and you were wrong (false).\n",
        "* **Type II error, or False negative:** classifying something as _not X_ when it really was _X_. That is, you guessed _not X_ (negative) and you were wrong (false).\n",
        "\n",
        "**Precision for classifying `X`**: `num_correct_Xs_guessed ÷ num_Xs_guessed`. Ranges from 0-1, with 1 as the best (there were no false posities). The reciprocal is false positive rate (the percentage of items guessed as `X` that were wrong).\n",
        "\n",
        "**Recall for classifying `X`**: `num_correct_Xs_guessed ÷ num_true_Xs`. Ranges from 0-1, with 1 as the best (there were no false negatives). The reciprocal is false negative rate (the percentage of items guessed as `not X` that were wrong)."
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      "source": [
        "# The text used in this example comes from the preamble, \n",
        "# first, and second sections of the United States Consitution. \n",
        "# Source: https://www.gutenberg.org/ebooks/5\n",
        "input_txt = \"\"\"\n",
        "THE CONSTITUTION OF THE UNITED STATES OF AMERICA, 1787\n",
        "\n",
        "We the people of the United States, in Order to form a more perfect Union,\n",
        "establish Justice, insure domestic Tranquility, provide for the common defence,\n",
        "promote the general Welfare, and secure the Blessings of Liberty to ourselves\n",
        "and our Posterity, do ordain and establish this Constitution for the\n",
        "United States of America.\n",
        "\n",
        "Article 1\n",
        "\n",
        "Section 1.  All legislative Powers herein granted shall be vested in a\n",
        "Congress of the United States, which shall consist of a Senate and\n",
        "House of Representatives.\n",
        "\n",
        "Section 2.  The House of Representatives shall be composed of Members\n",
        "chosen every second Year by the People of the several States,\n",
        "and the electors in each State shall have the qualifications requisite\n",
        "for electors of the most numerous branch of the State legislature.\n",
        "\n",
        "No Person shall be a Representative who shall not have attained to the\n",
        "Age of twenty five Years, and been seven Years a citizen of the United States,\n",
        "and who shall not, when elected, be an Inhabitant of that State in which\n",
        "he shall be chosen.\n",
        "\n",
        "Representatives and direct Taxes shall be apportioned among\n",
        "the several States which may be included within this Union,\n",
        "according to their respective Numbers, which shall be determined\n",
        "by adding to the whole number of free Persons, including those\n",
        "bound to Service for a Term of Years, and excluding Indians not taxed,\n",
        "three fifths of all other Persons.  The actual Enumeration shall be made\n",
        "within three Years after the first Meeting of the Congress of the\n",
        "United States, and within every subsequent Term of ten Years,\n",
        "in such Manner as they shall by law Direct.  The number of\n",
        "Representatives shall not exceed one for every thirty Thousand,\n",
        "but each State shall have at least one Representative;\n",
        "and until such enumeration shall be made, the State of New Hampshire\n",
        "shall be entitled to chuse three, Massachusetts eight, Rhode Island\n",
        "and Providence Plantations one, Connecticut five, New York six,\n",
        "New Jersey four, Pennsylvania eight, Delaware one, Maryland six,\n",
        "Virginia ten, North Carolina five, South Carolina five, and Georgia three.\n",
        "\n",
        "When vacancies happen in the Representation from any State, the Executive\n",
        "Authority thereof shall issue Writs of Election to fill such Vacancies.\n",
        "\n",
        "The House of Representatives shall chuse their Speaker and other Officers;\n",
        "and shall have the sole Power of Impeachment.\n",
        "\"\"\"\n",
        "\n",
        "tokens = input_txt.split()\n",
        "\n",
        "print('Word count: ', len(tokens), sep='')"
      ],
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          "text": [
            "Word count: 387\n"
          ]
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        "# Assignment 02, LING 360, Text Analytics in Python\n",
        "# Updated on: 21-Jan-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# =========================\n",
        "# Count nominalizations\n",
        "# =========================\n",
        "'''\n",
        "Purpose: count the number of nominalizations in an input text.\n",
        "Also returns information about accuracy: precision and recall.\n",
        "\n",
        "Reference: http://www.dailywritingtips.com/nominalized-verbs/\n",
        "Author: Ryan Parker\n",
        "Updated on: 21-Jan-2022\n",
        "\n",
        "Sample input text comes from the preamble and first two sections\n",
        "of the United States Constitution.\n",
        "Source: https://www.gutenberg.org/ebooks/5\n",
        "'''\n",
        "# Regular expressions module\n",
        "import re\n",
        "\n",
        "# First run the cell above to load a value for input_txt\n",
        "print('Length of input text:', len(input_txt.split()), 'words\\n')\n",
        "\n",
        "# The true_nominalizations list is used for testing precision and recall\n",
        "# I reviewed the text word-by-word to determine which words are\n",
        "# true nominalizations. There may be errors, but I did my best.\n",
        "true_nominalizations = [\n",
        "    'CONSTITUTION', 'Union', 'Justice', 'Tranquility', 'defence',\n",
        "    'Liberty', 'Posterity', 'Constitution', 'Section',\n",
        "    'Representatives', 'Section', 'Representatives', 'electors',\n",
        "    'qualifications', 'electors', 'legislature', 'Representative',\n",
        "    'Inhabitant', 'Representatives', 'Union', 'Enumeration',\n",
        "    'Meeting', 'Representatives', 'Representative', 'enumeration',\n",
        "    'vacancies', 'Representation', 'Authority', 'Election', 'Vacancies',\n",
        "    'Representatives', 'Speaker', 'Officers', 'Impeachment'\n",
        "]\n",
        "\n",
        "\n",
        "# Gerunds, words that end in '-ing'\n",
        "# pattern: one or more alphanumeric characters \n",
        "# followed by -ing and then a word break.\n",
        "pattern = r'\\w+ing\\b'\n",
        "gerunds = re.findall(pattern=pattern, string=input_txt, flags=re.IGNORECASE)\n",
        "\n",
        "# Agent nouns, which end in '-or' or '-er'\n",
        "# pattern: two or more alphanumeric characters\n",
        "# followed by -or(s) or -er(s) and then a word break.\n",
        "pattern = r'\\w{2,}(or|er)s?\\b'  # or, r'\\w+or[s]?\\b|\\w+er[s]?\\b' to avoid use of capture groups\n",
        "agent_nouns = [r.group() for r in re.finditer(\n",
        "    pattern=pattern, string=input_txt, flags=re.IGNORECASE)]\n",
        "\n",
        "# Recipient nouns, which end in '-ee'\n",
        "# pattern: one or more alphanumeric characters (not 'thr' [from three])\n",
        "# followed by -ee(s) and then a word break.\n",
        "# See: https://stackoverflow.com/questions/977251/regular-expressions-and-negating-a-whole-character-group/977294\n",
        "# for help using a \"negative look-ahead\" to eliminate sequences \n",
        "# that start with 'thr' and end in 'ee(s)' (that is, the word 'three(s)')\n",
        "pattern = r'\\w+ee[s]?\\b'\n",
        "recipient_nouns = re.findall(pattern=pattern, string=input_txt, flags=re.IGNORECASE)\n",
        "\n",
        "# Others: words that end in -tion, -sion, -ment, -ence, -ance, and -tive\n",
        "# pattern: one or more alphanumeric characters\n",
        "# followed by -tion(s), -sion(s), -ment(s), -ence(s), -ance(s), -tive(s) and then a word break.\n",
        "pattern = r'\\w+(tion|sion|ment|ence|ance|tive)s?\\b'\n",
        "others = [r.group() for r in re.finditer(\n",
        "    pattern=pattern, string=input_txt, flags=re.IGNORECASE)]\n",
        "\n",
        "# Note, there are many others that are trickier to catch,\n",
        "# like progress, overpass, surface, journey, dive, \n",
        "# workout, setup, record, vist, number, etc.\n",
        "# I have not handled any of those tricky forms in this code.\n",
        "\n",
        "# Combine all lists into a single one\n",
        "all_nominalizations = []\n",
        "all_nominalizations.extend(gerunds)\n",
        "all_nominalizations.extend(agent_nouns)\n",
        "all_nominalizations.extend(recipient_nouns)\n",
        "all_nominalizations.extend(others)\n",
        "\n",
        "# Determine the number of correct predictions\n",
        "# ===========================================\n",
        "# First, create word frequency dictionaries\n",
        "true_dict = {}\n",
        "for word in true_nominalizations:\n",
        "    word = word.lower()\n",
        "    if word in true_dict.keys():\n",
        "        true_dict[word] += 1\n",
        "    else:\n",
        "        true_dict[word] = 1\n",
        "\n",
        "predictions_dict = {}\n",
        "for word in all_nominalizations:\n",
        "    word = word.lower()\n",
        "    if word in predictions_dict.keys():\n",
        "        predictions_dict[word] += 1\n",
        "    else:\n",
        "        predictions_dict[word] = 1\n",
        "\n",
        "# Next, count the times a word was correctly identified\n",
        "correct_predictions = 0.0\n",
        "for word in true_dict.keys():\n",
        "    if word in predictions_dict.keys():\n",
        "        correct_predictions += min(true_dict[word], predictions_dict[word])\n",
        "\n",
        "incorrect_predictions = [word for word in predictions_dict.keys() if word not in true_dict.keys()]\n",
        "\n",
        "# Compute accuracy\n",
        "# =================\n",
        "# precision (correct_predictions ÷ total_predictions)\n",
        "acc_precision = correct_predictions / float(len(all_nominalizations))\n",
        "\n",
        "# recall (correct_predictions ÷ true_nominalizations)\n",
        "acc_recall = correct_predictions / float(len(true_nominalizations))\n",
        "\n",
        "\n",
        "# Display results\n",
        "print('True nominalizations:', len(true_nominalizations))\n",
        "print('Predicted nominalizations:', len(all_nominalizations))\n",
        "print('Precision:', f'{acc_precision:.2%}')\n",
        "print('Recall:', f'{acc_recall:.2%}')\n",
        "print('==========================\\n')\n",
        "print('True nominalizations:', true_nominalizations)\n",
        "print('\\nGerunds, count:', len(gerunds), '\\n', gerunds)\n",
        "print('\\nAgent nouns, count:', len(agent_nouns), '\\n', agent_nouns)\n",
        "print('\\nRecipient nouns, count:', len(recipient_nouns), '\\n', recipient_nouns)\n",
        "print('\\nOthers, count:', len(others), '\\n', others)\n",
        "print('\\n==========================\\n')\n",
        "print('Incorrect predictions:', incorrect_predictions)"
      ],
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          "text": [
            "Length of input text: 387 words\n",
            "\n",
            "True nominalizations: 34\n",
            "Predicted nominalizations: 43\n",
            "Precision: 53.49%\n",
            "Recall: 67.65%\n",
            "==========================\n",
            "\n",
            "True nominalizations: ['CONSTITUTION', 'Union', 'Justice', 'Tranquility', 'defence', 'Liberty', 'Posterity', 'Constitution', 'Section', 'Representatives', 'Section', 'Representatives', 'electors', 'qualifications', 'electors', 'legislature', 'Representative', 'Inhabitant', 'Representatives', 'Union', 'Enumeration', 'Meeting', 'Representatives', 'Representative', 'enumeration', 'vacancies', 'Representation', 'Authority', 'Election', 'Vacancies', 'Representatives', 'Speaker', 'Officers', 'Impeachment']\n",
            "\n",
            "Gerunds, count: 5 \n",
            " ['according', 'adding', 'including', 'excluding', 'Meeting']\n",
            "\n",
            "Agent nouns, count: 15 \n",
            " ['Order', 'Powers', 'Members', 'electors', 'electors', 'Numbers', 'number', 'other', 'after', 'Manner', 'number', 'Speaker', 'other', 'Officers', 'Power']\n",
            "\n",
            "Recipient nouns, count: 0 \n",
            " []\n",
            "\n",
            "Others, count: 23 \n",
            " ['CONSTITUTION', 'defence', 'Constitution', 'Section', 'legislative', 'Representatives', 'Section', 'Representatives', 'qualifications', 'Representative', 'Representatives', 'respective', 'Enumeration', 'Representatives', 'Representative', 'enumeration', 'Providence', 'Plantations', 'Representation', 'Executive', 'Election', 'Representatives', 'Impeachment']\n",
            "\n",
            "==========================\n",
            "\n",
            "Incorrect predictions: ['according', 'adding', 'including', 'excluding', 'order', 'powers', 'members', 'numbers', 'number', 'other', 'after', 'manner', 'power', 'legislative', 'respective', 'providence', 'plantations', 'executive']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3: Compare formal and informal texts\n",
        "31-Jan-2022\n",
        "\n",
        "**Purpose:** compare the word usage between formal and informal texts.\n",
        "\n",
        "**Returns:** counts of the number of subject pronouns, contractions, and modal verbs across both the formal and informal texts.\n",
        "\n",
        "**Text sources**\n",
        "* **Formal:** [\"The Love of God\"](https://www.churchofjesuschrist.org/study/general-conference/2021/10/15christofferson?lang=eng) by Elder D. Todd Christofferson, October 2021 general conference, ChurchOfJesusChrist.org\n",
        "* **Informal:** [\"Simple Reminders for Finding Your Way through Life\"](https://www.churchofjesuschrist.org/study/ya-weekly/2022/01/simple-reminders-for-finding-your-way-through-life?lang=eng) by Erin Murdock, YA Weekly, Jan 2022, ChurchOfJesusChrist.org\n"
      ],
      "metadata": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Informal text\n",
        "informal_text = '''\n",
        "Millions of people around the world watched on television as the first humans walked on the moon. My dad was one of them. Suddenly, at only age seven, he knew what he wanted to do with his life.\n",
        "\n",
        "When he learned that many astronauts started out as fighter pilots, he decided right then what he wanted to focus on in school—aerospace engineering. He eventually became a fighter pilot in the United States Navy with the dream of becoming an astronaut.\n",
        "\n",
        "While I was growing up, I felt like my life needed to be mapped out too. But it wasn’t so easy for me to decide what I wanted to do. I felt like I wasn’t smart enough, dedicated enough, or brave enough to set a course for my life and stick with it. To be honest, I still struggle with this sometimes.\n",
        "\n",
        "As I’ve gotten older, I’ve learned that many young people feel the same way. Few people know exactly what path to take early on. Most of us figure it out as we go. And that’s all right! Thankfully, we don’t have to go it alone. There is help along the way.\n",
        "\n",
        "Remember What Matters Most\n",
        "Being unsure of what direction I wanted to take caused me to often feel uneasy. I felt that I should be doing something else or be somewhere else. I felt like there was just one thing that Heavenly Father wanted me to do, and if I missed it, I would derail my opportunities for eternity. As you can imagine, this created a lot of unnecessary anxiety in my life!\n",
        "\n",
        "I’ve since learned that Heavenly Father has blessed each of us with spiritual gifts and that there are many good things we can do that will please Him. We don’t need to worry about finding the one and only path for our life. As long as we’re living the gospel and making and keeping sacred covenants, He will help us make the most of where we are now and know how to move forward.\n",
        "\n",
        "Our Small Choices Add Up\n",
        "When I received my patriarchal blessing, one part stood out to me—something that applies to all of us. I was reminded that getting to the celestial kingdom isn’t done by one decisive act or one great deed. It’s done in the choices—big and small—that we make each day, particularly as we choose to come unto Jesus Christ and live His gospel.\n",
        "\n",
        "As we take action in all areas of our lives with faith in Heavenly Father and Jesus Christ, we put ourselves in a position to be guided. We’ll also find that wherever They lead us will be exactly where we want to be too.\n",
        "\n",
        "Things Will Work Out\n",
        "Even with the best-laid plans, things don’t always turn out the way we think they will. My dad’s life didn’t. He never became an astronaut, but he’d be the first to tell you that the Lord guided him to what was best for him and our family.\n",
        "'''"
      ],
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    {
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      "source": [
        "formal_text = '''\n",
        "Our Heavenly Father loves us profoundly and perfectly. In His love, He created a plan, a plan of redemption and happiness to open to us all the opportunities and joys we are willing to receive, up to and including all that He has and is. To achieve this, He was even willing to offer His Beloved Son, Jesus Christ, as our Redeemer. “For God so loved the world, that he gave his only begotten Son, that whosoever believeth in him should not perish, but have everlasting life.” His is a Father’s pure love—universal to all yet personal to each.\n",
        "\n",
        "Jesus Christ shares with the Father this same perfect love. When the Father first elaborated His great plan of happiness, He called for one to act as a Savior to redeem us—an essential part of that plan. Jesus volunteered, “Here am I, send me.” The Savior “doeth not anything save it be for the benefit of the world; for he loveth the world, even that he layeth down his own life that he may draw all men unto him. Wherefore, he commandeth none that they shall not partake of his salvation.”\n",
        "\n",
        "This divine love should give us abundant comfort and confidence as we pray to the Father in the name of Christ. Not one of us is a stranger to Them. We need not hesitate to call upon God, even when we feel unworthy. We can rely on the mercy and merits of Jesus Christ to be heard. As we abide in God’s love, we depend less and less on the approval of others to guide us.\n",
        "\n",
        "The Love of God Does Not Excuse Sin; Rather, It Offers Redemption\n",
        "Because God’s love is all-embracing, some speak of it as “unconditional,” and in their minds they may project that thought to mean that God’s blessings are “unconditional” and that salvation is “unconditional.” They are not. Some are wont to say, “The Savior loves me just as I am,” and that is certainly true. But He cannot take any of us into His kingdom just as we are, “for no unclean thing can dwell there, or dwell in his presence.” Our sins must first be resolved.\n",
        "\n",
        "Professor Hugh Nibley once noted that the kingdom of God cannot endure if it indulges even the smallest sin: “The slightest taint of corruption means that the other world would be neither incorruptible nor eternal. The tiniest flaw in a building, institution, code, or character will inevitably prove fatal in the long run of eternity.” The commandments of God are “strict” because His kingdom and its citizens can stand only if they consistently reject evil and choose good, without exception.\n",
        "\n",
        "Elder Jeffrey R. Holland observed, “Jesus clearly understood what many in our modern culture seem to forget: that there is a crucial difference between the commandment to forgive sin (which He had an infinite capacity to do) and the warning against condoning it (which He never ever did even once).”\n",
        "\n",
        "Despite our present imperfections, however, we can still hope to attain “a name and standing,” a place, in His Church and in the celestial world.\n",
        "'''"
      ],
      "metadata": {
        "id": "TXeEfc2zSxQz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# True counts, used for calculating precision and recall\n",
        "# Subject pronouns, informal\n",
        "sp_i_truth = ['I', 'I', 'I', 'I', 'I', 'I', 'I', 'I', 'I', 'I', 'I', 'I', 'I',\n",
        "              'I', 'I', 'I', 'I', 'you', 'you', 'he', 'he', 'he', 'he', 'he',\n",
        "              'He', 'He', 'He', 'he', 'we', 'we', 'we', 'We', 'we', 'we', 'we',\n",
        "              'we', 'we', 'we', 'We', 'we', 'we', 'They', 'they']\n",
        "\n",
        "# Subject pronouns, formal\n",
        "sp_f_truth = ['I', 'I', 'He', 'He', 'He', 'he', 'He', 'he', 'he', 'he', 'he',\n",
        "              'He', 'He', 'He', 'we', 'we', 'We', 'we', 'We', 'we', 'we', 'we',\n",
        "              'we', 'they', 'they', 'They', 'they']\n",
        "\n",
        "# Modal verbs, informal\n",
        "mv_i_truth = ['can', 'can', 'should', 'would', 'will', 'will', 'will', 'Will',\n",
        "              'will', 'need to', 'have to']\n",
        "\n",
        "# Modal verbs, formal\n",
        "mv_f_truth = ['can', 'can', 'can', 'can', 'may', 'may', 'should', 'should',\n",
        "              'would', 'will', 'must', 'shall']\n",
        "\n",
        "# Contractions, informal\n",
        "c_i_truth = [\"wasn't\", \"wasn't\", \"I've\", \"I've\", \"that's\", \"don't\", \"I've\",\n",
        "             \"don't\", \"we're\", \"isn't\", \"It's\", \"We'll\", \"don't\", \"didn't\",\n",
        "             \"he'd\"]\n",
        "\n",
        "# Contractions, formal\n",
        "c_f_truth = []"
      ],
      "metadata": {
        "id": "Mowk5Gen8nHA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accuracy function: precision and recall"
      ],
      "metadata": {
        "id": "pShxOeyL6RNN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================\n",
        "# Calculate accuracy scores, precision and recall\n",
        "# ===============================================\n",
        "def accuracy(true_list, prediction_list):\n",
        "    '''\n",
        "    This function returns a dictionary with\n",
        "    scores for precision and recall based on\n",
        "    the inputs `true_list` and `prediction_list`.\n",
        "\n",
        "    Return format\n",
        "    ---\n",
        "    ```\n",
        "    {\n",
        "        'precision': precision_score (float),\n",
        "        'recall': recall_score (float)\n",
        "    }\n",
        "    ```\n",
        "\n",
        "    Parameters\n",
        "    ---\n",
        "    `true_list`: list of str\n",
        "        The list of all true positive strings.\n",
        "    `prediction_list`: list of str\n",
        "        The list of all predicted positive strings.\n",
        "\n",
        "    Example\n",
        "    ---\n",
        "    ```\n",
        "    >>> acc = accuracy(true_list=['A', 'A', 'b', 'c'], prediction_list=['A', 'a', 'b', 'b', 'C'])\n",
        "    >>> acc['precision']\n",
        "    ... 0.4\n",
        "    >>> acc['recall']\n",
        "    ... 0.5\n",
        "    \n",
        "    # Or, to see the calculation\n",
        "    correct_predictions = ['A', 'b']\n",
        "    precision = len(correct_predictions) / len(prediction_list) = 2/5 = 0.4\n",
        "    recall = len(correct_predictions) / len(true_list) = 2/4 = 0.5\n",
        "    ```\n",
        "\n",
        "    Calculation\n",
        "    ---\n",
        "    precision = (num_correct_predictions / total_predictions)\n",
        "    recall = (num_correct_predictions / num_true_values)\n",
        "    '''\n",
        "    # First, create word frequency dictionaries\n",
        "    true_dict = {}\n",
        "    for word in true_list:\n",
        "        word = word.lower()\n",
        "        if word in true_dict:\n",
        "            true_dict[word] += 1\n",
        "        else:\n",
        "            true_dict[word] = 1\n",
        "\n",
        "    predictions_dict = {}\n",
        "    for word in prediction_list:\n",
        "        word = word.lower()\n",
        "        if word in predictions_dict:\n",
        "            predictions_dict[word] += 1\n",
        "        else:\n",
        "            predictions_dict[word] = 1\n",
        "\n",
        "    # Next, count the times a word was correctly identified.\n",
        "    # The correct predictions for a word is the minimum of the true\n",
        "    # word count and the predicted word count (e.g., if true count = 2\n",
        "    # and predicted count = 3, the correct number of predictions was 2).\n",
        "    correct_predictions = 0.0\n",
        "    for word in true_dict:\n",
        "        if word in predictions_dict:\n",
        "            correct_predictions += min(true_dict[word], predictions_dict[word])\n",
        "\n",
        "    # Compute accuracy\n",
        "    # =================\n",
        "    # precision (correct_predictions ÷ total_predictions)\n",
        "    acc_precision = correct_predictions / float(len(prediction_list))\n",
        "\n",
        "    # recall (correct_predictions ÷ true_list)\n",
        "    if len(true_list) == 0:\n",
        "        acc_recall = 1\n",
        "        print('There were no values in true_list, so precision is 0% and recall is 100%')\n",
        "    else:\n",
        "        acc_recall = correct_predictions / float(len(true_list))\n",
        "\n",
        "    return {'precision': acc_precision, 'recall': acc_recall}\n"
      ],
      "metadata": {
        "id": "CT_HSamNDH_X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assignment 03, LING 360, Text Analytics in Python\n",
        "# Updated on: 31-Jan-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# =================================\n",
        "# Compare formal and informal texts\n",
        "# =================================\n",
        "'''\n",
        "Purpose: compare the word usage between formal and informal texts.\n",
        "Returns counts of the number of subject pronouns, contractions, \n",
        "and modal verbs across both the formal and informal texts.\n",
        "Also returns information about accuracy: precision and recall.\n",
        "\n",
        "Author: Ryan Parker\n",
        "Updated on: 31-Jan-2022\n",
        "\n",
        "Sample input texts:\n",
        "1. Informal: \"Simple Reminders for Finding Your Way through Life\" \n",
        "by Erin Murdock, YA Weekly, Jan 2022, ChurchOfJesusChrist.org. \n",
        "https://www.churchofjesuschrist.org/study/ya-weekly/2022/01/simple-reminders-for-finding-your-way-through-life?lang=eng\n",
        "2. Formal: \"The Love of God\" by Elder D. Todd Christofferson\n",
        "October 2021 general conference, ChurchOfJesusChrist.org\n",
        "https://www.churchofjesuschrist.org/study/general-conference/2021/10/15christofferson?lang=eng\n",
        "'''\n",
        "# Regular expressions module\n",
        "import re\n",
        "\n",
        "# Replace single left or right apostrophe marks (common in text from webpages)\n",
        "# with a single apostrophe.\n",
        "# See: https://stackoverflow.com/questions/24358361/removing-u2018-and-u2019-character\n",
        "# \\u2018 = ‘ and \\u2019 = ’\n",
        "informal_text = re.sub(pattern=r'(\\u2018|\\u2019)', repl=\"'\", string=informal_text)\n",
        "formal_text = re.sub(pattern=r'(\\u2018|\\u2019)', repl=\"'\", string=formal_text)\n",
        "print('Word count, informal text:', len(informal_text.split()), 'words')\n",
        "print('Word count, formal text:', len(formal_text.split()), 'words\\n')\n",
        "\n",
        "# See: https://www.grammarly.com/blog/the-basics-on-subject-and-object-pronouns-b/\n",
        "# Or, the complete list: https://en.wikipedia.org/wiki/Subject_pronoun\n",
        "subject_pronouns = ['I', 'you', 'she', 'he', 'we', 'they', 'who']\n",
        "# See: https://www.grammarly.com/blog/modal-verbs/\n",
        "modal_verbs = ['can', 'may', 'might', 'could', 'should', 'would', 'will',\n",
        "               'must', 'shall', 'ought to', 'got to', 'need to', 'have to']\n",
        "\n",
        "# Produce lists (to be counted later using len())\n",
        "# sp = subject pronouns\n",
        "sp_i = []\n",
        "sp_f = []\n",
        "for word in subject_pronouns:\n",
        "    sp_i.extend(re.findall(\n",
        "        pattern=r'\\b' + word + r'\\b',\n",
        "        string=informal_text,\n",
        "        flags=re.IGNORECASE))\n",
        "    sp_f.extend(re.findall(\n",
        "        pattern=r'\\b' + word + r'\\b',\n",
        "        string=formal_text,\n",
        "        flags=re.IGNORECASE))\n",
        "\n",
        "# mv = modal verbs\n",
        "mv_i = []\n",
        "mv_f = []\n",
        "for word in modal_verbs:\n",
        "    mv_i.extend(re.findall(\n",
        "        pattern=r'\\b' + word + r'\\b',\n",
        "        string=informal_text,\n",
        "        flags=re.IGNORECASE))\n",
        "    mv_f.extend(re.findall(\n",
        "        pattern=r'\\b' + word + r'\\b',\n",
        "        string=formal_text,\n",
        "        flags=re.IGNORECASE))\n",
        "\n",
        "# c_ = contractions\n",
        "# Count contractions by searching for apostrophes between other letters\n",
        "c_i = re.findall(pattern=r\"\\w+'\\w+\", string=informal_text)\n",
        "c_f = re.findall(pattern=r\"\\w+'\\w+\", string=formal_text)\n",
        "\n",
        "# Calculate precision and recall, using function defined above\n",
        "acc_sp_i = accuracy(true_list=sp_i_truth, prediction_list=sp_i)\n",
        "acc_sp_f = accuracy(true_list=sp_f_truth, prediction_list=sp_f)\n",
        "acc_mv_i = accuracy(true_list=mv_i_truth, prediction_list=mv_i)\n",
        "acc_mv_f = accuracy(true_list=mv_f_truth, prediction_list=mv_f)\n",
        "acc_c_i = accuracy(true_list=c_i_truth, prediction_list=c_i)\n",
        "acc_c_f = accuracy(true_list=c_f_truth, prediction_list=c_f)\n",
        "\n",
        "# Return counts as printed tables\n",
        "print('=================+==========+========================+')\n",
        "print('Type: Informal   |  Count   |  Precision  |  Recall  |')\n",
        "print('-----------------+----------+-------------+----------|')\n",
        "print(f'Subject pronouns |{len(sp_i):^10}|{acc_sp_i[\"precision\"]:^13.0%}|{acc_sp_i[\"recall\"]:^10.0%}|')\n",
        "print('-----------------+----------+-------------+----------|')\n",
        "print(f'Modal verbs      |{len(mv_i):^10}|{acc_mv_i[\"precision\"]:^13.0%}|{acc_mv_i[\"recall\"]:^10.0%}|')\n",
        "print('-----------------+----------+-------------+----------|')\n",
        "print(f'Contractions     |{len(c_i):^10}|{acc_c_i[\"precision\"]:^13.0%}|{acc_c_i[\"recall\"]:^10.0%}|')\n",
        "print('=================+==========+=============+==========+')\n",
        "\n",
        "print('\\n')\n",
        "print('=================+==========+========================+')\n",
        "print('Type: Formal     |  Count   |  Precision  |  Recall  |')\n",
        "print('-----------------+----------+-------------+----------|')\n",
        "print(f'Subject pronouns |{len(sp_f):^10}|{acc_sp_f[\"precision\"]:^13.0%}|{acc_sp_f[\"recall\"]:^10.0%}|')\n",
        "print('-----------------+----------+-------------+----------|')\n",
        "print(f'Modal verbs      |{len(mv_f):^10}|{acc_mv_f[\"precision\"]:^13.0%}|{acc_mv_f[\"recall\"]:^10.0%}|')\n",
        "print('-----------------+----------+-------------+----------|')\n",
        "print(f'Contractions     |{len(c_f):^10}|{acc_c_f[\"precision\"]:^13.0%}|{acc_c_f[\"recall\"]:^10.0%}|')\n",
        "print('=================+==========+=============+==========+')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxAKgHDtKaWN",
        "outputId": "36d0d490-dd41-4209-f20e-149a9dc65526"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word count, informal text: 501 words\n",
            "Word count, formal text: 518 words\n",
            "\n",
            "There were no values in true_list, so precision is 0% and recall is 100%\n",
            "=================+==========+========================+\n",
            "Type: Informal   |  Count   |  Precision  |  Recall  |\n",
            "-----------------+----------+-------------+----------|\n",
            "Subject pronouns |    43    |    100%     |   100%   |\n",
            "-----------------+----------+-------------+----------|\n",
            "Modal verbs      |    11    |    100%     |   100%   |\n",
            "-----------------+----------+-------------+----------|\n",
            "Contractions     |    16    |     94%     |   100%   |\n",
            "=================+==========+=============+==========+\n",
            "\n",
            "\n",
            "=================+==========+========================+\n",
            "Type: Formal     |  Count   |  Precision  |  Recall  |\n",
            "-----------------+----------+-------------+----------|\n",
            "Subject pronouns |    27    |    100%     |   100%   |\n",
            "-----------------+----------+-------------+----------|\n",
            "Modal verbs      |    12    |    100%     |   100%   |\n",
            "-----------------+----------+-------------+----------|\n",
            "Contractions     |    4     |     0%      |   100%   |\n",
            "=================+==========+=============+==========+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4: Compare categories in a text corpus\n",
        "2-Feb-2022\n",
        "\n",
        "Using the `Mini-CORE_new.zip` archive (Corpus of Online Registers of English), which contains 1,600 texts evenly separated between 8 categories, choose 3 linguistic features and compare their normalized occurence across the categories.\n",
        "\n",
        "These are the \"registers,\" or categories, in the corpus:\n",
        "* HI: How-to/Instructional\n",
        "* ID: Interactive Discussion\n",
        "* IN: Informational (Wikipedia)\n",
        "* IP: Informational Persuasion\n",
        "* LY: Song Lyrics\n",
        "* NA: News reports\n",
        "* OP: Opinion blogs\n",
        "* SP: Interview transcripts"
      ],
      "metadata": {
        "id": "j346nQDPONVq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assignment 04, LING 360, Text Analytics in Python\n",
        "# Updated on: 2-Feb-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# =======================================\n",
        "# Compare categories in an English corpus\n",
        "# =======================================\n",
        "'''\n",
        "Purpose: compare three linguistic features across 8 categories of\n",
        "text in an English corpus with 1,600 documents.\n",
        "\n",
        "Output: a .csv file with normalized counts for each linguistic\n",
        "feature across the 8 categories.\n",
        "\n",
        "Also includes accuracy information (precision and recall) for a\n",
        "single document to test the performance of the RegEx patterns used.\n",
        "\n",
        "Author: Ryan Parker\n",
        "Updated on: 2-Feb-2022\n",
        "\n",
        "Note: the code in this module assumes the `Mini-CORE_new.zip` file\n",
        "is saved in the current working directory.\n",
        "\n",
        "Linguistic features compared:\n",
        "1. Subject pronouns\n",
        "2. Contractions\n",
        "3. Proper nouns\n",
        "4. Average word length (in characters)\n",
        "5. Average sentence length (in words)\n",
        "'''\n",
        "# re module for RegEx searches, os module for working with files, \n",
        "# zipfile for extracting .zip file, csv for writing results to a\n",
        "# .csv file, and statistics module for computing averages\n",
        "import re, os, zipfile, csv, statistics\n",
        "\n",
        "# ==============================================\n",
        "# Locate and unzip the Mini-CORE_new.zip archive\n",
        "# ==============================================\n",
        "cur_dir = os.getcwd()\n",
        "zip_path = cur_dir + '/Mini-CORE_new.zip'\n",
        "\n",
        "# Unzip the file\n",
        "# See: https://stackoverflow.com/questions/31346790/unzip-all-zipped-files-in-a-folder-to-that-same-folder-using-python-2-7-5/56850153#56850153\n",
        "zipfile.ZipFile(zip_path).extractall()\n",
        "\n",
        "# =============================================\n",
        "# Loop through all files in the unzipped folder\n",
        "# =============================================\n",
        "# Create a dictionary to store the counts\n",
        "# Keys are the category (register) names, and values are\n",
        "# a sub-dictionary with keys for each linguistic feature\n",
        "# and values showing the count of that feature.\n",
        "data_dict = {\n",
        "    'HI': {'sub_pronouns': [], 'contractions': [], 'proper_nouns': [], 'word_count': [], 'sent_count': [], 'avg_word_length': [], 'avg_sent_length': []},\n",
        "    'ID': {'sub_pronouns': [], 'contractions': [], 'proper_nouns': [], 'word_count': [], 'sent_count': [], 'avg_word_length': [], 'avg_sent_length': []},\n",
        "    'IN': {'sub_pronouns': [], 'contractions': [], 'proper_nouns': [], 'word_count': [], 'sent_count': [], 'avg_word_length': [], 'avg_sent_length': []},\n",
        "    'IP': {'sub_pronouns': [], 'contractions': [], 'proper_nouns': [], 'word_count': [], 'sent_count': [], 'avg_word_length': [], 'avg_sent_length': []},\n",
        "    'LY': {'sub_pronouns': [], 'contractions': [], 'proper_nouns': [], 'word_count': [], 'sent_count': [], 'avg_word_length': [], 'avg_sent_length': []},\n",
        "    'NA': {'sub_pronouns': [], 'contractions': [], 'proper_nouns': [], 'word_count': [], 'sent_count': [], 'avg_word_length': [], 'avg_sent_length': []},\n",
        "    'OP': {'sub_pronouns': [], 'contractions': [], 'proper_nouns': [], 'word_count': [], 'sent_count': [], 'avg_word_length': [], 'avg_sent_length': []},\n",
        "    'SP': {'sub_pronouns': [], 'contractions': [], 'proper_nouns': [], 'word_count': [], 'sent_count': [], 'avg_word_length': [], 'avg_sent_length': []}\n",
        "}\n",
        "\n",
        "# List subject pronouns\n",
        "# See: https://en.wikipedia.org/wiki/Subject_pronoun\n",
        "subject_pronouns = ['I', 'you', 'she', 'he', 'we', 'they', 'who']\n",
        "\n",
        "# For contractions, here's a RegEx pattern to find apostrophes between other letters\n",
        "# r\"\\w+'\\w+\"\n",
        "\n",
        "# RegEx pattern to find proper nouns: words that start with a capital\n",
        "# letter, followed by at least one lowercase letter, and not at the beginning\n",
        "# of a line or a sentence (that is, not preceded by \". \", \"? \", or \"! \").\n",
        "# Here, I use a \"verbose\" pattern with comments.\n",
        "# See: https://docs.python.org/3/library/re.html#re.X\n",
        "prop_nouns = r'''\n",
        "    (?<![.?!]\\s)    # negative look-behind assertion: don't match after the end of a sentence\n",
        "    (?<!\\A)         # \"\": don't match at the beginning of the string\n",
        "    (?<!\\n\\s)       # \"\": don't match directly after a newline\n",
        "    [A-Z]\\w+\\b      # match 1 capital letter followed by 1 or more alphanumeric characters and a word boundary'''\n",
        "# Demonstration: https://pythex.org/?regex=(%3F%3C!%5B.%3F!%5D%5Cs)(%3F%3C!%5CA)(%3F%3C!%5Cn)%5BA-Z%5D%5Cw%2B%5Cb&test_string=Hi!%20I%20am%20happy%20to%20see%20you.%20I%27m%20from%20Texas.%20My%20name%20is%20Zach.%20What%27s%20your%20name%3F%0A%0AMy%20name%20is%20John.%0A%0APleasure%20to%20meet%20you%2C%20John.%20Where%20are%20you%20from%3F%0A%0AGeorgia.&ignorecase=0&multiline=0&dotall=0&verbose=0\n",
        "\n",
        "\n",
        "corpus_dir = cur_dir + '/Mini-CORE_new'\n",
        "for one_file in os.listdir(corpus_dir):\n",
        "    if one_file.endswith('.txt'):\n",
        "        # Get the 2-letter category code from the filename\n",
        "        category = one_file[2:4]\n",
        "        # Open the file; note the encoding is not UTF-8, but cp437, \n",
        "        # as noted by Dr. Brown.\n",
        "        # See: https://docs.python.org/3/library/codecs.html#standard-encodings\n",
        "        with open(corpus_dir + '/' + one_file, mode='rt', encoding='cp437') as txt_file:\n",
        "            # Store the file's contents in a variable\n",
        "            all_text = txt_file.read()\n",
        "        \n",
        "        # Remove any text between angle brackets (<>), non-greedy\n",
        "        all_text = re.sub(pattern=r'<.*?>', repl='', string=all_text)\n",
        "        # Count words\n",
        "        all_words = [wrd for wrd in re.split(r'\\W+', string=all_text) if wrd != '']\n",
        "        data_dict[category]['word_count'].append(len(all_words))\n",
        "        # Count number of characters in the words\n",
        "        total_chars = sum([len(wrd) for wrd in all_words])\n",
        "        data_dict[category]['avg_word_length'].append(total_chars)\n",
        "        # Count sentences, which end in either: ., !, or ? (optionally: \") and a space\n",
        "        all_sents = [sent for sent in re.split(r'[.?!][\"]?\\s', string=all_text) if sent != '']\n",
        "        data_dict[category]['sent_count'].append(len(all_sents))\n",
        "        # Count subject pronouns\n",
        "        sp = sum([len(re.findall(pattern=r'\\b' + wrd + r'\\b', string=all_text, flags=re.I)) for wrd in subject_pronouns])\n",
        "        data_dict[category]['sub_pronouns'].append(sp)\n",
        "        # Count contractions\n",
        "        cont = len(re.findall(pattern=r\"\\w+'\\w+\", string=all_text))\n",
        "        data_dict[category]['contractions'].append(cont)\n",
        "        # Count proper nouns\n",
        "        pn = len(re.findall(pattern=prop_nouns, string=all_text, flags=re.VERBOSE))\n",
        "        data_dict[category]['proper_nouns'].append(pn)\n",
        "\n",
        "# ==================================\n",
        "# Calculate averages and proportions\n",
        "# ==================================\n",
        "# In this section, I overwrite values in the data_dict,\n",
        "# replacing them with category-level averages\n",
        "\n",
        "for catg in data_dict:\n",
        "    # Calculate the average word length (characters per word)\n",
        "    # using the total character count stored in the \n",
        "    # 'avg_word_length' list and the total word count\n",
        "    # stored in the 'word_count' list\n",
        "    chars = sum(data_dict[catg]['avg_word_length'])\n",
        "    wrds = sum(data_dict[catg]['word_count'])\n",
        "    data_dict[catg]['avg_word_length'] = chars / wrds\n",
        "\n",
        "    # Calculate the average sentence length (words per sentence)\n",
        "    sents = sum(data_dict[catg]['sent_count'])\n",
        "    data_dict[catg]['avg_sent_length'] = wrds / sents\n",
        "\n",
        "    # Calculate subject pronouns per word\n",
        "    sp = sum(data_dict[catg]['sub_pronouns'])\n",
        "    data_dict[catg]['sub_pronouns'] = sp / wrds\n",
        "\n",
        "    # Calculate contractions per word\n",
        "    cont = sum(data_dict[catg]['contractions'])\n",
        "    data_dict[catg]['contractions'] = cont / wrds\n",
        "\n",
        "    # Calculate proper nouns per word\n",
        "    pn = sum(data_dict[catg]['proper_nouns'])\n",
        "    data_dict[catg]['proper_nouns'] = pn / wrds\n",
        "\n",
        "    # Overwrite total words and total sentences\n",
        "    data_dict[catg]['word_count'] = wrds\n",
        "    data_dict[catg]['sent_count'] = sents\n",
        "\n",
        "# ================================\n",
        "# Save normalized values as a .csv\n",
        "# ================================\n",
        "header_row = ['Register'] + [key for key in data_dict['HI']]\n",
        "normalized_factor = 1000\n",
        "with open('Parker_04.csv', mode='wt') as csv_file:\n",
        "    csv_writer = csv.writer(csv_file)\n",
        "    csv_writer.writerow(header_row)\n",
        "    for catg in data_dict:\n",
        "        # Normalize values (rates of occurrence per 1000 words)\n",
        "        for col in ['sub_pronouns', 'contractions', 'proper_nouns']:\n",
        "            data_dict[catg][col] = data_dict[catg][col] * normalized_factor\n",
        "        # Create row for csv file\n",
        "        one_row = [catg] + [val for val in data_dict[catg].values()]\n",
        "        # Alternate version: single-step list comprehension, eliminates the normalizing step\n",
        "        # one_row = [catg] + [val*normalized_factor if key in ['sub_pronouns', 'contractions', 'proper_nouns'] else val for key, val in data_dict[catg].items()]\n",
        "        csv_writer.writerow(one_row)"
      ],
      "metadata": {
        "id": "PCszkVX4gZe3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate accuracy\n",
        "Return the precision and recall scores for a given text document.\n",
        "\n",
        "The main problem is when proper nouns occur at the beginning of a sentence. Right now, the RegEx pattern does not count capitalized words at the beginning of a sentence or at the beginning of a line."
      ],
      "metadata": {
        "id": "8kr4jWjutdm1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os, re\n",
        "cur_dir = os.getcwd()\n",
        "file_name = '1+HI+HT+HI-HI-HI-HI+FH-HT-HT-HT+NNNN+0142743.txt'\n",
        "\n",
        "# Lists of true values\n",
        "# Subject pronouns\n",
        "sp_truth = ['I', 'I', 'I', 'I', 'you', 'you', 'you', 'you', 'You', 'you',\n",
        "            'you', 'you', 'you', 'you', 'you', 'you', 'you', 'you', 'you',\n",
        "            'you', 'you', 'you', 'You', 'you', 'you', 'you']\n",
        "\n",
        "# Contractions\n",
        "cont_truth = [\"What's\", \"you'll\"]\n",
        "\n",
        "# Proper nouns\n",
        "pn_truth = ['ANZ', 'Internet', 'Banking', 'ANZ', 'Internet',\n",
        "            'Banking', 'ANZ', 'ANZ', 'Internet', 'Banking', 'Customer',\n",
        "            'Registration', 'Number', 'CRN', 'Telecode', 'ANZ', 'Phone',\n",
        "            'Banking', 'ANZ', 'Phone', 'Banking', 'CRN', 'Telecode',\n",
        "            'ANZ', 'Internet', 'Banking', 'CRN', 'ANZ',\n",
        "            'Customer', 'Service', 'Consultant', 'ANZ',\n",
        "            'Internet', 'Banking', 'ANZ', 'Internet', 'Banking', 'ANZ',\n",
        "            'Internet', 'Banking', 'CRN', 'Telecode', 'CRN', 'Telecode',\n",
        "            'CRN', 'Telecode', 'ANZ', 'Internet', 'Banking',\n",
        "            'ANZ', 'Phone', 'Banking', 'CRN', 'Telecode', 'ANZ',\n",
        "            'Internet', 'Banking', 'CRN', 'CRN', 'ANZ', 'Internet',\n",
        "            'Banking', 'CRN', 'CRN', 'ANZ', 'Customer',\n",
        "            'Service', 'Consultant', 'ANZ', 'Card', 'ANZ', 'Internet',\n",
        "            'Banking', 'ANZ', 'Internet', 'Banking', 'Pay',\n",
        "            'Anyone', 'Security', 'Device', 'ANZ', 'Internet', 'Banking',\n",
        "            'Pay', 'Anyone', 'Security', 'Device', 'Operators', 'ANZ',\n",
        "            'Internet', 'Banking', 'Business', 'ANZ', 'Internet',\n",
        "            'Banking', 'ANZ', 'Internet', 'Banking', 'CRN', 'ANZ',\n",
        "            'Internet', 'Banking', 'ANZ', 'Internet', 'Banking', 'Help',\n",
        "            'FAQs', 'ANZ', 'Internet', 'Banking', 'Log', 'ANZ',\n",
        "            'Internet', 'Banking', 'Business', 'Administrators', 'Authorisers',\n",
        "            'ANZ', 'Multiple', 'Payments', 'ANZ', 'Internet',\n",
        "            'Banking', 'ANZ', 'Multiple', 'Payments', 'Electronic',\n",
        "            'Transaction', 'Business', 'Mortgage', 'Loan', 'Offset',\n",
        "            'Electronic', 'Transactions', 'ANZ', 'Business', 'Classic', 'ANZ',\n",
        "            'Business', 'Extra', 'ANZ', 'Revolving', 'Agri', 'Line', 'ANZ',\n",
        "            'Negotiator', 'ANZ', 'Security', 'Device', 'Code', 'Security',\n",
        "            'Device']\n",
        "\n",
        "# List of subject pronouns\n",
        "# See: https://en.wikipedia.org/wiki/Subject_pronoun\n",
        "subject_pronouns = ['I', 'you', 'she', 'he', 'we', 'they', 'who']\n",
        "prop_nouns = r'''\n",
        "    (?<![.?!]\\s)    # negative look-behind assertion: don't match after the end of a sentence\n",
        "    (?<!\\A)         # \"\": don't match at the beginning of the string\n",
        "    (?<!\\n\\s)       # \"\": don't match directly after a newline\n",
        "    [A-Z]\\w+\\b      # match 1 capital letter followed by 1 or more lowercase letters and a word boundary'''\n",
        "\n",
        "with open(cur_dir + '/Mini-CORE_new/' + file_name, mode='rt', encoding='cp437') as txt_file:\n",
        "    # Store the file's contents in a variable\n",
        "    all_text = txt_file.read()\n",
        "\n",
        "# Remove any text between angle brackets (<>), non-greedy\n",
        "all_text = re.sub(pattern=r'<.*?>', repl='', string=all_text)\n",
        "\n",
        "# Count words\n",
        "all_words = [wrd for wrd in re.split(r'\\W+', string=all_text) if wrd != '']\n",
        "print('Word count:', len(all_words))\n",
        "\n",
        "# Count sentences, which end in either: ., !, or ? (optionally: \") and a space\n",
        "all_sents = [sent for sent in re.split(r'[.?!][\"]?\\s', string=all_text) if sent != '']\n",
        "print('Sentence count:', len(all_sents))\n",
        "\n",
        "# List subject pronouns\n",
        "sp = []\n",
        "for wrd in subject_pronouns:\n",
        "    sp.extend(re.findall(pattern=r'\\b' + wrd + r'\\b', string=all_text, flags=re.I))\n",
        "acc = accuracy(true_list=sp_truth, prediction_list=sp)\n",
        "print('\\nSubject pronouns, count:', len(sp), '\\nList:', sp, \n",
        "      '\\nPrecision:', acc['precision'], '\\nRecall:', acc['recall'])\n",
        "\n",
        "# List contractions\n",
        "cont = re.findall(pattern=r\"\\w+'\\w+\", string=all_text)\n",
        "acc = accuracy(true_list=cont_truth, prediction_list=cont)\n",
        "print('\\nContractions, count:', len(cont), '\\nList:', cont, \n",
        "      '\\nPrecision:', acc['precision'], '\\nRecall:', acc['recall'])\n",
        "\n",
        "# list proper nouns\n",
        "pn = re.findall(pattern=prop_nouns, string=all_text, flags=re.VERBOSE)\n",
        "acc = accuracy(true_list=pn_truth, prediction_list=pn)\n",
        "print('\\nProper nouns, count:', len(pn), '\\nList:', pn, \n",
        "      '\\nPrecision:', acc['precision'], '\\nRecall:', acc['recall'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9Qmpr8Xtk-H",
        "outputId": "8a22c4f1-c10a-4424-cd80-90adc472a599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word count: 690\n",
            "Sentence count: 35\n",
            "\n",
            "Subject pronouns, count: 26 \n",
            "List: ['I', 'I', 'I', 'I', 'you', 'you', 'you', 'you', 'You', 'you', 'you', 'you', 'you', 'you', 'you', 'you', 'you', 'you', 'you', 'you', 'you', 'you', 'You', 'you', 'you', 'you'] \n",
            "Precision: 1.0 \n",
            "Recall: 1.0\n",
            "\n",
            "Contractions, count: 3 \n",
            "List: [\"What's\", \"you'll\", \"customer's\"] \n",
            "Precision: 0.6666666666666666 \n",
            "Recall: 1.0\n",
            "\n",
            "Proper nouns, count: 157 \n",
            "List: ['ANZ', 'Internet', 'Banking', 'NZ', 'Internet', 'Banking', 'ANZ', 'ANZ', 'Internet', 'Banking', 'Customer', 'Registration', 'Number', 'CRN', 'Telecode', 'NZ', 'Phone', 'Banking', 'ANZ', 'Phone', 'Banking', 'CRN', 'Telecode', 'ANZ', 'Internet', 'Banking', 'CRN', 'ANZ', 'Customer', 'Service', 'Consultant', 'ANZ', 'Internet', 'Banking', 'ANZ', 'Internet', 'Banking', 'ANZ', 'Internet', 'Banking', 'CRN', 'Telecode', 'CRN', 'Telecode', 'CRN', 'Telecode', 'ANZ', 'Internet', 'Banking', 'International', 'ANZ', 'Phone', 'Banking', 'CRN', 'Telecode', 'ANZ', 'Internet', 'Banking', 'CRN', 'CRN', 'ANZ', 'Internet', 'Banking', 'International', 'CRN', 'CRN', 'ANZ', 'Customer', 'Service', 'Consultant', 'ANZ', 'Card', 'NZ', 'Internet', 'Banking', 'Internet', 'ANZ', 'Internet', 'Banking', 'Pay', 'Anyone', 'Security', 'Device', 'ANZ', 'Internet', 'Banking', 'Pay', 'Anyone', 'Security', 'Device', 'Operators', 'ANZ', 'Internet', 'Banking', 'Business', 'ANZ', 'Internet', 'Banking', 'ANZ', 'Internet', 'Banking', 'CRN', 'ANZ', 'Internet', 'Banking', 'ANZ', 'Internet', 'Banking', 'Help', 'FAQs', 'ANZ', 'Internet', 'Banking', 'Log', 'Fee', 'ANZ', 'Internet', 'Banking', 'Business', 'Administrators', 'Authorisers', 'Electronic', 'ANZ', 'Multiple', 'Payments', 'ANZ', 'Internet', 'Banking', 'ANZ', 'Multiple', 'Payments', 'Transaction', 'Business', 'Mortgage', 'Loan', 'Offset', 'Electronic', 'Transactions', 'ANZ', 'Business', 'Classic', 'ANZ', 'Business', 'Extra', 'ANZ', 'Revolving', 'Agri', 'Line', 'ANZ', 'Negotiator', 'Fee', 'ANZ', 'Security', 'Device', 'Code', 'Security', 'Device'] \n",
            "Precision: 0.9490445859872612 \n",
            "Recall: 0.9802631578947368\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5: Word frequency distribution across categories in a corpus\n",
        "19-Feb-2022\n",
        "\n",
        "Using the `Mini-CORE_new.zip` archive (Corpus of Online Registers of English), which contains 1,600 texts evenly separated between 8 categories, create .csv files with word-frequency distributions from each category, excluding stopwords (words with little lexical meaning).\n",
        "\n",
        "These are the 8 \"registers,\" or categories, in the corpus:\n",
        "1. HI: How-to/Instructional\n",
        "2. ID: Interactive Discussion\n",
        "3. IN: Informational (Wikipedia)\n",
        "4. IP: Informational Persuasion\n",
        "5. LY: Song Lyrics\n",
        "6. NA: News reports\n",
        "7. OP: Opinion blogs\n",
        "8. SP: Interview transcripts"
      ],
      "metadata": {
        "id": "5-N_bLaobGKU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Practice cells for getting familiar with NLTK"
      ],
      "metadata": {
        "id": "4je37yBGqAVd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "some_text = \"Early in the morning, John and Peter ran to the seplulchre.\"\n",
        "sample_tokens = nltk.word_tokenize(some_text)\n",
        "print(sample_tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jJtkHcle0nJ",
        "outputId": "1ebcd0f8-d119-423c-a385-a52671aa2ec6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Early', 'in', 'the', 'morning', ',', 'John', 'and', 'Peter', 'ran', 'to', 'the', 'seplulchre', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords', quiet=True)\n",
        "len(nltk.corpus.stopwords.words('english'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGAC69ISgCGj",
        "outputId": "0da44f0b-5221-4f13-dab6-da6dcd65c135"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "179"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Program to turn in"
      ],
      "metadata": {
        "id": "g_mnvVxKqCq9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assignment 05, LING 360, Text Analytics in Python\n",
        "# Updated on: 19-Feb-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# =======================================\n",
        "# Compare categories in an English corpus\n",
        "# =======================================\n",
        "'''\n",
        "Author: Ryan Parker\n",
        "Updated on: 19-Feb-2022\n",
        "\n",
        "Purpose: compare word frequency distributions across\n",
        "8 categories of text in an English corpus with 1,600 documents.\n",
        "\n",
        "Output: 8 .csv files with word frequency counts (listed in\n",
        "descending order by counts) across the 8 categories, or registers.\n",
        "\n",
        "Note: the code in this module assumes the `Mini-CORE_new.zip` file\n",
        "is saved in the current working directory.\n",
        "\n",
        "Categories, or registers, in the corpus:\n",
        "1. HI: How-to/Instructional\n",
        "2. ID: Interactive Discussion\n",
        "3. IN: Informational (Wikipedia)\n",
        "4. IP: Informational Persuasion\n",
        "5. LY: Song Lyrics\n",
        "6. NA: News reports\n",
        "7. OP: Opinion blogs\n",
        "8. SP: Interview transcripts\n",
        "'''\n",
        "# re module for RegEx searches, os module for working with files, \n",
        "# zipfile for extracting .zip file, csv for writing results to\n",
        "# .csv files, and nltk for stopwords list and tokenizing\n",
        "import re, os, zipfile, csv, nltk\n",
        "\n",
        "# Download the dependency required for word tokenizing.\n",
        "# quiet=True tells NLTK to not print any outputs\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "# Download the stopwords list from NLTK\n",
        "nltk.download('stopwords', quiet=True)\n",
        "\n",
        "# Store stopwords in a list\n",
        "stopwords_list = nltk.corpus.stopwords.words('english')\n",
        "\n",
        "\n",
        "# ==============================================\n",
        "# Locate and unzip the Mini-CORE_new.zip archive\n",
        "# ==============================================\n",
        "cur_dir = os.getcwd()\n",
        "zip_path = cur_dir + '/Mini-CORE_new.zip'\n",
        "\n",
        "# Unzip the file\n",
        "# See: https://stackoverflow.com/questions/31346790/unzip-all-zipped-files-in-a-folder-to-that-same-folder-using-python-2-7-5/56850153#56850153\n",
        "zipfile.ZipFile(zip_path).extractall()\n",
        "\n",
        "# =============================================\n",
        "# Loop through all files in the unzipped folder\n",
        "# =============================================\n",
        "# Create a dictionary to store the counts\n",
        "# Keys are the category (register) names, and values are\n",
        "# a sub-dictionary with keys for each unique word and\n",
        "# values representing how many times each word occurred.\n",
        "data_dict = {\n",
        "    'HI': {},\n",
        "    'ID': {},\n",
        "    'IN': {},\n",
        "    'IP': {},\n",
        "    'LY': {},\n",
        "    'NA': {},\n",
        "    'OP': {},\n",
        "    'SP': {}\n",
        "}\n",
        "\n",
        "corpus_dir = cur_dir + '/Mini-CORE_new'\n",
        "for one_file in os.listdir(corpus_dir):\n",
        "    if one_file.endswith('.txt'):\n",
        "        # Get the 2-letter category code from the filename\n",
        "        category = one_file[2:4]\n",
        "        # Open the file; note the encoding is not UTF-8, but cp437, \n",
        "        # as noted by Dr. Brown.\n",
        "        # See: https://docs.python.org/3/library/codecs.html#standard-encodings\n",
        "        with open(corpus_dir + '/' + one_file, mode='rt', encoding='cp437') as txt_file:\n",
        "            # Store the file's contents in a variable\n",
        "            all_text = txt_file.read()\n",
        "        \n",
        "        # Remove any text between angle brackets (<>), non-greedy.\n",
        "        # This strips the heading lines and removes HTML formatting tags.\n",
        "        all_text = re.sub(pattern=r'<.*?>', repl='', string=all_text)\n",
        "\n",
        "        # Split text into words, using any non-alphanumeric character\n",
        "        # as the delimeter (i.e., [^A-Za-z0-9_])\n",
        "        all_words = [wrd.lower() for wrd in re.split(r'\\W+', string=all_text) if wrd != '']\n",
        "\n",
        "        # Alternative version (preserves punction, which isn't desirable\n",
        "        # for this task)\n",
        "        # Tokenize text (split into words and punctuation)\n",
        "        # all_words = nltk.word_tokenize(all_text)\n",
        "\n",
        "        # Update the word frequency distribution\n",
        "        for wrd in all_words:\n",
        "            if wrd not in stopwords_list:\n",
        "                if wrd in data_dict[category]:\n",
        "                    # increment the word's count by 1\n",
        "                    data_dict[category][wrd] += 1\n",
        "                else:\n",
        "                    # create a new key with count of 1\n",
        "                    data_dict[category][wrd] = 1\n",
        "\n",
        "print('Finished creating word frequency counts. Saving to .csv files...')\n",
        "\n",
        "# =================================================\n",
        "# Save sorted frequency distributions as .csv files\n",
        "# =================================================\n",
        "\n",
        "# Track top 20 words across each category for quick comparison\n",
        "top_words = {\n",
        "    'HI': [],\n",
        "    'ID': [],\n",
        "    'IN': [],\n",
        "    'IP': [],\n",
        "    'LY': [],\n",
        "    'NA': [],\n",
        "    'OP': [],\n",
        "    'SP': []\n",
        "}\n",
        "\n",
        "for catg in data_dict:\n",
        "    # Sort the dictionary by word count.\n",
        "    # See: Jake VanderPlas's A Whirldwind Tour of Python, Ch. 08, \"Defining Functions: Anonymous lambda Functions\"\n",
        "    # https://jakevdp.github.io/WhirlwindTourOfPython/08-defining-functions.html#Anonymous-(lambda)-Functions\n",
        "    # Returns a list of tuples in the form [('word1', count), ('word2', count)]\n",
        "    sorted_distribution = sorted(data_dict[catg].items(), key=lambda item: item[1], reverse=True)\n",
        "\n",
        "    # Add to top-20 words list\n",
        "    for wrd in sorted_distribution[:20]:\n",
        "        top_words[catg].append(wrd)\n",
        "\n",
        "    # Iterate through the list and write to .csv file\n",
        "    with open('word_frequencies_' + catg + '.csv', mode='wt') as csv_file:\n",
        "        csv_writer = csv.writer(csv_file)\n",
        "        csv_writer.writerow(['Word', 'Count'])\n",
        "        for word_count in sorted_distribution:\n",
        "            csv_writer.writerow(list(word_count))\n",
        "\n",
        "# Create a .csv file for the top-20 word comparison\n",
        "with open('word_freq_comp.csv', mode='wt') as csv_file:\n",
        "    csv_writer = csv.writer(csv_file)\n",
        "    # Write header row\n",
        "    csv_writer.writerow(top_words.keys())\n",
        "    # Write data rows\n",
        "    for row_num in range(20):\n",
        "        csv_writer.writerow([top_words[catg][row_num] for catg in top_words.keys()])\n",
        "\n",
        "# ================================================\n",
        "# Create a single .zip file with all 8 .csv files\n",
        "# ================================================\n",
        "# See: https://www.kite.com/python/examples/3416/zipfile-compress-files-into-a-zip-file\n",
        "# Also: https://docs.python.org/3/library/zipfile.html#zipfile-objects\n",
        "\n",
        "# Uses the \"with ... as ___\" context manager to handle opening and closing the .zip archive\n",
        "with zipfile.ZipFile('word_freq.zip', mode='w') as zf:\n",
        "    for catg in data_dict:\n",
        "        filename = 'word_frequencies_' + catg + '.csv'\n",
        "        zf.write(filename)\n",
        "    # Add the comparison csv file\n",
        "    zf.write('word_freq_comp.csv')\n",
        "\n",
        "print('Complete! .csv files saved to word_freq.zip in the current directory')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YiVwVuE2bGKV",
        "outputId": "a68b89ea-c38c-4bd6-953a-92e842e8cd42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished creating word frequency counts. Saving to .csv files...\n",
            "Complete! .csv files saved to word_freq.zip in the current directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7: Compare categories based on bigram frequency\n",
        "5-Mar-2022\n",
        "\n",
        "Using the `AWE_untagd.zip` archive, create .csv files with noun-bigram frequency distributions from each category, focusing on common nouns only (no proper nouns).\n",
        "\n",
        "These are the 6 \"registers,\" or categories, in the corpus:\n",
        "1. JA_BI\n",
        "2. JA_HI\n",
        "3. PS_BI\n",
        "4. PS_HI\n",
        "5. TB_BI\n",
        "6. TB_HI\n",
        "\n",
        "\n",
        "## Resources\n",
        "* NLTK book, [Chapter 5, Categorizing and Tagging Words](https://www.nltk.org/book/ch05.html)"
      ],
      "metadata": {
        "id": "558Ve5zfnpt1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## NLTK practice: part-of-speech tagging"
      ],
      "metadata": {
        "id": "j5bY3siTsMeU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "some_text = \"Early in the morning, John and Peter ran to the seplulchre.\"\n",
        "sample_tokens = nltk.word_tokenize(some_text)\n",
        "print(sample_tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74132f8f-656a-48b9-b2e4-eb49b2e32445",
        "id": "eHReCh0lnpt2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Early', 'in', 'the', 'morning', ',', 'John', 'and', 'Peter', 'ran', 'to', 'the', 'seplulchre', '.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apply part-of-speech tagging to each word token in the input."
      ],
      "metadata": {
        "id": "_lMTT8mrsHsA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Required for NLTK's default part-of-speech tagger\n",
        "nltk.download('averaged_perceptron_tagger', quiet=True)\n",
        "nltk.download('universal_tagset', quiet=True)\n",
        "\n",
        "# nltk.pos_tag(sample_tokens, tagset='universal')\n",
        "nltk.pos_tag(sample_tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjJw8KT1poCJ",
        "outputId": "6677f1a2-76a9-42f2-d24f-837633334924"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Early', 'RB'),\n",
              " ('in', 'IN'),\n",
              " ('the', 'DT'),\n",
              " ('morning', 'NN'),\n",
              " (',', ','),\n",
              " ('John', 'NNP'),\n",
              " ('and', 'CC'),\n",
              " ('Peter', 'NNP'),\n",
              " ('ran', 'VBD'),\n",
              " ('to', 'TO'),\n",
              " ('the', 'DT'),\n",
              " ('seplulchre', 'NN'),\n",
              " ('.', '.')]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Explanation of tag names. Use `nltk.help.upenn_tagsets()` (no input parameters) to return a list of _all_ tags and their meanings plus examples."
      ],
      "metadata": {
        "id": "qBHI_hezr6lt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('tagsets', quiet=True)\n",
        "nltk.help.upenn_tagset('NN*')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wz3fFU5am-2v",
        "outputId": "194bc563-1ced-49dc-909c-49f70a895f3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NN: noun, common, singular or mass\n",
            "    common-carrier cabbage knuckle-duster Casino afghan shed thermostat\n",
            "    investment slide humour falloff slick wind hyena override subhumanity\n",
            "    machinist ...\n",
            "NNP: noun, proper, singular\n",
            "    Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos\n",
            "    Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA\n",
            "    Shannon A.K.C. Meltex Liverpool ...\n",
            "NNPS: noun, proper, plural\n",
            "    Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists\n",
            "    Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques\n",
            "    Apache Apaches Apocrypha ...\n",
            "NNS: noun, common, plural\n",
            "    undergraduates scotches bric-a-brac products bodyguards facets coasts\n",
            "    divestitures storehouses designs clubs fragrances averages\n",
            "    subjectivists apprehensions muses factory-jobs ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sentence tokenizing. Splits the text by _characters_, rather than by words, which isn't as helpful when assigning part-of-speech tags.\n",
        "\n",
        "⚠ **Lesson learned:** stick with word tokenizing and `nltk.pos_tag(word_tokens)`."
      ],
      "metadata": {
        "id": "RKadSylxrAxn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Good Timber by Douglas Malloch\n",
        "# Source: https://www.familyfriendpoems.com/poem/good-timber-by-douglas-malloch\n",
        "\n",
        "sample_sentences = '''\n",
        "The tree that never had to fight \n",
        "For sun and sky and air and light, \n",
        "But stood out in the open plain \n",
        "And always got its share of rain, \n",
        "Never became a forest king \n",
        "But lived and died a scrubby thing. \n",
        "\n",
        "The man who never had to toil \n",
        "To gain and farm his patch of soil, \n",
        "Who never had to win his share \n",
        "Of sun and sky and light and air, \n",
        "Never became a manly man \n",
        "But lived and died as he began. \n",
        "\n",
        "Good timber does not grow with ease, \n",
        "The stronger wind, the stronger trees, \n",
        "The further sky, the greater length, \n",
        "The more the storm, the more the strength. \n",
        "By sun and cold, by rain and snow, \n",
        "In trees and men good timbers grow. \n",
        "\n",
        "Where thickest lies the forest growth \n",
        "We find the patriarchs of both. \n",
        "And they hold counsel with the stars \n",
        "Whose broken branches show the scars \n",
        "Of many winds and much of strife. \n",
        "This is the common law of life.\n",
        "'''\n",
        "\n",
        "sent_tokens = nltk.sent_tokenize(sample_sentences)\n",
        "print(f'There are {len(sent_tokens)} sentences.')\n",
        "sent_tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XypYGNF_oM53",
        "outputId": "0e0ac2d3-f7b4-44b2-eb42-431536bb399a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 7 sentences.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['\\nThe tree that never had to fight \\nFor sun and sky and air and light, \\nBut stood out in the open plain \\nAnd always got its share of rain, \\nNever became a forest king \\nBut lived and died a scrubby thing.',\n",
              " 'The man who never had to toil \\nTo gain and farm his patch of soil, \\nWho never had to win his share \\nOf sun and sky and light and air, \\nNever became a manly man \\nBut lived and died as he began.',\n",
              " 'Good timber does not grow with ease, \\nThe stronger wind, the stronger trees, \\nThe further sky, the greater length, \\nThe more the storm, the more the strength.',\n",
              " 'By sun and cold, by rain and snow, \\nIn trees and men good timbers grow.',\n",
              " 'Where thickest lies the forest growth \\nWe find the patriarchs of both.',\n",
              " 'And they hold counsel with the stars \\nWhose broken branches show the scars \\nOf many winds and much of strife.',\n",
              " 'This is the common law of life.']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.pos_tag_sents(sent_tokens)\n",
        "\n",
        "# Returns a list of part-of-speech tags by CHARACTER (not by word).\n",
        "# Seems to be a strange error related to nltk.pos_tag_sents()"
      ],
      "metadata": {
        "id": "kekjkYNTpEEg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program for assignment"
      ],
      "metadata": {
        "id": "-KxQtJ1enpt3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assignment 07, LING 360, Text Analytics in Python\n",
        "# Updated on: 5-Mar-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# =================================================================\n",
        "# Compare categories in an Enlgish corpus based on bigram frequency\n",
        "# =================================================================\n",
        "'''\n",
        "Author: Ryan Parker (https://github.com/rparkr)\n",
        "Updated on: 5-Mar-2022\n",
        "\n",
        "Purpose: compare noun-bigram frequency distributions across\n",
        "6 categories of text in an English corpus.\n",
        "\n",
        "Output: 6 .csv files with noun-bigram word frequency counts (listed in\n",
        "descending order by counts) across the 6 categories, or registers.\n",
        "\n",
        "Note: the code in this module assumes the `AWE_untagd.zip` file\n",
        "is saved in the current working directory.\n",
        "\n",
        "Categories, or registers, in the corpus:\n",
        "1. JA_BI\n",
        "2. JA_HI\n",
        "3. PS_BI\n",
        "4. PS_HI\n",
        "5. TB_BI\n",
        "6. TB_HI\n",
        "'''\n",
        "# re module for RegEx searches; os module for working with files;\n",
        "# zipfile for extracting .zip files; csv for writing results to\n",
        "# .csv files; nltk for tokenizing, part-of-speech tagging,\n",
        "# and creating frequency distribution of bigrams;\n",
        "# charset_normalizer for determining file encoding.\n",
        "import re, os, zipfile, csv, nltk, charset_normalizer\n",
        "\n",
        "# Download the dependency required for word tokenizing.\n",
        "# quiet=True tells NLTK not to print any outputs\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "# Required for NLTK's default part-of-speech tagger\n",
        "nltk.download('averaged_perceptron_tagger', quiet=True)\n",
        "\n",
        "\n",
        "# ==============================================\n",
        "# Locate and unzip the AWE_untagd.zip archive\n",
        "# ==============================================\n",
        "# cur_dir = os.getcwd()\n",
        "# zip_path = cur_dir + '/AWE_untagd.zip'\n",
        "zip_path = './AWE_untagd.zip'\n",
        "\n",
        "# Unzip the file\n",
        "# See: https://stackoverflow.com/questions/31346790/unzip-all-zipped-files-in-a-folder-to-that-same-folder-using-python-2-7-5/56850153#56850153\n",
        "zipfile.ZipFile(zip_path).extractall()\n",
        "corpus_dir = './AWE_untagd'\n",
        "\n",
        "# =============================================\n",
        "# Loop through all files in the unzipped folder\n",
        "# =============================================\n",
        "# Create a dictionary to store the counts\n",
        "# Keys are the category (register) names, and values are\n",
        "# a sub-dictionary with keys for each unique bigram and\n",
        "# values representing how many times each bigram occurred.\n",
        "\n",
        "data_dict = {\n",
        "    'JA_BI': {},\n",
        "    'JA_HI': {},\n",
        "    'PS_BI': {},\n",
        "    'PS_HI': {},\n",
        "    'TB_BI': {},\n",
        "    'TB_HI': {}\n",
        "}\n",
        "\n",
        "common_noun_tags = ['NN', 'NNS']\n",
        "\n",
        "for one_file in os.listdir(corpus_dir):\n",
        "    if one_file.endswith('.txt'):\n",
        "        # Get the 5-character category code from the filename\n",
        "        category = one_file[:5]\n",
        "        # Determine file encoding using charset_normalizer.\n",
        "        # This adds about 25 seconds to file processing time (6x normal time)\n",
        "        # but does well at figuring out encoding to use when reading files.\n",
        "        # See: https://pypi.org/project/charset-normalizer/\n",
        "        # Also: https://charset-normalizer.readthedocs.io/en/latest/user/getstarted.html#basic-usage\n",
        "        #   list of encodings Python supports: https://docs.python.org/3/library/codecs.html#standard-encodings\n",
        "        enc = charset_normalizer.from_path(corpus_dir + '/' + one_file).best()\n",
        "        all_text = str(enc)\n",
        "        \n",
        "        # # Alternate version, faster because it assumes just one encoding\n",
        "        # # enc = 'cp1250'  # also try: 'cp775'\n",
        "        # # Open the file\n",
        "        # with open(corpus_dir + '/' + one_file, mode='rt', encoding=enc) as txt_file:\n",
        "        #     # Store the file's contents in a variable\n",
        "        #     all_text = txt_file.read()\n",
        "        \n",
        "        # Remove any text between angle brackets (<>), non-greedy.\n",
        "        # This strips the heading lines and removes HTML formatting tags.\n",
        "        all_text = re.sub(pattern=r'<.*?>', repl='', string=all_text)\n",
        "\n",
        "        # Tokenize text (split into words and punctuation)\n",
        "        all_words = nltk.word_tokenize(all_text)\n",
        "        \n",
        "        # Add part-of-speech tags\n",
        "        tagged_words = nltk.pos_tag(all_words)\n",
        "\n",
        "        # Loop through word-pos tuples and find noun-noun bigrams\n",
        "        # End at the second-to-last item (i.e., [:-1])\n",
        "        for i, wrd_tuple in enumerate(tagged_words[:-1]):\n",
        "            # Check if current word and the following word are common nouns\n",
        "            if (wrd_tuple[1] in common_noun_tags\n",
        "              and tagged_words[i+1][1] in common_noun_tags):\n",
        "                bigram = f'{wrd_tuple[0]} {tagged_words[i+1][0]}'\n",
        "                if bigram in data_dict[category]:\n",
        "                    # increment the bigram's count by 1\n",
        "                    data_dict[category][bigram] += 1\n",
        "                else:\n",
        "                    # create a new key with count of 1\n",
        "                    data_dict[category][bigram] = 1\n",
        "\n",
        "print('Finished creating word frequency counts. Saving to .csv files...')\n",
        "\n",
        "# =================================================\n",
        "# Save sorted frequency distributions as .csv files\n",
        "# =================================================\n",
        "\n",
        "# Track top 20 bigrams across each category for quick comparison\n",
        "top_bigrams = {\n",
        "    'JA_BI': [],\n",
        "    'JA_HI': [],\n",
        "    'PS_BI': [],\n",
        "    'PS_HI': [],\n",
        "    'TB_BI': [],\n",
        "    'TB_HI': []\n",
        "}\n",
        "\n",
        "for catg in data_dict:\n",
        "    # Sort the dictionary by bigram count.\n",
        "    # See: Jake VanderPlas's A Whirldwind Tour of Python, Ch. 08, \"Defining Functions: Anonymous lambda Functions\"\n",
        "    # https://jakevdp.github.io/WhirlwindTourOfPython/08-defining-functions.html#Anonymous-(lambda)-Functions\n",
        "    # Returns a list of tuples in the form [('bigram1', count), ('bigram2', count)]\n",
        "    sorted_distribution = sorted(data_dict[catg].items(), key=lambda item: item[1], reverse=True)\n",
        "\n",
        "    # Add to top-20 words list\n",
        "    for bigram in sorted_distribution[:20]:\n",
        "        top_bigrams[catg].append(bigram)\n",
        "\n",
        "    # Iterate through the list and write to .csv file\n",
        "    with open('bigram_frequencies_' + catg + '.csv', mode='wt') as csv_file:\n",
        "        csv_writer = csv.writer(csv_file)\n",
        "        csv_writer.writerow(['Bigram', 'Count'])\n",
        "        for word_count in sorted_distribution:\n",
        "            csv_writer.writerow(list(word_count))\n",
        "\n",
        "# Create a .csv file for the top-20 word comparison\n",
        "with open('bigram_freq_comp.csv', mode='wt') as csv_file:\n",
        "    csv_writer = csv.writer(csv_file)\n",
        "    # Write header row\n",
        "    csv_writer.writerow(top_bigrams.keys())\n",
        "    # Write data rows\n",
        "    for row_num in range(20):\n",
        "        csv_writer.writerow([top_bigrams[catg][row_num] for catg in top_bigrams.keys()])\n",
        "\n",
        "# ================================================\n",
        "# Create a single .zip file with all 8 .csv files\n",
        "# ================================================\n",
        "# See: https://www.kite.com/python/examples/3416/zipfile-compress-files-into-a-zip-file\n",
        "# Also: https://docs.python.org/3/library/zipfile.html#zipfile-objects\n",
        "\n",
        "# Uses the \"with ... as ___\" context manager to handle opening and closing the .zip archive\n",
        "with zipfile.ZipFile('bigram_freq.zip', mode='w') as zf:\n",
        "    for catg in data_dict:\n",
        "        filename = 'bigram_frequencies_' + catg + '.csv'\n",
        "        zf.write(filename)\n",
        "    # Add the comparison csv file\n",
        "    zf.write('bigram_freq_comp.csv')\n",
        "\n",
        "print('Complete! .csv files saved to bigram_freq.zip in the current directory')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b63c43d-ce9c-4a76-caab-40bffcbcaa42",
        "id": "Z8og8j_8npt4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished creating word frequency counts. Saving to .csv files...\n",
            "Complete! .csv files saved to bigram_freq.zip in the current directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('./bigram_freq_comp.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qMKoDH1yIOh1",
        "outputId": "53ab80da-03fb-46bd-dfe0-8a58c578850c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-914023c6-3b5f-42c8-8d03-dc90f2085c45\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>JA_BI</th>\n",
              "      <th>JA_HI</th>\n",
              "      <th>PS_BI</th>\n",
              "      <th>PS_HI</th>\n",
              "      <th>TB_BI</th>\n",
              "      <th>TB_HI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>('mass loss', 6)</td>\n",
              "      <td>('world history', 14)</td>\n",
              "      <td>('fossil record', 3)</td>\n",
              "      <td>('status quo', 4)</td>\n",
              "      <td>('cell wall', 4)</td>\n",
              "      <td>('massacre ’', 6)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>('motor neuron', 6)</td>\n",
              "      <td>('time discipline', 5)</td>\n",
              "      <td>('cocoa beans', 3)</td>\n",
              "      <td>('virus infection', 3)</td>\n",
              "      <td>('body size', 4)</td>\n",
              "      <td>('’ s', 3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>('heart disease', 5)</td>\n",
              "      <td>('porcelain production', 3)</td>\n",
              "      <td>('growth dynamics', 3)</td>\n",
              "      <td>('home front', 3)</td>\n",
              "      <td>('water quality', 4)</td>\n",
              "      <td>('peace negotiations', 3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>('ejaculatory bulb', 4)</td>\n",
              "      <td>('population policy', 3)</td>\n",
              "      <td>('mate choice', 3)</td>\n",
              "      <td>('state governments', 3)</td>\n",
              "      <td>('cell body', 4)</td>\n",
              "      <td>('air force', 2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>('mdxCv mice', 4)</td>\n",
              "      <td>('woman poet', 2)</td>\n",
              "      <td>('male snakes', 2)</td>\n",
              "      <td>('historians today', 2)</td>\n",
              "      <td>('body sites', 4)</td>\n",
              "      <td>('defense budget', 2)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-914023c6-3b5f-42c8-8d03-dc90f2085c45')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-914023c6-3b5f-42c8-8d03-dc90f2085c45 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-914023c6-3b5f-42c8-8d03-dc90f2085c45');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                     JA_BI                        JA_HI  \\\n",
              "0         ('mass loss', 6)        ('world history', 14)   \n",
              "1      ('motor neuron', 6)       ('time discipline', 5)   \n",
              "2     ('heart disease', 5)  ('porcelain production', 3)   \n",
              "3  ('ejaculatory bulb', 4)     ('population policy', 3)   \n",
              "4        ('mdxCv mice', 4)            ('woman poet', 2)   \n",
              "\n",
              "                    PS_BI                     PS_HI                 TB_BI  \\\n",
              "0    ('fossil record', 3)         ('status quo', 4)      ('cell wall', 4)   \n",
              "1      ('cocoa beans', 3)    ('virus infection', 3)      ('body size', 4)   \n",
              "2  ('growth dynamics', 3)         ('home front', 3)  ('water quality', 4)   \n",
              "3      ('mate choice', 3)  ('state governments', 3)      ('cell body', 4)   \n",
              "4      ('male snakes', 2)   ('historians today', 2)     ('body sites', 4)   \n",
              "\n",
              "                       TB_HI  \n",
              "0          ('massacre ’', 6)  \n",
              "1                 ('’ s', 3)  \n",
              "2  ('peace negotiations', 3)  \n",
              "3           ('air force', 2)  \n",
              "4      ('defense budget', 2)  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Measure accuracy"
      ],
      "metadata": {
        "id": "zk8iI6Vm5mzs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Accuracy function\n",
        "Calculates precision and recall. Copied from the function in section 3, above.\n",
        "\n",
        "The input to this function is a list of bigrams separated by a space, like this `['asteroid belt', 'space time']`."
      ],
      "metadata": {
        "id": "EVFJ06Yw55Yn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ===============================================\n",
        "# Calculate accuracy scores, precision and recall\n",
        "# ===============================================\n",
        "def accuracy(true_list, prediction_list):\n",
        "    '''\n",
        "    This function returns a dictionary with\n",
        "    scores for precision and recall based on\n",
        "    the inputs `true_list` and `prediction_list`.\n",
        "\n",
        "    Return format\n",
        "    ---\n",
        "    ```\n",
        "    {\n",
        "        'precision': precision_score (float),\n",
        "        'recall': recall_score (float)\n",
        "    }\n",
        "    ```\n",
        "\n",
        "    Parameters\n",
        "    ---\n",
        "    `true_list`: list of str\n",
        "        The list of all true positive strings.\n",
        "    `prediction_list`: list of str\n",
        "        The list of all predicted positive strings.\n",
        "\n",
        "    Example\n",
        "    ---\n",
        "    ```\n",
        "    >>> acc = accuracy(true_list=['A', 'A', 'b', 'c'], prediction_list=['A', 'a', 'b', 'b', 'C'])\n",
        "    >>> acc['precision']\n",
        "    ... 0.4\n",
        "    >>> acc['recall']\n",
        "    ... 0.5\n",
        "    \n",
        "    # Or, to see the calculation\n",
        "    correct_predictions = ['A', 'b']\n",
        "    precision = len(correct_predictions) / len(prediction_list) = 2/5 = 0.4\n",
        "    recall = len(correct_predictions) / len(true_list) = 2/4 = 0.5\n",
        "    ```\n",
        "\n",
        "    Calculation\n",
        "    ---\n",
        "    precision = (num_correct_predictions / total_predictions)\n",
        "    recall = (num_correct_predictions / num_true_values)\n",
        "    '''\n",
        "    # First, create word frequency dictionaries\n",
        "    true_dict = {}\n",
        "    for word in true_list:\n",
        "        word = word.lower()\n",
        "        if word in true_dict:\n",
        "            true_dict[word] += 1\n",
        "        else:\n",
        "            true_dict[word] = 1\n",
        "\n",
        "    predictions_dict = {}\n",
        "    for word in prediction_list:\n",
        "        word = word.lower()\n",
        "        if word in predictions_dict:\n",
        "            predictions_dict[word] += 1\n",
        "        else:\n",
        "            predictions_dict[word] = 1\n",
        "\n",
        "    # Next, count the times a word was correctly identified.\n",
        "    # The correct predictions for a word is the minimum of the true\n",
        "    # word count and the predicted word count (e.g., if true count = 2\n",
        "    # and predicted count = 3, the correct number of predictions was 2).\n",
        "    correct_predictions = 0.0\n",
        "    for word in true_dict:\n",
        "        if word in predictions_dict:\n",
        "            correct_predictions += min(true_dict[word], predictions_dict[word])\n",
        "\n",
        "    # Compute accuracy\n",
        "    # =================\n",
        "    # precision (correct_predictions ÷ total_predictions)\n",
        "    acc_precision = correct_predictions / float(len(prediction_list))\n",
        "\n",
        "    # recall (correct_predictions ÷ true_list)\n",
        "    if len(true_list) == 0:\n",
        "        acc_recall = 1\n",
        "        print('There were no values in true_list, so precision is 0% and recall is 100%')\n",
        "    else:\n",
        "        acc_recall = correct_predictions / float(len(true_list))\n",
        "\n",
        "    return {'precision': acc_precision, 'recall': acc_recall}\n"
      ],
      "metadata": {
        "id": "mv0LQMS75mWG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function: automatic accuracy verification\n"
      ],
      "metadata": {
        "id": "yFLT0l769eXN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to clear displayed output\n",
        "# See: https://stackoverflow.com/a/70084498/17005348\n",
        "\n",
        "import os           # clearing displayed output\n",
        "import platform     # determine operating system type\n",
        "import sys          # check installed Python modules\n",
        "\n",
        "def clear_output():\n",
        "    '''\n",
        "    Checks operating system (Windows vs. Unix-like)\n",
        "    and clears any output already displayed (printed).\n",
        "    '''\n",
        "    # Check if in IPython notebook\n",
        "    # See: https://stackoverflow.com/a/37661854/17005348\n",
        "    if 'ipykernel' in sys.modules:\n",
        "        import IPython.display\n",
        "        IPython.display.clear_output(wait=False)\n",
        "    else:\n",
        "        if platform.system() == 'Windows':\n",
        "            os.system('cls')\n",
        "        else:\n",
        "            os.system('clear')"
      ],
      "metadata": {
        "id": "v4mk9tflf_8x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk                 # compute part-of-speech tags\n",
        "import charset_normalizer   # determine file encoding\n",
        "\n",
        "# Download the dependency required for word tokenizing.\n",
        "# quiet=True tells NLTK not to print any outputs\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "# Required for NLTK's default part-of-speech tagger\n",
        "nltk.download('averaged_perceptron_tagger', quiet=True)\n",
        "\n",
        "def verify_acc(file_path, nouns_only=True):\n",
        "    '''\n",
        "    This function returns a dictionary with\n",
        "    scores for precision and recall based on\n",
        "    a given file in the file_path parameter.\n",
        "\n",
        "    Return format\n",
        "    ---\n",
        "    ```\n",
        "    {\n",
        "        'precision': precision_score (float),\n",
        "        'recall': recall_score (float)\n",
        "    }\n",
        "    ```\n",
        "\n",
        "    Parameters\n",
        "    ---\n",
        "    `file_path`: str\n",
        "        Absolute or relative file path to the file\n",
        "        whose accuracy user will verify.\n",
        "    `nouns_only`: bool, default=True\n",
        "        If True, user must verify only noun-noun bigrams\n",
        "        and confirm whether the bigrams are both common nouns.\n",
        "        If False, user must verify all bigrams in the text file\n",
        "        which equals the number of word tokens in the file minus 1.\n",
        "    '''\n",
        "\n",
        "    # List of predicted common-noun bigrams\n",
        "    prediction_list = []\n",
        "\n",
        "    # Read the file using charset_normalizer to determine file's encoding.\n",
        "    # See: https://pypi.org/project/charset-normalizer/\n",
        "    # Also: https://charset-normalizer.readthedocs.io/en/latest/user/getstarted.html#basic-usage\n",
        "    #   list of encodings Python supports: https://docs.python.org/3/library/codecs.html#standard-encodings\n",
        "    all_text = str(charset_normalizer.from_path(file_path).best())\n",
        "\n",
        "    # Remove any text between angle brackets (<>), non-greedy.\n",
        "    # This strips the heading lines and removes HTML formatting tags.\n",
        "    all_text = re.sub(pattern=r'<.*?>', repl='', string=all_text)\n",
        "\n",
        "    # Tokenize text (split into words and punctuation)\n",
        "    all_words = nltk.word_tokenize(all_text)\n",
        "    \n",
        "    # Add part-of-speech tags\n",
        "    tagged_words = nltk.pos_tag(all_words)\n",
        "\n",
        "    common_noun_tags = ['NN', 'NNS']\n",
        "\n",
        "    # Loop through word-pos tuples and find noun-noun bigrams\n",
        "    # End at the second-to-last item (i.e., [:-1])\n",
        "    for i, wrd_tuple in enumerate(tagged_words[:-1]):\n",
        "        # Check if current word and the following word are common nouns\n",
        "        if (wrd_tuple[1] in common_noun_tags\n",
        "            and tagged_words[i+1][1] in common_noun_tags):\n",
        "            bigram = f'{wrd_tuple[0]} {tagged_words[i+1][0]}'\n",
        "            # Add to predicted list\n",
        "            prediction_list.append(bigram)\n",
        "\n",
        "    # =========================\n",
        "    # Ask user for verification\n",
        "    # =========================\n",
        "    true_list = []\n",
        "\n",
        "    # print(f'Please answer the following {len(tagged_words[:-1])} questions.\\n')\n",
        "\n",
        "    # Note that the input re-writes over the previous line each time\n",
        "    # by ending each input string with '\\r'.\n",
        "    # See: https://www.kite.com/python/answers/how-to-print-over-the-same-line-in-python\n",
        "\n",
        "    noun_tags = ['NN', 'NNS', 'NNP', 'NNPS']\n",
        "\n",
        "    for i, wrd_tuple in enumerate(tagged_words[:-1]):\n",
        "        if (nouns_only\n",
        "            and (wrd_tuple[1] not in noun_tags or tagged_words[i+1][1] not in noun_tags)):\n",
        "                continue\n",
        "        else:\n",
        "            check = ('✅' if wrd_tuple[1] in noun_tags and tagged_words[i+1][1] in noun_tags else '')\n",
        "            bigram = f'{wrd_tuple[0]} {tagged_words[i+1][0]}'\n",
        "            input_text = ('\\nAre the following words both common nouns?'\n",
        "                        + '\\n\\nIf yes, press \"y\" then [Enter].'\n",
        "                        + '\\nIf no, just press [Enter].'\n",
        "                        + f'\\n\\n{i+1}/{len(tagged_words[:-1])}'\n",
        "                        + f'\\n\\n{bigram}'\n",
        "                        + f'\\n{wrd_tuple[1]}   {tagged_words[i+1][1]}  {check}'\n",
        "                        + f'\\n{wrd_tuple}'\n",
        "                        + f'\\n{tagged_words[i+1]}'\n",
        "                        + '\\n\\n')\n",
        "            # clear_output()\n",
        "            user_answer = input(input_text)\n",
        "            if user_answer == 'y':\n",
        "                true_list.append(bigram)\n",
        "    \n",
        "    # ================\n",
        "    # Compute accuracy\n",
        "    # ================\n",
        "\n",
        "    acc = accuracy(true_list, prediction_list)\n",
        "\n",
        "    return acc"
      ],
      "metadata": {
        "id": "x78agMJz9eBr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Run function to verify accuracy"
      ],
      "metadata": {
        "id": "Wkornj4EYukG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "verify_acc(file_path = './AWE_untagd/TB_BI_24.txt', nouns_only=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PBTBauqV_tO",
        "outputId": "ca780a2b-cdd1-4fe5-fa1a-f61842a5c5f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "2/621\n",
            "\n",
            "Probability distributions\n",
            "NNP   NNS  ✅\n",
            "('Probability', 'NNP')\n",
            "('distributions', 'NNS')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "7/621\n",
            "\n",
            "world everything\n",
            "NN   NN  ✅\n",
            "('world', 'NN')\n",
            "('everything', 'NN')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "14/621\n",
            "\n",
            "speciation rates\n",
            "NN   NNS  ✅\n",
            "('speciation', 'NN')\n",
            "('rates', 'NNS')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "30/621\n",
            "\n",
            "time t\n",
            "NN   NN  ✅\n",
            "('time', 'NN')\n",
            "('t', 'NN')\n",
            "\n",
            "\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "40/621\n",
            "\n",
            "time t\n",
            "NN   NN  ✅\n",
            "('time', 'NN')\n",
            "('t', 'NN')\n",
            "\n",
            "\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "195/621\n",
            "\n",
            "time period\n",
            "NN   NN  ✅\n",
            "('time', 'NN')\n",
            "('period', 'NN')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "297/621\n",
            "\n",
            "wet season\n",
            "NN   NN  ✅\n",
            "('wet', 'NN')\n",
            "('season', 'NN')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "383/621\n",
            "\n",
            "rain today\n",
            "NN   NN  ✅\n",
            "('rain', 'NN')\n",
            "('today', 'NN')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "389/621\n",
            "\n",
            "rain yesterday\n",
            "NN   NN  ✅\n",
            "('rain', 'NN')\n",
            "('yesterday', 'NN')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "530/621\n",
            "\n",
            "days increases\n",
            "NNS   NNS  ✅\n",
            "('days', 'NNS')\n",
            "('increases', 'NNS')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "550/621\n",
            "\n",
            "reason statisticians\n",
            "NN   NNS  ✅\n",
            "('reason', 'NN')\n",
            "('statisticians', 'NNS')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "554/621\n",
            "\n",
            "shorthand algebra\n",
            "NN   NN  ✅\n",
            "('shorthand', 'NN')\n",
            "('algebra', 'NN')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "559/621\n",
            "\n",
            "probability distributions\n",
            "NN   NNS  ✅\n",
            "('probability', 'NN')\n",
            "('distributions', 'NNS')\n",
            "\n",
            "y\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "588/621\n",
            "\n",
            "+ q\n",
            "NNP   NN  ✅\n",
            "('+', 'NNP')\n",
            "('q', 'NN')\n",
            "\n",
            "\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "589/621\n",
            "\n",
            "q =\n",
            "NN   NNP  ✅\n",
            "('q', 'NN')\n",
            "('=', 'NNP')\n",
            "\n",
            "\n",
            "Are the following words both common nouns?\n",
            "\n",
            "If yes, press \"y\" then [Enter].\n",
            "If no, just press [Enter].\n",
            "\n",
            "619/621\n",
            "\n",
            "+ q\n",
            "NNP   NN  ✅\n",
            "('+', 'NNP')\n",
            "('q', 'NN')\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'precision': 0.8333333333333334, 'recall': 0.9090909090909091}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8: Tweet sentiment\n",
        "9-Mar-2022\n",
        "\n",
        "## Purpose\n",
        "Use Twitter's developer API to collect tweets from a company using that company's main hashtag.\n",
        "\n",
        "Search those tweets for a keyword of one of that company's products, then perform sentiment analysis (e.g., using nltk.sentiment.vader) on the tweet groups and return the mean compound sentiment score for tweets mentioning the company and for tweets mentioning its product.\n",
        "\n",
        "In addition, return the five most frequent words in positive-sentiment tweets about the product.\n",
        "\n",
        "\n",
        "## Resources\n",
        "* [Twitter API sandbox](https://developer.twitter.com/apitools/api?endpoint=%2F2%2Ftweets%2Fsearch%2Frecent&method=get) (for creating the search query)\n",
        "* [Search recent tweets](https://developer.twitter.com/en/docs/twitter-api/tweets/search/api-reference/get-tweets-search-recent): GET /2/tweets/search/recent, Twitter API v2 docs. \"Recent\" means \"within past 7 days.\"\n",
        "* [Guide to building a search query for tweets](https://developer.twitter.com/en/docs/twitter-api/tweets/search/integrate/build-a-query), Twitter API docs"
      ],
      "metadata": {
        "id": "zWxRw7wDIvKK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assignment 08, LING 360, Text Analytics in Python\n",
        "# Updated on: 9-Mar-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# =======================================\n",
        "# Perform sentiment analysis on tweets\n",
        "# =======================================\n",
        "'''\n",
        "Author: Ryan Parker\n",
        "Updated on: 9-Mar-2022\n",
        "\n",
        "Purpose: Use Twitter's developer API to collect tweets from a company using \n",
        "that company's main hashtag.\n",
        "\n",
        "Search those tweets for a keyword of one of that company's products, then \n",
        "perform sentiment analysis (e.g., using nltk.sentiment.vader) on the tweet \n",
        "groups and return the mean compound sentiment score for tweets mentioning \n",
        "the company and for tweets mentioning its product.\n",
        "\n",
        "In addition, return the five most frequent words in positive-sentiment \n",
        "tweets about the product.\n",
        "\n",
        "References:\n",
        "* Twitter API docs for searching recent tweets: https://developer.twitter.com/en/docs/twitter-api/tweets/search/api-reference/get-tweets-search-recent\n",
        "* Twitter API sandbox: https://developer.twitter.com/apitools/api?endpoint=%2F2%2Ftweets%2Fsearch%2Frecent&method=get\n",
        "* Twitter API guide to building a query: https://developer.twitter.com/en/docs/twitter-api/tweets/search/integrate/build-a-query\n",
        "'''\n",
        "\n",
        "# ======================================\n",
        "# Authorization\n",
        "# Replace with your unique bearer token\n",
        "BEARER_TOKEN = 'AAAAAAAAAAAAAAAAAAAAAL%2FWTwEAAAAA5EfmdextDteTJf2FWCnBXHXo3Go%3DOAyXMOALY0ld83caRupbjNb4ZazOkLxuTyzFryj9xTBALsA4Ur'\n",
        "# ======================================\n",
        "hashtag = '#microsoft'\n",
        "product_keyword = 'windows'\n",
        "# List of additional stopwords to filter out of top-5 words list\n",
        "other_stopwords = ['#microsoft', '#windows11', '#windows', '11', \n",
        "                       'windows', 'microsoft']\n",
        "\n",
        "# nltk for stopwords list and sentiment analysis\n",
        "import nltk\n",
        "# json for storing JSON strings and parsing them as Python data structures\n",
        "import json\n",
        "# requests for accessing the Twitter API\n",
        "import requests\n",
        "\n",
        "# ==============\n",
        "# NLTK downloads\n",
        "# ==============\n",
        "# Download the dependency required for sentence tokenizing.\n",
        "# quiet=True tells NLTK to not print any outputs\n",
        "nltk.download('punkt', quiet=True)\n",
        "\n",
        "# Download the lexicon (vocabulary) and trained sentiment model (vader)\n",
        "nltk.download('vader_lexicon', quiet=True)\n",
        "\n",
        "# Download the stopwords list from NLTK\n",
        "nltk.download('stopwords', quiet=True)\n",
        "\n",
        "# =================================\n",
        "# Collect tweets from Twitter's API\n",
        "# =================================\n",
        "base_url = 'https://api.twitter.com/2/tweets/search/recent?query='\n",
        "# max_results ranges from 1-100. tweet.fields has many tweet attributes avaiable\n",
        "parameters = '&max_results=100&tweet.fields=id,text'\n",
        "# Query is case-insensitive. See query building guide referenced in docstring.\n",
        "# Encode string to valid url using requests.utils.quote()\n",
        "# See: https://stackoverflow.com/a/46783596/17005348\n",
        "# Also: https://www.adamsmith.haus/python/answers/how-to-encode-a-url-in-python\n",
        "query = requests.utils.quote(f'{hashtag} has:hashtags lang:en')\n",
        "\n",
        "headers = {'Authorization': f'Bearer {BEARER_TOKEN}'}\n",
        "\n",
        "next_token = ''\n",
        "\n",
        "# Create a list to store all returned tweets (as JSON data)\n",
        "json_tweets = []\n",
        "\n",
        "# Collect tweets (1,000 total, 100 per batch)\n",
        "for tweet_batch in range(10):\n",
        "    if next_token != '':\n",
        "        url = base_url + query + parameters + f'&next_token={next_token}'\n",
        "    else:\n",
        "        url = base_url + query + parameters\n",
        "    response = requests.get(url, headers=headers)\n",
        "    # Store Python data structure (dictionary) of JSON text\n",
        "    json_data = response.json()\n",
        "    json_tweets.append(json_data)\n",
        "    try:\n",
        "        next_token = json_data['meta']['next_token']\n",
        "    except KeyError:\n",
        "        # Exit loop, there are no more pages of tweets to collect\n",
        "        break\n",
        "\n",
        "# Create a dictionary to consolidate all 1,000 tweets\n",
        "all_tweets = {'data': []}\n",
        "# Create a dictionary of the product-level tweets\n",
        "product_tweets = {'data': []}\n",
        "\n",
        "# Consolidate all tweets from the batches\n",
        "for tweet_batch in json_tweets:\n",
        "    for item in tweet_batch['data']:\n",
        "        all_tweets['data'].append(item)\n",
        "        # Consolidate product-specific tweets\n",
        "        if product_keyword in item['text'].lower():\n",
        "            product_tweets['data'].append(item)\n",
        "\n",
        "print(f\"Total number of tweets collected: {len(all_tweets['data']):,d}\")\n",
        "print(f\"Total number of tweets about '{product_keyword}': {len(product_tweets['data']):,d}\")\n",
        "\n",
        "# Save JSON file of tweets\n",
        "with open('all_tweets.json', mode='wt') as f:\n",
        "    # use json.dumpstring to create a JSON string\n",
        "    # representation of the Python dictionary\n",
        "    f.write(json.dumps(all_tweets))\n",
        "\n",
        "with open('product_tweets.json', mode='wt') as f:\n",
        "    f.write(json.dumps(product_tweets))\n",
        "\n",
        "# ====================================\n",
        "# Perform sentiment analysis on Tweets\n",
        "# ====================================\n",
        "sia = nltk.sentiment.SentimentIntensityAnalyzer()\n",
        "company_mean_sentiment = []\n",
        "product_mean_sentiment = []\n",
        "\n",
        "for tweet in all_tweets['data']:\n",
        "    # Calculate sentiment\n",
        "    compound_sentiment = sia.polarity_scores(tweet['text'])['compound']\n",
        "    company_mean_sentiment.append(compound_sentiment)\n",
        "    # Add sentiment data to tweet\n",
        "    tweet['sentiment'] = compound_sentiment\n",
        "\n",
        "# Repeat the sentiment analysis, but only on the product tweets\n",
        "for tweet in product_tweets['data']:\n",
        "    # Calculate sentiment\n",
        "    compound_sentiment = sia.polarity_scores(tweet['text'])['compound']\n",
        "    product_mean_sentiment.append(compound_sentiment)\n",
        "    # Add sentiment data to tweet\n",
        "    tweet['sentiment'] = compound_sentiment\n",
        "\n",
        "# Save JSON file of tweets with sentiment scores\n",
        "with open('all_tweets_plus_sentiment.json', mode='wt') as f:\n",
        "    f.write(json.dumps(all_tweets))\n",
        "\n",
        "with open('product_tweets_plus_sentiment.json', mode='wt') as f:\n",
        "    f.write(json.dumps(product_tweets))\n",
        "\n",
        "# Print mean sentiment scores\n",
        "company_mean_sentiment = sum(company_mean_sentiment) / len(company_mean_sentiment)\n",
        "product_mean_sentiment = sum(product_mean_sentiment) / len(product_mean_sentiment)\n",
        "comparison = ('higher' if product_mean_sentiment > company_mean_sentiment else 'lower')\n",
        "if company_mean_sentiment != product_mean_sentiment:\n",
        "    print(f'\\nThe average sentiment score for the product was {product_mean_sentiment:.1%},'\n",
        "          + f'\\nwhich is {comparison} than the average sentiment score for the company ({company_mean_sentiment:.1%}).')\n",
        "else:\n",
        "    print(f'The average sentiment scores for the company and the product were equal: {company_mean_sentiment:.1%}')\n",
        "\n",
        "# ===================\n",
        "# Find frequent words\n",
        "# ===================\n",
        "freq_words = {}\n",
        "positive_words = []\n",
        "# Store stopwords in a list\n",
        "stopwords_list = nltk.corpus.stopwords.words('english')\n",
        "# Add keywords to the stopwords list\n",
        "stopwords_list.extend(other_stopwords)\n",
        "\n",
        "for tweet in product_tweets['data']:\n",
        "    # Positive tweets only\n",
        "    if tweet['sentiment'] > 0:\n",
        "        positive_words.extend(tweet['text'].split())\n",
        "\n",
        "for word in positive_words:\n",
        "    wrd = word.lower()\n",
        "    if wrd not in stopwords_list:\n",
        "        if wrd in freq_words:\n",
        "            freq_words[wrd] += 1\n",
        "        else:\n",
        "            freq_words[wrd] = 1\n",
        "\n",
        "# Sort the dictionary by word count.\n",
        "# See: Jake VanderPlas's A Whirldwind Tour of Python, Ch. 08, \"Defining Functions: Anonymous lambda Functions\"\n",
        "# https://jakevdp.github.io/WhirlwindTourOfPython/08-defining-functions.html#Anonymous-(lambda)-Functions\n",
        "# Returns a list of tuples in the form [('word1', count), ('word2', count)]\n",
        "sorted_distribution = sorted(freq_words.items(), key=lambda item: item[1], reverse=True)\n",
        "\n",
        "print('\\nThe five most frequent words used in positive tweets about the product were:')\n",
        "print(*[wrd[0] for wrd in sorted_distribution[:5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "814oum5rIx_M",
        "outputId": "9e24fa24-0afb-4580-e38f-7ebfb4aba62b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of tweets collected: 1,000\n",
            "Total number of tweets about 'windows': 102\n",
            "\n",
            "The average sentiment score for the product was 8.5%,\n",
            "which is lower than the average sentiment score for the company (17.0%).\n",
            "\n",
            "The five most frequent words used in positive tweets about the product were:\n",
            "rt new #search security server\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 9: Web crawling\n",
        "16-Mar-2022\n",
        "\n",
        "## Purpose\n",
        "From a given site, randomly select 10 internal links on that page and collect the text from those 10 linked pages. Save each page's text as a separate .txt file.\n",
        "\n",
        "Key idea: practice creating a mini corpus."
      ],
      "metadata": {
        "id": "WV6L5OB2FA6u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# packages for extracting text from an HTML document\n",
        "# trafilatura is more advanced\n",
        "%pip install justext        # https://pypi.org/project/jusText/\n",
        "%pip install trafilatura    # https://pypi.org/project/trafilatura/"
      ],
      "metadata": {
        "id": "ztu-0_q_hhtz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assignment 09, LING 360, Text Analytics in Python\n",
        "# Updated on: 16-Mar-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# ==============================================\n",
        "# Create a mini text corpus through web scraping\n",
        "# ==============================================\n",
        "'''\n",
        "Author: Ryan Parker\n",
        "Updated on: 16-Mar-2022\n",
        "\n",
        "Purpose: From a given site, randomly select 10 internal links on that page \n",
        "and collect the text from those 10 linked pages. Save each page's text \n",
        "as a separate .txt file.\n",
        "\n",
        "Note: for the project, I use CarAndDriver.com's automaker pages. \n",
        "CarAndDriver.com's robots.txt file allows web scraping, permitted I use a \n",
        "10ms delay between requests, which I comply with.\n",
        "'''\n",
        "\n",
        "import requests                 # access web pages\n",
        "from bs4 import BeautifulSoup   # parse HTML\n",
        "import re                       # RegEx pattern matching\n",
        "import time                     # pause 10ms between each webpage request\n",
        "import random                   # select random sample from links on page\n",
        "import zipfile                  # create .zip archive for all .txt files\n",
        "import justext                  # text extraction from HTML\n",
        "import trafilatura              # advanced text extraction from HTML\n",
        "\n",
        "\n",
        "def get_links(main_url):\n",
        "    '''\n",
        "    Uses requests and BeautifulSoup to extract links\n",
        "    from the page given in the parameter main_url.\n",
        "\n",
        "    Note: this function is designed to work with\n",
        "    automaker pages from CarAndDriver.com, such as\n",
        "    https://www.caranddriver.com/honda\n",
        "    This function wil not work with other pages.\n",
        "    '''\n",
        "    # Disclaimer: I borrowed lines 44 and 47 from code I wrote last semester.\n",
        "    # For best speed, use the 'lxml' parser. The 'html.parser' is built-in with Python, but isn't as fast.\n",
        "    # See: https://www.crummy.com/software/BeautifulSoup/bs4/doc/#installing-a-parser\n",
        "    page = BeautifulSoup(requests.get(url=main_url).text, 'lxml')\n",
        "    # Get the parent page (site_url) from the provided main_url\n",
        "    last_slash = main_url.rfind('/')\n",
        "    site_url = main_url[:last_slash]\n",
        "    # List of all links to vehicle model pages\n",
        "    return [site_url + item.find('a').get('href') for item in page.find_all('div', {'class':'vehicle-item-body'})]\n",
        "\n",
        "\n",
        "def extract_text(url, method='trafilatura'):\n",
        "    '''\n",
        "    Takes a URL as input and returns just the text\n",
        "    from that webpage.\n",
        "\n",
        "    # Parameters\n",
        "    ---\n",
        "    `url` : str\n",
        "        The webpage whose text you which to extract\n",
        "    \n",
        "    `method` : {'trafilatura', 'justext'}, default='trafilatura'\n",
        "        The method to use for text extraction. The default,\n",
        "        'trafilatura', uses the trafilatura package to extract\n",
        "        text from a webpage. 'justext' uses the less-advanced\n",
        "        jusText package for text extraction.\n",
        "\n",
        "    # Return value\n",
        "    ---\n",
        "    A dictionary in the format:\n",
        "    ```\n",
        "    {\n",
        "        'success': {True, False},\n",
        "        'page_text': 'string containing text from page'\n",
        "        'page_title': 'title of the webpage'\n",
        "    }\n",
        "    ```\n",
        "    If the `method` is set to 'justext' and the function finds no\n",
        "    text to extract, the `success` key will return False and the\n",
        "    `page_text` key will return a message indicating that the text\n",
        "    extraction was unsuccessful. Otherwise, `success` will return True\n",
        "    and `page_text` will return a string with the text from the webpage.\n",
        "\n",
        "    # Dependencies\n",
        "    ---\n",
        "    This function uses the following packages:\n",
        "    * requests, for accessing web pages\n",
        "    * trafilatura, for advanced text extraction\n",
        "    * justext, for text extraction\n",
        "    '''\n",
        "    page_content = requests.get(url).text\n",
        "    # Get the page title, useful for naming each .txt file later\n",
        "    page_title = BeautifulSoup(page_content).title.text\n",
        "\n",
        "    if method == 'trafilatura':\n",
        "        # Use the trafilatura text extraction package\n",
        "        # See: https://trafilatura.readthedocs.io/en/latest/usage-python.html\n",
        "        page_text = trafilatura.extract(trafilatura.fetch_url(url))\n",
        "        success = True\n",
        "    else:\n",
        "        # Use the jusText text extraction package\n",
        "        # See: https://pypi.org/project/jusText/\n",
        "        paragraphs = justext.justext(page_content, justext.get_stoplist('English'))\n",
        "        num_paragraphs = sum([1 for p in paragraphs if not p.is_boilerplate])\n",
        "        paragraph_count = 0\n",
        "        page_text = ''\n",
        "        for paragraph in paragraphs:\n",
        "            if not paragraph.is_boilerplate:\n",
        "                paragraph_count += 1\n",
        "                if paragraph_count < num_paragraphs:\n",
        "                    page_text += paragraph.text + '\\n'\n",
        "                else:\n",
        "                    # Final paragraph reached, don't append newline\n",
        "                    page_text += paragraph.text\n",
        "        \n",
        "        if page_text == '':\n",
        "            # Only boilerplate code was found\n",
        "            success = False\n",
        "            page_text = ( f\"No text to extract from URL: {url}.\"\n",
        "                        + \"\\nThe jusText package found only boilerplate code on the page.\"\n",
        "                        + \"\\nTry setting the method to 'trafilatura' or try a different URL.\" )\n",
        "        else:\n",
        "            success = True\n",
        "    \n",
        "    return {'success': success, 'page_text': page_text, 'page_title': page_title}"
      ],
      "metadata": {
        "id": "DPnZsbE6FbuD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extract text from 10 pages"
      ],
      "metadata": {
        "id": "VesCFjq4wwxQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ============================\n",
        "# Crawl pages and extract text\n",
        "# ============================\n",
        "\n",
        "main_url = 'https://www.caranddriver.com/honda'\n",
        "\n",
        "# List of all links to vehicle models from the automaker's page\n",
        "links = get_links(main_url)\n",
        "\n",
        "# Counter to track the number pages whose text has been scraped\n",
        "pages_scraped = 0\n",
        "\n",
        "# Track names of .txt files saved\n",
        "filenames = []\n",
        "\n",
        "# Track which links have been visited\n",
        "visited_links = []\n",
        "\n",
        "if len(links) < 10:\n",
        "    # scrape all links on page\n",
        "    for link in links:\n",
        "        result = extract_text(link, method='trafilatura')\n",
        "        # Pause for 10ms to honor request in robots.txt\n",
        "        time.sleep(0.01)\n",
        "        if result['success']:\n",
        "            # Text extraction successful, save to a file\n",
        "            pages_scraped += 1\n",
        "            visited_links.append(link)\n",
        "            filename = f'{result[\"page_title\"]}.txt'\n",
        "            filenames.append(filename)\n",
        "            with open(filename, mode='wt') as txtfile:\n",
        "                txtfile.write(result['page_text'])\n",
        "else:\n",
        "    # More than 10 links on the page, so scrape only 10 of them\n",
        "    while pages_scraped < 10:\n",
        "        # Randomly select 10 links from the page\n",
        "        random_links = random.sample(links, k=10)\n",
        "        random_links = [l for l in random_links if l not in visited_links]\n",
        "        # Log the links that have already been visited\n",
        "        visited_links.extend(random_links)\n",
        "        for link in random_links:\n",
        "            result = extract_text(link, method='trafilatura')\n",
        "            # Pause for 10ms to honor request in robots.txt\n",
        "            time.sleep(0.01)\n",
        "            if result['success']:\n",
        "                # Text extraction successful, save to a file\n",
        "                pages_scraped += 1\n",
        "                filename = f'{result[\"page_title\"]}.txt'\n",
        "                filenames.append(filename)\n",
        "                with open(filename, mode='wt') as txtfile:\n",
        "                    txtfile.write(result['page_text'])\n",
        "\n",
        "\n",
        "# ================================================\n",
        "# Create a single .zip file with all 10 .txt files\n",
        "# ================================================\n",
        "# See: https://www.kite.com/python/examples/3416/zipfile-compress-files-into-a-zip-file\n",
        "# Also: https://docs.python.org/3/library/zipfile.html#zipfile-objects\n",
        "\n",
        "# Uses the \"with ... as ___\" context manager to handle opening and closing the .zip archive\n",
        "with zipfile.ZipFile('corpus.zip', mode='w') as zf:\n",
        "    for filename in filenames:\n",
        "        zf.write(filename)\n",
        "\n",
        "print('Successfully scraped the text from 10 pages.'\n",
        "    + f'\\nURLs scraped: {visited_links}')\n",
        "\n",
        "print('All .txt files saved to \"corpus.zip\"')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RA2F892pw1oj",
        "outputId": "7986e836-1a2c-406b-fcdb-672123fc3300"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully scraped the text from 10 pages.\n",
            "URLs scraped: ['https://www.caranddriver.com/honda/civic-type-r-2017', 'https://www.caranddriver.com/honda/odyssey-2020', 'https://www.caranddriver.com/honda/cr-v-2020', 'https://www.caranddriver.com/honda/clarity-2020', 'https://www.caranddriver.com/honda/accord-2020', 'https://www.caranddriver.com/honda/clarity-2018', 'https://www.caranddriver.com/honda/cr-v-2018', 'https://www.caranddriver.com/honda/ridgeline-2020', 'https://www.caranddriver.com/honda/civic-2020', 'https://www.caranddriver.com/honda/pilot-2018']\n",
            "All .txt files saved to \"corpus.zip\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 10: Topic modeling with LDA\n",
        "22-Mar-2022\n",
        "\n",
        "## Purpose\n",
        "Use the Latent Dirichlet Allocation (LDA) algorithm to find the topics in a corpus of documents from BYU's Daily Universe newspaper.\n",
        "\n",
        "\n",
        "## Resources\n",
        "* [Topic Modeling with Gensim and pyLDAvis](https://stackabuse.com/python-for-nlp-working-with-the-gensim-library-part-2/), StackAbuse article\n",
        "* [Topic Modeling](https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/), Machine Learning Plus\n",
        "* [Topic Modeling with sklearn](https://stackabuse.com/python-for-nlp-topic-modeling/), Stackabuse article"
      ],
      "metadata": {
        "id": "schXo5ii3fjv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# packages for topic modeling and visualization\n",
        "%pip install gensim     # LDA algorithm\n",
        "%pip install pyLDAvis   # topic visualization"
      ],
      "metadata": {
        "id": "OlPDXzbuqhSy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assignment 10, LING 360, Text Analytics in Python\n",
        "# Updated on: 22-Mar-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# ===========================\n",
        "# Discover topics through LDA\n",
        "# ===========================\n",
        "'''\n",
        "Author: Ryan Parker\n",
        "Updated on: 22-Mar-2022\n",
        "\n",
        "Purpose: Use the Latent Dirichlet Allocation (LDA) algorithm to find the \n",
        "topics in a corpus of documents from BYU's Daily Universe newspaper.\n",
        "'''\n",
        "\n",
        "import csv                          # create .csv files\n",
        "import matplotlib.pyplot as plt     # plotting library\n",
        "import re                           # RegEx pattern matching\n",
        "import zipfile                      # unzip starting file (news_universe.zip)\n",
        "import os                           # file management\n",
        "import nltk                         # text preprocessing (e.g., stopword removal)\n",
        "import gensim                       # LDA algorithm\n",
        "import pyLDAvis                     # LDA topics visualization\n",
        "from tqdm.notebook import tqdm      # progress bars\n",
        "from nltk.corpus import wordnet     # groups words by similarity (synonym sets)\n",
        "import collections                  # has a Counter object for efficient dict counting\n",
        "\n",
        "# Download the stopwords list from NLTK\n",
        "nltk.download('stopwords', quiet=True)\n",
        "\n",
        "# Download wordnet from NLTK\n",
        "nltk.download('wordnet', quiet=True)\n",
        "\n",
        "def unzip_given_file(filepath_to_zip_archive):\n",
        "    '''\n",
        "    Uses Python's zipfile module in the standard library\n",
        "    to unzip a .zip archive found at the\n",
        "    `filepath_to_zip_archive` path.\n",
        "\n",
        "    Returns `None`. Unzipped directory is added to\n",
        "    the directory where it was found\n",
        "    '''\n",
        "    # Unzip the file\n",
        "    # See: https://stackoverflow.com/questions/31346790/unzip-all-zipped-files-in-a-folder-to-that-same-folder-using-python-2-7-5/56850153#56850153\n",
        "    zipfile.ZipFile(filepath_to_zip_archive).extractall()\n",
        "\n",
        "\n",
        "def collect_corpus(filepath_to_directory):\n",
        "    '''\n",
        "    Loops through all .txt files in the provided\n",
        "    `filepath_to_directory` and creates a list of documents.\n",
        "\n",
        "    Returns a list of strings, each string is the full text\n",
        "    from a .txt document in the given directory.\n",
        "    '''\n",
        "    corpus = []\n",
        "    start_path = filepath_to_directory\n",
        "    for onefile in os.listdir(start_path):\n",
        "        # Verify that the file has a .txt extension\n",
        "        if os.path.splitext(onefile)[1] == '.txt':\n",
        "            with open(os.path.join(start_path, onefile), 'rt') as txtfile:\n",
        "                corpus.append(txtfile.read())\n",
        "    \n",
        "    return corpus\n",
        "\n",
        "\n",
        "def preprocess_text(corpus_list,\n",
        "                    stopwords_list,\n",
        "                    word_lemmatizer,\n",
        "                    min_char_len=5):\n",
        "    '''\n",
        "    Given a list of strings (`corpus_list`), where\n",
        "    each item in the list is a separate document, loop\n",
        "    through the list and preprocess the text. Steps applied:\n",
        "    1. Remove non-alphabetic characters (assumes text is written\n",
        "        in English)\n",
        "    2. Lowercase text\n",
        "    3. Tokenize the text\n",
        "    4. Remove any token that is in the `stopwords_list`\n",
        "    5. Remove any token that has fewer than `min_char_len` characters\n",
        "    6. Lemmatize each token using word_lemmatizer (an instance of\n",
        "        `nltk.stem.WordNetLemmatizer()`)\n",
        "    \n",
        "    Returns a list-of-lists: the first-level list represents the documents,\n",
        "    and the second-level list are the tokens within each document\n",
        "    '''\n",
        "\n",
        "    processed_list = []\n",
        "\n",
        "    for doc in corpus_list:\n",
        "        # Remove characters that are not letters or spaces [a-zA-z ]\n",
        "        doc = re.sub(r'[^a-zA-Z ]+', ' ', doc)\n",
        "\n",
        "        # Replace 2 or more spaces with single spaces\n",
        "        doc = re.sub(r'\\s{2,}?', ' ', doc)\n",
        "\n",
        "        # Convert to lowercase\n",
        "        doc = doc.lower()\n",
        "\n",
        "        # Tokenize text (split by spaces) and eliminate stopwords\n",
        "        # and words with fewer than min_char_len characters\n",
        "        tokens = [wrd for wrd in doc.split() \n",
        "                    if (wrd not in stopwords_list\n",
        "                    and len(wrd) >= min_char_len)]\n",
        "        \n",
        "        # Lemmatize words (condense to root)\n",
        "        tokens = [word_lemmatizer.lemmatize(wrd) for wrd in tokens]\n",
        "\n",
        "        # Add processed text to the list of documents\n",
        "        processed_list.append(tokens)\n",
        "    \n",
        "    return processed_list\n",
        "\n",
        "\n",
        "def optimal_num_topics(gensim_dict,\n",
        "                       gensim_corpus,\n",
        "                       cleaned_corpus,\n",
        "                       num_passes=20,\n",
        "                       min_topics=2,\n",
        "                       max_topics=20,\n",
        "                       step=1):\n",
        "    '''\n",
        "    Runs LDA models with varying numbers of topics to\n",
        "    attempt to find the optimal number of topics.\n",
        "\n",
        "    Parameters\n",
        "    ---\n",
        "    `gensim_dict`:\n",
        "        an instance of gensim.corpora.Dictionary()\n",
        "    `gensim_corpus`:\n",
        "        a list of bag-of-words representations of each document\n",
        "        in a corpus, created by the gensim_dict.doc2bow() function.\n",
        "    `cleaned_corpus`: list-of-lists\n",
        "        first-level list represents a document, and second-level list are\n",
        "        the tokens in that document, after being processed with the\n",
        "        `preprocess_text()` function. This list of documents-as-tokens\n",
        "        is needed for calculating the coherence score of a model.\n",
        "    `num_passes`: int, default=20\n",
        "        the number of passes that the LDA model will run\n",
        "    `min_topics`: int, default=2\n",
        "        the minimum number of topics to assess\n",
        "    `max_topics`: int, default=20\n",
        "        the maximum number of topics to assess\n",
        "    `step`: int, default=1\n",
        "        the stepsize between topic counts\n",
        "\n",
        "    Returns\n",
        "    ---\n",
        "    A dictionary in the form:\n",
        "    ```python\n",
        "    {\n",
        "        'lda_models': [lda_model1, lda_model2, etc.], \n",
        "        'perplexity_scores': [model1_score, model2_score, etc.],\n",
        "        'coherence_scores': [model1_score, model2_score, etc.]\n",
        "    }\n",
        "    ```\n",
        "\n",
        "    The optimal number of topics is where the perplexity score is lowest\n",
        "    and the coherence score is highest.\n",
        "    '''\n",
        "    # Store each model as an item in a list\n",
        "    models = []\n",
        "    # Store the number of topics used\n",
        "    topics = []\n",
        "    # Store the scores of the models as items in lists\n",
        "    perplexity_scores = []\n",
        "    coherence_scores = []\n",
        "\n",
        "    # Initialize progress bar\n",
        "    total_models_tested = ((max_topics - min_topics) / step) + 1\n",
        "    pbar = tqdm(total=total_models_tested)\n",
        "\n",
        "    for i in range(min_topics, max_topics + 1, step):\n",
        "        # Store the number of topics\n",
        "        topics.append(i)\n",
        "        # Create the LDA model\n",
        "        model = gensim.models.ldamodel.LdaModel(\n",
        "            gensim_corpus,\n",
        "            num_topics = i,\n",
        "            id2word = gensim_dict,\n",
        "            passes = num_passes)\n",
        "        # Add the LDA model to the list\n",
        "        models.append(model)\n",
        "        # Add the perplexity score of the LDA model to the list\n",
        "        perplexity_scores.append(model.log_perplexity(gensim_corpus))\n",
        "        # Add the coherence score of the LDA model to the list\n",
        "        coherence_scores.append(\n",
        "            gensim.models.CoherenceModel(\n",
        "                model = model,\n",
        "                texts = cleaned_corpus,\n",
        "                dictionary = gensim_dict,\n",
        "                coherence = 'c_v'\n",
        "            ).get_coherence()\n",
        "        )\n",
        "        pbar.set_description(f'Num topics: {i}, perplexity: {perplexity_scores[-1]:0.2f}, coherence: {coherence_scores[-1]:0.2f}')\n",
        "        pbar.update(n=1)\n",
        "    \n",
        "    model_dict = {\n",
        "        'models': models,\n",
        "        'num_topics': topics,\n",
        "        'perplexity_scores': perplexity_scores,\n",
        "        'coherence_scores': coherence_scores\n",
        "    }\n",
        "    \n",
        "    return model_dict\n",
        "\n",
        "def argmax(input_list):\n",
        "    '''\n",
        "    Returns the index (an `int`) of the item\n",
        "    with the max value in the given `input_list`.\n",
        "\n",
        "    If there are multiple items with the same value,\n",
        "    this function returns the first one in the list.\n",
        "    '''\n",
        "    max_val = max(input_list)\n",
        "    max_index = 0\n",
        "    for n, item in enumerate(input_list):\n",
        "        if item == max_val:\n",
        "            max_index = n\n",
        "            # Exit loop\n",
        "            break\n",
        "    \n",
        "    return max_index\n",
        "\n",
        "\n",
        "def argmin(input_list):\n",
        "    '''\n",
        "    Returns the index (an `int`) of the item\n",
        "    with the min value in the given `input_list`.\n",
        "\n",
        "    If there are multiple items with the same value,\n",
        "    this function returns the first one in the list.\n",
        "    '''\n",
        "    min_val = min(input_list)\n",
        "    min_index = 0\n",
        "    for n, item in enumerate(input_list):\n",
        "        if item == min_val:\n",
        "            min_index = n\n",
        "            # Exit loop\n",
        "            break\n",
        "    \n",
        "    return min_index\n",
        "\n",
        "\n",
        "def get_topic_name(word_list):\n",
        "    '''\n",
        "    Uses the given `word_list` and \n",
        "    `nltk.corpus.wordnet.synsets(word)` to \n",
        "    return the most likely single-word topic.\n",
        "    '''\n",
        "    # Initialize the Counter() object to track\n",
        "    # the number of times a word appears\n",
        "    word_meaning = collections.Counter()\n",
        "\n",
        "    # Loop through each word in the input list\n",
        "    for wrd in word_list:\n",
        "        # Initialize set to hold unique lemmas for each word\n",
        "        lemma_set = set()\n",
        "        # Loop through each synset for the given wrd\n",
        "        for ss in wordnet.synsets(wrd):\n",
        "            for lemma in ss.lemma_names():\n",
        "                lemma_set.add(lemma)\n",
        "        # Update the counter with the words that are related\n",
        "        # to the current wrd\n",
        "        word_meaning.update(lemma_set)\n",
        "    \n",
        "    # Return the most common word\n",
        "    # The first [0] indexes into the list and the second [0]\n",
        "    # indexes into the word ([1] returns the count)\n",
        "    if word_meaning.most_common(1)[0][1] > 1:\n",
        "        top_word = word_meaning.most_common(1)[0][0]\n",
        "    else:\n",
        "        top_word = 'N/A'\n",
        "    return top_word"
      ],
      "metadata": {
        "id": "0Zb2DnBl3iur",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82060155-a30f-43a4-a87d-a0411056bec8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
            "  from collections import Iterable\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare LDA models"
      ],
      "metadata": {
        "id": "IQTm9Ljva9uZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ==========================\n",
        "# Load and prepare text data\n",
        "# ==========================\n",
        "# Store stopwords in a list\n",
        "stopwords_list = nltk.corpus.stopwords.words('english')\n",
        "\n",
        "# Word lemmatization (reducing to core part of word, its lemma)\n",
        "lemma = nltk.stem.WordNetLemmatizer()\n",
        "\n",
        "unzip_given_file('./news_universe.zip')\n",
        "corpus = collect_corpus('./news_universe')\n",
        "cleaned_corpus = preprocess_text(corpus, stopwords_list, lemma, min_char_len=5)\n",
        "\n",
        "# ===================================\n",
        "# Create Gensim dictionary and corpus\n",
        "# ===================================\n",
        "# gensim.corpora.Dictionary stores words by an index.\n",
        "# It represents the complete vocabulary in the corpus.\n",
        "gensim_dict = gensim.corpora.Dictionary(cleaned_corpus)\n",
        "# Create a bag-of-words representation of each document in the corpus\n",
        "gensim_corpus = [gensim_dict.doc2bow(doc, allow_update=True) for doc in cleaned_corpus]\n",
        "\n",
        "\n",
        "# ====================\n",
        "# Create the LDA model\n",
        "# ====================\n",
        "models_dict = optimal_num_topics(gensim_dict,\n",
        "                       gensim_corpus,\n",
        "                       cleaned_corpus,\n",
        "                       num_passes=50,\n",
        "                       min_topics=2,\n",
        "                       max_topics=20,\n",
        "                       step=1)"
      ],
      "metadata": {
        "id": "xyOngdaIa1Ue"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the perplexity and coherence by topics\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(models_dict['num_topics'], models_dict['perplexity_scores'], color='darkblue', label='Perplexity')\n",
        "# Mark the lowest point\n",
        "xval = models_dict['num_topics'][argmin(models_dict['perplexity_scores'])]\n",
        "yval = min(models_dict['perplexity_scores'])\n",
        "plt.scatter(xval, yval, marker='o', color='darkblue', label='Min')\n",
        "plt.legend()\n",
        "plt.xlim(min(models_dict['num_topics']), max(models_dict['num_topics']))\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(models_dict['num_topics'], models_dict['coherence_scores'], color='darkred', label='Coherence')\n",
        "# Mark the highest point\n",
        "xval = models_dict['num_topics'][argmax(models_dict['coherence_scores'])]\n",
        "yval = max(models_dict['coherence_scores'])\n",
        "plt.scatter(xval, yval, marker='o', color='darkred', label='Max')\n",
        "plt.legend()\n",
        "plt.xlim(min(models_dict['num_topics']), max(models_dict['num_topics']))\n",
        "plt.xlabel('Number of topics')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "hxLTz_cFdYbH",
        "outputId": "79e4f986-8a7c-4e6f-d632-e083acccb6b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_perplexity = argmin(models_dict['perplexity_scores'])\n",
        "best_coherence = argmax(models_dict['coherence_scores'])\n",
        "\n",
        "if best_perplexity == best_coherence:\n",
        "    # Could use either best_perplexity or best_coherence, since they are equal\n",
        "    best_num_topics = models_dict['num_topics'][best_perplexity]\n",
        "    best_model = models_dict['models'][best_perplexity]\n",
        "    print(f\"Optimal number of topics is {best_num_topics} \"\n",
        "    + f\"which achieves a perplexity score of {models_dict['perplexity_scores'][best_perplexity]:0.2f} \"\n",
        "    + f\"and a coherence score of {models_dict['coherence_scores'][best_coherence]:0.2f}.\")\n",
        "else:\n",
        "    print(\"Choose the best model based on your judgement.\")\n",
        "    print(f\"The lowest perplexity score was {models_dict['perplexity_scores'][best_perplexity]:0.2f}, \"\n",
        "        + f\"with {models_dict['num_topics'][best_perplexity]} topics, and the highest coherence score was \"\n",
        "        + f\"{models_dict['coherence_scores'][best_coherence]:0.2f}, \"\n",
        "        + f\"with {models_dict['num_topics'][best_coherence]} topics.\")\n",
        "    print('Here are the perplexity and coherence scores:'\n",
        "      + '\\n=============================================')\n",
        "    print('Number of topics:', models_dict['num_topics'])\n",
        "    print('Perplexity scores:', [round(score, 2) for score in models_dict['perplexity_scores']])\n",
        "    print('Coherence scores:', [round(score, 2) for score in models_dict['coherence_scores']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gS933QYSa4fc",
        "outputId": "5741c519-24d1-476c-b3a7-24cac22957e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Choose the best model based on your judgement.\n",
            "The lowest perplexity score was -7.96, with 2 topics, and the highest coherence score was 0.53, with 49 topics.\n",
            "Here are the perplexity and coherence scores:\n",
            "=============================================\n",
            "Number of topics: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]\n",
            "Perplexity scores: [-7.96, -7.92, -7.9, -7.87, -7.87, -7.87, -7.84, -7.85, -7.87, -7.82, -7.83, -7.8, -7.78, -7.83, -7.8, -7.82, -7.87, -7.84, -7.83, -7.89, -7.8, -7.85, -7.81, -7.79, -7.82, -7.79, -7.85, -7.83, -7.81, -7.91, -7.82, -7.85, -7.87, -7.8, -7.88, -7.89, -7.91, -7.88, -7.92, -7.84, -7.87, -7.87, -7.8, -7.92, -7.85, -7.93, -7.89, -7.89, -7.87]\n",
            "Coherence scores: [0.26, 0.27, 0.33, 0.32, 0.3, 0.37, 0.38, 0.46, 0.38, 0.42, 0.43, 0.45, 0.48, 0.46, 0.46, 0.4, 0.39, 0.46, 0.4, 0.4, 0.53, 0.49, 0.46, 0.51, 0.46, 0.49, 0.48, 0.47, 0.52, 0.41, 0.52, 0.52, 0.49, 0.52, 0.43, 0.47, 0.48, 0.46, 0.5, 0.5, 0.46, 0.48, 0.53, 0.47, 0.52, 0.48, 0.49, 0.53, 0.47]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose the model with 9 topics (the 7th index)\n",
        "chosen_index = 7\n",
        "print('Chosen number of topics:', models_dict['num_topics'][chosen_index])\n",
        "best_model = models_dict['models'][chosen_index]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ysR772Cal-9a",
        "outputId": "6db75ade-bf92-41da-d444-f4bf5a27f3c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chosen number of topics: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save the topics"
      ],
      "metadata": {
        "id": "cyHzzUk7c4ts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_topics = best_model.num_topics"
      ],
      "metadata": {
        "id": "wETbQMrjz6gm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_list = best_model.show_topics(num_topics=num_topics, num_words=10, formatted=False)"
      ],
      "metadata": {
        "id": "k2b8yLL8c8Li"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estimate names for each topic and save the topics to a .csv file"
      ],
      "metadata": {
        "id": "emXZVZN7MiS5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample_topic_names = []\n",
        "all_words = []\n",
        "\n",
        "for topic in topic_list:\n",
        "    # A tuple of (topic_num, word_list)\n",
        "    # We need to access the second item, word_list\n",
        "    wrd_list = []\n",
        "    for item in topic[1]:\n",
        "        # A tuple of (word, probability)\n",
        "        # We need to access the first item, word\n",
        "        wrd_list.append(item[0])\n",
        "    all_words.append(wrd_list)\n",
        "    sample_topic_names.append(get_topic_name(wrd_list))\n",
        "\n",
        "with open('./news_topics.csv', mode='wt') as csv_file:\n",
        "    csv_writer = csv.writer(csv_file)\n",
        "    csv_writer.writerow(['Topic'] + sample_topic_names)\n",
        "    for i in range(len(all_words[0])):\n",
        "        one_row = []\n",
        "        for topic in all_words:\n",
        "            one_row.append(topic[i])\n",
        "        csv_writer.writerow([f'Word {i+1}'] + one_row)"
      ],
      "metadata": {
        "id": "UkRDSmSoMRBg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualize LDA model"
      ],
      "metadata": {
        "id": "6fq-4V4NbBlI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the visualization\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim_models\n",
        "lda_viz = pyLDAvis.gensim_models.prepare(best_model, gensim_corpus, gensim_dict, sort_topics=False)\n",
        "\n",
        "# Display the visualization\n",
        "pyLDAvis.display(lda_viz)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "e_uQ5mTDbCt0",
        "outputId": "52033505-72d8-4ee9-c2a0-7dcdfa6ce215"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyLDAvis/_prepare.py:247: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  by='saliency', ascending=False).head(R).drop('saliency', 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "\n",
              "<div id=\"ldavis_el591406098767652648144379795\"></div>\n",
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\"former\", \"former\", \"francis\", \"francis\", \"francis\", \"francis\", \"freedom\", \"freedom\", \"freedom\", \"gaylord\", \"gaylord\", \"gender\", \"george\", \"giardinelli\", \"going\", \"going\", \"going\", \"going\", \"going\", \"going\", \"going\", \"govherbert\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"group\", \"guerneville\", \"guerneville\", \"hannan\", \"hanoi\", \"harker\", \"health\", \"health\", \"health\", \"health\", \"health\", \"health\", \"health\", \"healthy\", \"healthy\", \"hearing\", \"hearing\", \"hearing\", \"helped\", \"helping\", \"helping\", \"herbert\", \"herbert\", \"hollywood\", \"honda\", \"house\", \"house\", \"house\", \"house\", \"house\", \"house\", \"house\", \"house\", \"house\", \"http\", \"http\", \"http\", \"http\", \"husband\", \"husband\", \"idaho\", \"idaho\", \"idaho\", \"identity\", \"identity\", \"ilhan\", \"impeachment\", \"inch\", \"include\", \"include\", \"include\", \"include\", \"include\", \"include\", \"include\", \"increase\", \"increase\", \"increase\", \"index\", \"index\", \"indicate\", \"indictment\", \"inflator\", \"intelligence\", \"intent\", \"interested\", \"investigation\", \"investigation\", \"investigation\", \"investigation\", \"investigation\", \"involved\", \"involved\", \"involved\", \"involved\", \"involved\", \"involved\", \"involvement\", \"israel\", \"israel\", \"jacquelyn\", \"jones\", \"journalist\", \"journalist\", \"jquery\", \"judge\", \"judge\", \"judge\", \"judge\", \"julie\", \"katseanes\", \"kelly\", \"ketamine\", \"ketamine\", \"khashoggi\", \"killer\", \"killer\", \"korea\", \"korea\", \"korean\", \"korean\", \"language\", \"language\", \"language\", \"language\", \"latter\", \"latter\", \"latter\", \"latter\", \"latter\", \"latter\", \"latter\", \"lawmaker\", \"lawmaker\", \"lawmaker\", \"lawmaker\", \"lawmaker\", \"lawyer\", \"lawyer\", \"lawyer\", \"lawyer\", \"leader\", \"leader\", \"leader\", \"leader\", \"leader\", \"leader\", \"leadership\", \"leadership\", \"leadership\", \"leadership\", \"leave\", \"leave\", \"leave\", \"leave\", \"leave\", \"leave\", \"legislation\", \"legislation\", \"legislation\", \"legislation\", \"lgbtq\", \"lgbtq\", \"liddiard\", \"lisonbee\", \"lisonbee\", \"list\", \"lived\", \"lived\", \"longest\", \"maloney\", \"marble\", \"march\", \"march\", \"march\", \"march\", \"march\", \"march\", \"march\", \"march\", \"march\", \"martin\", \"martin\", \"martin\", \"mascot\", \"mayor\", \"medium\", \"medium\", \"medium\", \"melbourne\", \"member\", \"member\", \"member\", \"member\", \"member\", \"member\", \"member\", \"member\", \"mental\", \"mental\", \"mental\", \"mental\", \"mental\", \"mental\", \"meredith\", \"meter\", \"mexico\", \"mexico\", \"michael\", \"michael\", \"michael\", \"million\", \"million\", \"million\", \"million\", \"million\", \"million\", \"minor\", \"minor\", \"minor\", \"minus\", \"mobile\", \"money\", \"money\", \"money\", \"money\", \"money\", \"money\", \"monte\", \"month\", \"month\", \"month\", \"month\", \"month\", \"month\", \"month\", \"mueller\", \"multiple\", \"multiple\", \"multiple\", \"multiple\", \"music\", \"music\", \"muslim\", \"nadler\", \"national\", \"national\", \"national\", \"national\", \"national\", \"national\", \"nearly\", \"nearly\", \"nearly\", \"nearly\", \"nearly\", \"nearly\", \"netflix\", \"netflix\", \"netflix\", \"newport\", \"nhtsa\", \"nielson\", \"nondefense\", \"north\", \"north\", \"north\", \"north\", \"north\", \"northern\", \"northern\", \"northern\", \"northern\", \"nuclear\", \"offer\", \"offer\", \"offer\", \"offer\", \"offer\", \"official\", \"official\", \"official\", \"official\", \"official\", \"official\", \"official\", \"opioid\", \"orchestra\", \"orientation\", \"orientation\", \"outlet\", \"outlet\", \"oversight\", \"owner\", \"owner\", \"owner\", \"palestinian\", \"panel\", \"panel\", \"parent\", \"parent\", \"parent\", \"parent\", \"parent\", \"parent\", \"parent\", \"parental\", \"parental\", \"parliament\", \"patient\", \"patient\", \"patient\", \"patient\", \"patient\", \"pattern\", \"payment\", \"pelosi\", \"pelosi\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"percent\", \"percent\", \"percent\", \"percent\", \"percent\", \"percent\", \"percent\", \"perform\", \"performing\", \"perry\", \"photo\", \"photo\", \"photo\", \"photo\", \"photo\", \"photo\", \"photo\", \"photo\", \"photo\", \"physical\", \"physical\", \"physical\", \"physical\", \"placement\", \"placement\", \"plane\", \"platform\", \"platform\", \"player\", \"player\", \"player\", \"police\", \"police\", \"police\", \"police\", \"portfolio\", \"practice\", \"practice\", \"practice\", \"preferential\", \"president\", \"president\", \"president\", \"president\", \"president\", \"president\", \"president\", \"press\", \"press\", \"press\", \"press\", \"press\", \"press\", \"press\", \"press\", \"price\", \"prison\", \"prison\", \"prison\", \"probe\", \"probe\", \"probe\", \"process\", \"process\", \"process\", \"process\", \"process\", \"process\", \"process\", \"prohibition\", \"project\", \"project\", \"project\", \"project\", \"project\", \"proposal\", \"proposal\", \"protect\", \"protect\", \"protect\", \"protect\", \"protect\", \"protected\", \"protects\", \"protects\", \"protestors\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provides\", \"provides\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"quada\", \"qualify\", \"recall\", \"recalled\", \"received\", \"received\", \"received\", \"received\", \"received\", \"received\", \"recognizes\", \"recruit\", \"recruited\", \"recruiting\", \"reform\", \"rehearse\", \"relative\", \"rental\", \"repeat\", \"repeat\", \"repeat\", \"researcher\", \"resident\", \"resident\", \"resident\", \"resident\", \"resident\", \"resisted\", \"resolution\", \"resolution\", \"reviving\", \"right\", \"right\", \"right\", \"right\", \"right\", \"river\", \"river\", \"river\", \"river\", \"romero\", \"rule\", \"rule\", \"rule\", \"rule\", \"russia\", \"russian\", \"russian\", \"russian\", \"sailing\", \"saint\", \"saint\", \"saint\", \"saint\", \"saint\", \"saint\", \"saint\", \"santa\", \"saudi\", \"saying\", \"saying\", \"saying\", \"saying\", \"saying\", \"saying\", \"scandal\", \"scandal\", \"schloss\", \"scholarship\", \"school\", \"school\", \"school\", \"school\", \"school\", \"school\", \"secretary\", \"semitic\", \"semitism\", \"sentence\", \"sentence\", \"sentence\", \"sentencing\", \"session\", \"session\", \"session\", \"session\", \"session\", \"session\", \"sexual\", \"sexual\", \"sexual\", \"sexual\", \"sexual\", \"shelter\", \"shelter\", \"shift\", \"shift\", \"shift\", \"shock\", \"shown\", \"shown\", \"shutdown\", \"sierra\", \"significant\", \"significant\", \"sitting\", \"sitting\", \"sitting\", \"smith\", \"smith\", \"smith\", \"soccer\", \"software\", \"spending\", 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\"wednesday\", \"wednesday\", \"wednesday\", \"wednesday\", \"wednesday\", \"week\", \"week\", \"week\", \"week\", \"week\", \"white\", \"white\", \"white\", \"white\", \"white\", \"whole\", \"whole\", \"wilde\", \"willes\", \"williams\", \"williams\", \"williams\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"workday\", \"workfront\", \"world\", \"world\", \"world\", \"world\", \"world\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"xukjpln\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"youth\", \"youth\", \"youth\", \"youth\", \"youth\", \"youth\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3, 4, 5, 6, 7, 8, 9]};\n",
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      "source": [
        "# 11: Feature-label correlation\n",
        "2-Apr-2022\n",
        "\n",
        "## Purpose\n",
        "Choose two linguistic features that may be correlated with a numerical label (number of \"stars\" given in a review). Then, find the number of times each feature occurs in each review and compute correlation coefficients for those features with the label.\n",
        "\n",
        "## Potential features\n",
        "Good\n",
        "- Exclamation points\n",
        "- Shorter, but full sentences\n",
        "- Use of proper names (giving shout-outs to people by name)\n",
        "\n",
        "Bad\n",
        "- Longer (word count)\n",
        "- Long sentences (fewer periods, many conjunctions) \n",
        "- All caps\n",
        "- Imperative (\"Don't go here. No bueno.\")\n",
        "- Negative words (don't, never, won't, no)\n",
        " * https://www.grammarly.com/blog/negatives/\n",
        "- Absolutes (none, never, always, every)\n",
        "- Addresses the reader (\"_you_ should never buy this.\")\n",
        "- The word _this_ (\"_this_ is the worst place ever!\")\n",
        "- Use of euphemisms instead of correct names\n",
        "\n",
        "\n",
        "## Chosen features\n",
        "* **Avg. sentence length**. Hypothesis: positive reviews have shorter, but complete, sentences. Negative reviews have longer, drawn-out and rambling sentences that describe the details of a negative experience.\n",
        "* **Percentage of words in all caps**. Hypothesis: negative reviews use all-caps as a way to express anger or strong emotion.\n",
        "\n",
        "\n",
        "## Resources\n",
        "* [Yelp dataset documentation](https://www.yelp.com/dataset/documentation/main)"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# Assignment 11, LING 360, Text Analytics in Python\n",
        "# Updated on: 2-Apr-2022\n",
        "# Author: Ryan Parker\n",
        "\n",
        "# ==================================================\n",
        "# Determine relationship between linguistic features\n",
        "# and a numeric label\n",
        "# ==================================================\n",
        "'''\n",
        "Author: Ryan Parker\n",
        "Updated on: 2-Apr-2022\n",
        "\n",
        "Purpose: Choose two linguistic features that may be correlated \n",
        "with a numeric label (number of \"stars\" given in a review). \n",
        "Then, find the number of times each feature occurs in each review \n",
        "and compute correlation coefficients for those features with the label.\n",
        "Finally, create regression plots (scatter plot with regression line) \n",
        "showing the relationship between each linguistic feature and the label.\n",
        "'''\n",
        "\n",
        "import pandas as pd                 # Tabular data manipulation\n",
        "import json                         # JSON parsing\n",
        "import re                           # RegEx pattern matching\n",
        "import matplotlib.pyplot as plt     # General-purpose plotting library\n",
        "import seaborn as sns               # Statistical plotting library"
      ],
      "metadata": {
        "id": "1xhjanzkNK49"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Open the .JSON file and write its data to\n",
        "# a Python dictionary called json_data\n",
        "with open('./yelp_AZ_2018.json', mode='rt') as jsonfile:\n",
        "    # json_data = json.load(onefile)\n",
        "    json_data = [json.loads(line) for line in jsonfile]\n",
        "\n",
        "print(f\"There are {len(json_data):,d} reviews\")"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "h-RO87naTldv",
        "outputId": "b3e6a0e3-6655-4384-bdf5-37d2f5e58715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 119,376 reviews\n"
          ]
        }
      ]
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      "source": [
        "df = pd.DataFrame(json_data)\n",
        "df.head()"
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          "height": 475
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        "# Remove unnecessary columns\n",
        "df = df[['stars', 'text']]\n",
        "df.head()"
      ],
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            "text/plain": [
              "   stars                                               text\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RegEx patterns for my chosen features:\n",
        "* [Sentence length](https://pythex.org/?regex=%5B.%3F!%5D%7B1%2C%7D&test_string=%22Wow.%20What%20an%20amazing%20product!%20I%20mean%2C%20incredible%2C%20right%3F!%22&ignorecase=0&multiline=0&dotall=0&verbose=0): `re.findall(pattern=r'[.?!]{1,}', string=review_text)`, which returns a list of end-of-sentence punctuation (ignoring repeat-punctuation like '?!', '!!!', or '...'). I find the `len()` of that list, then I find `len(review_text.split())` (the word count) and divide the word count by the number of sentences to get the average sentence length (words per sentence).\n",
        "* [Percentage of words (2+ characters) in all caps](https://pythex.org/?regex=%5BA-Z%5D%7B2%2C%7D&test_string=I%20DON%27T%20RECOMMEND%20THIS%20PRODUCT!!!%20BAD%20EXPERIENCE!!!%20Let%20me%20share%20the%20details%3A%20I%20was%20walking%20down%20the%20road%2C%20when%20BAM%2C%20the%20product%20fell%20from%20the%20sky%20and%20then%20totally%20made%20my%20day%20rough%20--%20and%20I%20didn%27t%20do%20anything%20to%20deserve%20it!%20So%20now%20you%20know%2C%20don%27t%20buy%20that%20product.&ignorecase=0&multiline=0&dotall=0&verbose=0): `re.findall(r\"[A-Z]{2,}\", string=review_text)`, which returns a list of words in all caps, so long as the words have 2 or more letters. I find the `len()` of that list, and divide it by the total word count in the review to get the percentage of words in all caps."
      ],
      "metadata": {
        "id": "DVmTthTasV0H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# test_sentence = \"Wow. What an amazing product! I mean, incredible, right?!\"\n",
        "test_sentence = \"I DON'T RECOMMEND THIS PRODUCT!!! BAD EXPERIENCE!!! Let me share the details: I was walking down the road, when BAM, the product fell from the sky and then totally made my day rough -- and I didn't do anything to deserve it! So now you know, don't buy that product.\"\n",
        "\n",
        "num_sentences = re.findall(r'[.?!]{1,}', string=test_sentence)\n",
        "print(f\"End-of-sentence punctuation found: {num_sentences}\")\n",
        "print(f\"Number of sentences: {len(num_sentences)}\")\n",
        "print(f\"Avg sentence length: {len(test_sentence.split()) / len(num_sentences):.1f} words\")\n",
        "print()\n",
        "\n",
        "num_all_caps_words = re.findall(r\"[A-Z]{2,}\", string=test_sentence)\n",
        "print(f\"Words in all caps: {num_all_caps_words}\")\n",
        "print(f\"Number of words in all caps: {len(num_all_caps_words)}\")\n",
        "print(f\"Percentage of words in all caps: {len(num_all_caps_words) / len(test_sentence.split()):.2f}\")"
      ],
      "metadata": {
        "id": "XeHCzumMbcuV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c598a35-e185-4b04-8061-cc3ccff7b6bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "End-of-sentence punctuation found: ['!!!', '!!!', '!', '.']\n",
            "Number of sentences: 4\n",
            "Avg sentence length: 12.5 words\n",
            "\n",
            "Words in all caps: ['DON', 'RECOMMEND', 'THIS', 'PRODUCT', 'BAD', 'EXPERIENCE', 'BAM']\n",
            "Number of words in all caps: 7\n",
            "Percentage of words in all caps: 0.14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test of sentence length function\n",
        "test_lambda = lambda review_text: len(review_text.split()) / len(re.findall(r'[.?!]{1,}', review_text))\n",
        "\n",
        "# Avg words per sentence\n",
        "test_lambda(test_sentence)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgddJMNI2s6x",
        "outputId": "b79d4d74-5449-4636-8876-12eb2ecbac76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.5"
            ]
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          "metadata": {},
          "execution_count": 9
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    },
    {
      "cell_type": "code",
      "source": [
        "# Test of all-caps function\n",
        "test_lambda = lambda review_text: len(re.findall(r'[A-Z]{2,}', review_text)) / len(review_text.split())\n",
        "\n",
        "# Pct of all-caps words\n",
        "test_lambda(test_sentence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8iqPuagV1PK1",
        "outputId": "01337158-c0e8-4660-da21-46dddc4e1a02"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.14"
            ]
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          "metadata": {},
          "execution_count": 10
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# Full functions\n",
        "def avg_sentence_length(review_text):\n",
        "    '''\n",
        "    Returns the average number of words per sentence\n",
        "    in the input `review_text`.\n",
        "    '''\n",
        "    num_words = len(review_text.split())\n",
        "    num_sentences = len(re.findall(r'[.?!]{1,}', review_text))\n",
        "\n",
        "    # Ensure the number of sentences is at least 1\n",
        "    # to prevent ZeroDivisionError\n",
        "    if num_sentences == 0:\n",
        "        num_sentences = 1\n",
        "\n",
        "    return num_words / num_sentences\n",
        "\n",
        "\n",
        "\n",
        "def all_caps_pct(review_text):\n",
        "    '''\n",
        "    Returns the percentage of words (2+ characters)\n",
        "    that are in ALL CAPS.\n",
        "    '''\n",
        "    num_words_all_caps = len(re.findall(r'[A-Z]{2,}', review_text))\n",
        "    num_words = len(review_text.split())\n",
        "\n",
        "    # Ensure the number of words is at least 1\n",
        "    # to prevent ZeroDivisionError\n",
        "    if num_words == 0:\n",
        "        num_words = 1\n",
        "\n",
        "    return num_words_all_caps / num_words"
      ],
      "metadata": {
        "id": "bvC22lGW3Fd5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create columns for linguistic features\n",
        "df['All_caps_pct'] = df['text'].apply(lambda review_text: all_caps_pct(review_text))\n",
        "df['Avg_sentence_length'] = df['text'].apply(lambda review_text: avg_sentence_length(review_text))\n",
        "\n",
        "# Save as a .csv file\n",
        "df[['stars', 'All_caps_pct', 'Avg_sentence_length']].to_csv('./Parker_11.csv', index=False)\n",
        "\n",
        "df.head()"
      ],
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              "   stars                                               text  All_caps_pct  \\\n",
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              "          const element = document.querySelector('#df-ef300366-546a-4c9a-b716-c46a6c1892cf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show correlation\n",
        "corr = df[['stars', 'All_caps_pct', 'Avg_sentence_length']].corr()\n",
        "corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
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        "outputId": "7267e85e-43a8-487c-baa6-25884f705455"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        stars  All_caps_pct  Avg_sentence_length\n",
              "stars                1.000000     -0.069694            -0.149376\n",
              "All_caps_pct        -0.069694      1.000000            -0.022585\n",
              "Avg_sentence_length -0.149376     -0.022585             1.000000"
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              "      <th>stars</th>\n",
              "      <th>All_caps_pct</th>\n",
              "      <th>Avg_sentence_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>stars</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.069694</td>\n",
              "      <td>-0.149376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All_caps_pct</th>\n",
              "      <td>-0.069694</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.022585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg_sentence_length</th>\n",
              "      <td>-0.149376</td>\n",
              "      <td>-0.022585</td>\n",
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              "      </button>\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-06abcdb4-426b-494a-80e5-a7d88d742d99 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-06abcdb4-426b-494a-80e5-a7d88d742d99');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Heatmap versions\n",
        "# See: https://stackoverflow.com/questions/29432629/plot-correlation-matrix-using-pandas/50703596#50703596\n",
        "# and: https://datatofish.com/correlation-matrix-pandas/\n",
        "\n",
        "corr.style.background_gradient(cmap='coolwarm', axis=None).format(precision=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "mYODRh_Ug5xG",
        "outputId": "ca4bdff1-6af9-49c2-8f5f-4e158ec653fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f00cff16890>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_37f58_row0_col0, #T_37f58_row1_col1, #T_37f58_row2_col2 {\n",
              "  background-color: #b40426;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_37f58_row0_col1, #T_37f58_row1_col0 {\n",
              "  background-color: #4f69d9;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_37f58_row0_col2, #T_37f58_row2_col0 {\n",
              "  background-color: #3b4cc0;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "#T_37f58_row1_col2, #T_37f58_row2_col1 {\n",
              "  background-color: #5d7ce6;\n",
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              "<table id=\"T_37f58_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >stars</th>\n",
              "      <th class=\"col_heading level0 col1\" >All_caps_pct</th>\n",
              "      <th class=\"col_heading level0 col2\" >Avg_sentence_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_37f58_level0_row0\" class=\"row_heading level0 row0\" >stars</th>\n",
              "      <td id=\"T_37f58_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
              "      <td id=\"T_37f58_row0_col1\" class=\"data row0 col1\" >-0.07</td>\n",
              "      <td id=\"T_37f58_row0_col2\" class=\"data row0 col2\" >-0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37f58_level0_row1\" class=\"row_heading level0 row1\" >All_caps_pct</th>\n",
              "      <td id=\"T_37f58_row1_col0\" class=\"data row1 col0\" >-0.07</td>\n",
              "      <td id=\"T_37f58_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
              "      <td id=\"T_37f58_row1_col2\" class=\"data row1 col2\" >-0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_37f58_level0_row2\" class=\"row_heading level0 row2\" >Avg_sentence_length</th>\n",
              "      <td id=\"T_37f58_row2_col0\" class=\"data row2 col0\" >-0.15</td>\n",
              "      <td id=\"T_37f58_row2_col1\" class=\"data row2 col1\" >-0.02</td>\n",
              "      <td id=\"T_37f58_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df[['stars', 'All_caps_pct', 'Avg_sentence_length']].corr(), annot=True, fmt='.2f');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "SpVYqA8wgcxv",
        "outputId": "e496a98d-b419-45d5-8cf2-c4db50fdf2dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show scatter plots and regression lines\n",
        "# See: http://seaborn.pydata.org/tutorial/regression.html\n",
        "sns.regplot(x=df['All_caps_pct'], y=df['stars'])\n",
        "plt.title('Percentage of words in all caps vs. number of stars')\n",
        "plt.ylim(0, 6)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "wJ21C5eS4wny",
        "outputId": "cb7adfbc-1657-4bc5-ae0b-43187893a1e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x=df['Avg_sentence_length'], y=df['stars'])\n",
        "# Or, for just the scatter plot\n",
        "# df.plot.scatter(x='Avg_sentence_length', y='stars')\n",
        "# or simply\n",
        "# plt.scatter(x=df['Avg_sentence_length'], y=df['stars'])\n",
        "plt.title('Avg. words per sentence vs. number of stars')\n",
        "plt.ylim(0, 6)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "aiM5gq8k5Lum",
        "outputId": "e81f0675-2444-4ed9-c51a-4ec0d41804ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# All plots together\n",
        "fig, (ax_corr, ax_caps, ax_sent) = plt.subplots(3, figsize=(6, 18))\n",
        "\n",
        "# Create plots\n",
        "sns.heatmap(corr, annot=True, fmt='.2f', ax=ax_corr)\n",
        "sns.regplot(x=df['All_caps_pct'], y=df['stars'], ax=ax_caps)\n",
        "sns.regplot(x=df['Avg_sentence_length'], y=df['stars'], ax=ax_sent)\n",
        "\n",
        "# Set titles\n",
        "ax_corr.set_title('Correlation matrix')\n",
        "ax_caps.set_title('Percentage of words in all caps vs. number of stars')\n",
        "ax_sent.set_title('Avg. words per sentence vs. number of stars')\n",
        "\n",
        "# Adjust limits\n",
        "ax_caps.set_ylim(0, 6)\n",
        "ax_sent.set_ylim(0, 6)\n",
        "\n",
        "# Display the plots\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HTPXfBlkqsbG",
        "outputId": "4f4e0794-ac28-4624-caa2-55e02e735460"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x1296 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test accuracy of RegEx patterns"
      ],
      "metadata": {
        "id": "-XLPIeckwGyX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ask user to confirm metrics\n",
        "def confirm_accuracy(sample_list):\n",
        "    '''\n",
        "    Confirm function accuracy by asking a user to \n",
        "    supply values in place of the functions.\n",
        "\n",
        "    Returns a dictionary of the form:\n",
        "    ```python\n",
        "    {\n",
        "        'Text': sample_list,\n",
        "        'Num all caps': [],\n",
        "        'Num sents': [],\n",
        "        'Function all caps': [],\n",
        "        'Function sents': [],\n",
        "    }\n",
        "    ```\n",
        "    Also, princs precision and recall, where\n",
        "    precision and recall are calculated as:\n",
        "    precision = (num_correct_predictions / total_predictions)\n",
        "    recall = (num_correct_predictions / num_true_values)\n",
        "    '''\n",
        "    return_dict = {\n",
        "        'Text': sample_list,\n",
        "        'All caps count': [],\n",
        "        'Sents count': [],\n",
        "        'Function all caps': [],\n",
        "        'Function sents': []\n",
        "    }\n",
        "\n",
        "    for n, review_text in enumerate(sample_list):\n",
        "        print(f'Review text: {n+1}/{len(sample_list)}')\n",
        "        print('-'*20)\n",
        "        # Add a newline after each sentence to make them easier to read.\n",
        "        print(re.sub(r'([.?!]{1,})', r'\\1\\n', review_text))\n",
        "        print('-'*20)\n",
        "        sent_count = int(input('How many sentences are in the review text above? '))\n",
        "        all_caps_count = int(input('How many words in ALL CAPS are in the review text above? '))\n",
        "        print('='*20)\n",
        "        print('\\n')\n",
        "\n",
        "        # Collect metrics by function\n",
        "        function_sent_count = len(re.findall(r'[.?!]{1,}', review_text))\n",
        "        function_all_caps_count = len(re.findall(r\"[A-Z]{2,}\", review_text))\n",
        "\n",
        "        # Save values\n",
        "        return_dict['All caps count'].append(all_caps_count)\n",
        "        return_dict['Sents count'].append(sent_count)\n",
        "        return_dict['Function all caps'].append(function_all_caps_count)\n",
        "        return_dict['Function sents'].append(function_sent_count)\n",
        "    \n",
        "    # Calculate precision and recall\n",
        "    # precision = (num_correct_predictions / total_predictions)\n",
        "    # recall = (num_correct_predictions / num_true_values)\n",
        "\n",
        "    # Currently, recall is set to the inverse of precision since the \n",
        "    # RegEx patterns haven't missed correct matches, they have just caught\n",
        "    # some that are not correct.\n",
        "    print(f\"Precision, count of all caps: {sum(return_dict['All caps count']) / sum(return_dict['Function all caps']):.3f}\")\n",
        "    print(f\"Recall, count of all caps: {sum(return_dict['Function all caps']) / sum(return_dict['All caps count']):.3f}\")\n",
        "    print(f\"Precision, sentence count: {sum(return_dict['Sents count']) / sum(return_dict['Function sents']):.3f}\")\n",
        "    print(f\"Recall, sentence count: {sum(return_dict['Function sents']) / sum(return_dict['Sents count']):.3f}\")\n",
        "    \n",
        "    return return_dict"
      ],
      "metadata": {
        "id": "21ZPJ_KiwKEE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a sample of data\n",
        "sample = df['text'].sample(n=20, random_state=1)\n",
        "sample = sample.to_list()\n",
        "\n",
        "user_input = confirm_accuracy(sample)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vs2Yr2UmytgU",
        "outputId": "981ee4e3-cc04-4bc7-dfc3-a6b628dc6c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Review text:\n",
            "--------------------\n",
            "The food is made to order.\n",
            " We waited for about a few minutes for them to prepare it.\n",
            " It was worth the wait.\n",
            " The rice was so full of flavor and fluffy.\n",
            " The machaca was so juicy and had a robust flavor not over powering.\n",
            " Everything was so fresh and hot.\n",
            " I recommend this place.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 7\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "This place has amazing food!\n",
            " I got the eggplant entree with the rosemary and garlic naan and of course rice.\n",
            " It was delicious!\n",
            " The portions are big so I had leftovers for lunch the next day and I wanted more!\n",
            " \n",
            "I went on a Friday night and it was pretty empty which was nice- no waiting for a table or waiting forever to get our orders.\n",
            " The staff was very friendly and personable.\n",
            " \n",
            "I would definitely recommend going here!\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 7\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "This was our first time at a Smashburger so we are not certain if all locations operate the same way, but this was not a good experience.\n",
            " First, the cashier/order taker was very helpful navigating the menu and was efficient.\n",
            "  Next, when the food finally appeared, it was good.\n",
            "  The problem is staffing and cleanliness.\n",
            "  No one removed order numbers, cleaned the tables or bothered to empty the clearly overflowing garbage can or removes used glasses and dishes.\n",
            "  We were there about 45 - 60 minutes and never saw anyone take action.\n",
            "   Lastly, it appears Smashburger is under-staffing and we won't likely return as organization and cleanliness are key to us.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 7\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "Worst service!\n",
            " Arrived last night at 11pm after a concert at Ak-chin.\n",
            " \n",
            "\n",
            "Server was terrible about refilling drinks, bringing out appetizers before the meal.\n",
            " Took an hour to get our food and then it wasn't right and was luke warm.\n",
            " \n",
            "\n",
            "She blamed it on the kitchen but her personality and lack of service skills made matters worse.\n",
            " Many other patrons walked out before even getting their food \n",
            "\n",
            "Hopefully she realized how terrible she was by the lack of a tip left.\n",
            " \n",
            "\n",
            "Also, a friend of ours ordered coffee and had gave her a $5 and she never brought back his change.\n",
            " He was so tired of waiting he left without it.\n",
            " \n",
            "\n",
            "Sad, you lost so much money for that lack of service from your servers.\n",
            " Because none of them were stepping in to assist.\n",
            " It's a reflection of your business.\n",
            " Looked like that Filiberto's next door made a wise decision to open there to catch all your customers who walked out.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 13\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "We are so very grateful to Dr Sheena and her staff for their incredible compassion, and wonderful support they provided when it was time to let our beautiful boy Cody cross over the Rainbow Bridge.\n",
            "  While it was one of the hardest things we have ever had to do, being able to have him at home, with us for our final goodbyes was truly a gift.\n",
            "  Dr Sheena explained what would take place, and helped us through this difficult time.\n",
            "  We will be forever grateful to you for your understanding and compassion.\n",
            "  You are an Angel!\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 5\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "I really enjoyed coming here!\n",
            " Small, bus your own table kind of a place.\n",
            " The first time we came we just ordered takeout.\n",
            " The burger was absolutely everything wifey was craving!\n",
            " I also tried the brautwurst on special and I enjoyed it, but found the amount of mustard was a bit overwhelming.\n",
            " The buffalo bites are SO good!\n",
            " The fries are thin and crispy deliciousness!\n",
            " Last visit we got the Santa Monica burger and it was amazing!\n",
            " Right amounts of everything!!!!!\n",
            " Tsoynaumi* was also great!\n",
            " We will definitely be back, working our way through the menu.\n",
            " A little expensive, but worth the treat in yo face!\n",
            " Vegan food for the win!\n",
            " Nomnomnom\n",
            "--------------------\n",
            "How many sentences are in the review text above? 14\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "I am very particular about dentist I let do any work on me.\n",
            " I found Dr.\n",
            " McAdams by reading yelp.\n",
            " He is a very amazing gentle dentist.\n",
            " He makes you feel comfortable and is a conservative dentist.\n",
            " I would recommend him and his dental team to anyone.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 5\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "BBQ done right.\n",
            " Everything everyone has ever said about this place is true.\n",
            " Amazing food.\n",
            " If you plan on going, make sure you have at least 2-3 hours of your day blocked out because you'll be standing in line for a good hour waiting.\n",
            " Is it worth?\n",
            " I'd say so.\n",
            " I heard there's a second location popping up around the west valley and I am excited for that one.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 7\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "This place was a great place to take my kids.\n",
            " My sons eat all of their kids teriyaki beef bowl.\n",
            " And it came with a drink for $6.\n",
            "29.\n",
            " I got the lunch special which was the regular sized chicken and beef bowl plus a drink for $8.\n",
            "29.\n",
            " The large beef and chicken teriyaki bowl was $8.\n",
            "29.\n",
            " \n",
            "I really enjoy the flavor of their teriyaki sauce.\n",
            " This isn't my first time here, it won't be my last.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 7\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "These guys lied to me in writing and on the phone just to get me in their doors.\n",
            " I went to buy the truck they said would met my in-writing must haves and they said would fit in my budget.\n",
            " I actually found a truck on their website for the price I was telling them I wanted to pay and it had all the things I wanted.\n",
            " I arrived, test drive and was ready to buy but then they added $15,000 to the price for the upgrades that they forgot to include.\n",
            " They advertised it online with these upgrades and for the lower price but said it was an accident because someone hadn't updated their website to include the cost of the dealer upgrades.\n",
            " They emailed me in writing confirming the price range and the upgrades I wanted but admitted the online rep misled me.\n",
            " I wish I was a lawyer...\n",
            " I probably would have that truck.\n",
            " BUT I went to Peoria Ford the next am and the salesmen, Anthony  Perez, got me into a very similar truck for UNDER the price I wanted to pay.\n",
            " BELL FORD guys are slime bags.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 9\n",
            "How many words in ALL CAPS are in the review text above? 4\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "I recently had a great experience with Alamo @ the PHX airport this past holiday weekend.\n",
            " I originally had a reservation with a competitor, but after seeing almost 50 people in line (and very few agents), I went to check availability @ Alamo.\n",
            " They were fully staffed in anticipation of Presidents Day weekend.\n",
            " Phil was able to offer me a great deal on a midsize SUV.\n",
            " He explained the charges, offered optional services, and gave me detailed directions to Sedona.\n",
            " I was able to choose a car of my liking, and went with a new Ford Escape.\n",
            " A nice, young gentleman showed me to the car, and helped load my luggage (I think his name was Ty).\n",
            " Thanks Phil, for the great service and a great start to a holiday weekend in Arizona!\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 8\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "Sight lines from the outside walkway into the kitchen.\n",
            " Simple, rustic farm style decor.\n",
            " The food simple, subtle, pure and pretty much true to what you might find in Italy.\n",
            "  Start with hot bread with olive oil excellent.\n",
            " Had the sonnny boy pizza, great crust, each item on the pizza just added the right flavor especially the olives.\n",
            "  Got the papparadelle and again simple yet the bolognese and the parmesean just make it true touts Italian roots.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 6\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "It's too bad this place has horrible customer service.\n",
            " Last few times we have ordered from them have been awful.\n",
            " Takes over an hour and a half, always something happens and food is late and then the order is not complete!\n",
            " Then you are still charged for items you didn't even receive and then when you call, they say \"we will take that item off and give you a $10 credit for next time\" ....\n",
            "I'm over it.\n",
            " Don't order from here is you want what you order\n",
            "--------------------\n",
            "How many sentences are in the review text above? 5\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "This is the second Vig we have visited and both have inadequate parking.\n",
            " We won't return to this one because we were told after 4:30 all parking was valet.\n",
            " The parking areas were virtually empty.\n",
            " We parked across the street in the shopping center lot and had to cross a dangerous street twice.\n",
            " I can't believe people will hand the keys to their second most valuable asset over to a person they have met for the first time in their lives.\n",
            " I can't believe a business would dictate such conditions to their customers, the real employers, or that customers will tolerate such behavior.\n",
            " This doesn't even cover the fact that there are valuables in the car and I have to leave my gun in there when entering a bar and drinking.\n",
            " \n",
            "The food was average and the service adequate.\n",
            " Not enough to warrant a return.\n",
            "\n",
            "\n",
            "https://www.\n",
            "ranker.\n",
            "com/list/valet-horror-stories/jessica-lawshe\n",
            "\n",
            "http://www.\n",
            "barkingdogs.\n",
            "org/news/node/374\n",
            "\n",
            "https://jalopnik.\n",
            "com/your-most-deeply-appalling-valet-horror-stories-1784975751\n",
            "--------------------\n",
            "How many sentences are in the review text above? 12\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "I love this restaurant and hotel and have stayed here several times since it opened two years ago.\n",
            " Unfortunately, the bar stock and offerings have been downgraded from fun and eclectic to mundane and boring.\n",
            " 5 out of 8 taps are dry, wine list slim and predictably passing in quality.\n",
            " Just not the right match anymore for the beautiful venue.\n",
            " I feel sorry for the bartenders.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 5\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "If the slow service doesn't turn you off, then the bad food will.\n",
            "  Four of us sat at the bar, where you think the service would be on point - especially when there were only 8 other people at the large bar.\n",
            "  We ordered food and viola 45 minutes later our burgers arrived cold and next to lifeless fries.\n",
            "  The cheese on the burger had that glaze and consistency of a plate that had sat in the service window way too long.\n",
            "  There is nothing that could bring me back to this chain.\n",
            "  Given the mediocre reviews of them all, I'm surprised they are still expanding.\n",
            "  I hear the time clock ticking away as they run out of new people willing to take a chance and finally have to improve or close.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 7\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "I called the nearest alteration spot after the horrible experience at Gemma's Fashion & Sewing.\n",
            " Rita was right across the street from there and helped us out right away without having an appointment.\n",
            " Received excellent customer service at a fantastic price.\n",
            " Worth the drive from east mesa!\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 4\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "Waiter Mike (and all the wait staff) were on point and always helped each other out in serving.\n",
            " Filet I had was awesome, my partner's had gristle.\n",
            " Gnocchi was incredibly decadent.\n",
            " Bread ok but not warm.\n",
            " Ate so much no room for the warm butter cake we wanted to try.\n",
            " Dining room very dated and dark.\n",
            " They should give everyone as a gift penlights to read the menu, or have lights on the table that can be adjusted.\n",
            " Without exception, all diners got out their phones to use the flashlights.\n",
            " That should give them a clue!\n",
            " Room was quiet until the drinks started flowing then people lowered their inhibitions and raised their voices!\n",
            " A millediot sat next to us wearing his baseball cap.\n",
            " This is fine dining not a brew pub!\n",
            " The host should have said something.\n",
            " Not once did anyone in management check on us, but we're all over everyone else.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 14\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "I stopped by to drop off a suit I blindly bought online on a recommendation from a friend and they really took care of everything I asked on time.\n",
            " I had to request a rush on my suit to be tailored but at $5 i felt like this was not a big deal.\n",
            " The consultation or sizing was really quick and straightforward I probably took more time changing into my suit than fine tuning the alterations.\n",
            " The big risk when I picked up the suit was I didn't have time to try it on because I was already late to where I was heading so I literally had to try it on before the wedding I went to and to my surprise everything was how I imagined.\n",
            " Its wedding season and I definitely am dropping off a few more suits to get them tailored here.\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 5\n",
            "How many words in ALL CAPS are in the review text above? 0\n",
            "====================\n",
            "\n",
            "\n",
            "Review text:\n",
            "--------------------\n",
            "Jeremy was SO friendly and knowledgeable!\n",
            " I tend to be apprehensive letting a male in my home when I'm home alone, but Jeremy was so polite and warm and quickly felt safe with him coming in and out.\n",
            " He also took off his shoes outside the front door when spraying the inside which was a very nice gesture.\n",
            " Thanks!\n",
            " I will absolutely use Urban Desert Pest Control again!\n",
            "\n",
            "--------------------\n",
            "How many sentences are in the review text above? 5\n",
            "How many words in ALL CAPS are in the review text above? 1\n",
            "====================\n",
            "\n",
            "\n",
            "Precision, count of all caps: 1.000\n",
            "Recall, count of all caps: 31.400\n",
            "Precision, sentence count: 1.000\n",
            "Recall, sentence count: 0.059\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Currently, recall is set to the inverse of precision since the \n",
        "# RegEx patterns haven't missed correct matches, they have just caught\n",
        "# some that are not correct.\n",
        "print(f\"Precision, count of all caps: {sum(user_input['All caps count']) / sum(user_input['Function all caps']):.3f}\")\n",
        "print(f\"Recall, count of all caps: {sum(user_input['Function all caps']) / sum(user_input['All caps count']):.3f}\")\n",
        "print(f\"Precision, sentence count: {sum(user_input['Sents count']) / sum(user_input['Function sents']):.3f}\")\n",
        "print(f\"Recall, sentence count: {sum(user_input['Function sents']) / sum(user_input['Sents count']):.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtcRHT6NYHMY",
        "outputId": "d8e18e22-984d-48fb-db94-3545134d4ec6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision, count of all caps: 0.556\n",
            "Recall, count of all caps: 1.800\n",
            "Precision, sentence count: 0.968\n",
            "Recall, sentence count: 1.033\n"
          ]
        }
      ]
    }
  ]
}