NLP_biden_inauguration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table(\"biden_speech.rtf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>{\\rtf1\\ansi\\ansicpg1252\\cocoartf2577</th>\n",
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       "      <td>\\paperw11900\\paperh16840\\margl1440\\margr1440\\v...</td>\n",
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       "      <th>4</th>\n",
       "      <td>\\deftab720</td>\n",
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       "      <th>...</th>\n",
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       "      <td>Sustained by faith.\\</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Driven by conviction.\\</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>And, devoted to one another and to this countr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>May God bless America and may God protect our ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Thank you, America.}</td>\n",
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       "4                                           \\deftab720\n",
       "..                                                 ...\n",
       "210                               Sustained by faith.\\\n",
       "211                             Driven by conviction.\\\n",
       "212  And, devoted to one another and to this countr...\n",
       "213  May God bless America and may God protect our ...\n",
       "214                               Thank you, America.}\n",
       "\n",
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     "execution_count": 3,
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   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.drop(df.index[0:8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.columns = ['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>8</th>\n",
       "      <td>This is America '92s day.</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This is democracy '92s day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A day of history and hope.</td>\n",
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       "      <th>11</th>\n",
       "      <td>Of renewal and resolve.</td>\n",
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       "      <th>12</th>\n",
       "      <td>Through a crucible for the ages America has be...</td>\n",
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      "text/plain": [
       "                                                 text\n",
       "8                          This is America '92s day. \n",
       "9                        This is democracy '92s day. \n",
       "10                        A day of history and hope. \n",
       "11                           Of renewal and resolve. \n",
       "12  Through a crucible for the ages America has be..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['text'] = df1['text'].str.replace(\"\\\\\",\" \")\n",
    "df1['text'] = df1['text'].str.replace(\"}\",\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>This is America '92s day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This is democracy '92s day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A day of history and hope.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Of renewal and resolve.</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Through a crucible for the ages America has be...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Sustained by faith.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Driven by conviction.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>And, devoted to one another and to this countr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>May God bless America and may God protect our ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Thank you, America.</td>\n",
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       "                                                  text\n",
       "8                           This is America '92s day. \n",
       "9                         This is democracy '92s day. \n",
       "10                         A day of history and hope. \n",
       "11                            Of renewal and resolve. \n",
       "12   Through a crucible for the ages America has be...\n",
       "..                                                 ...\n",
       "210                               Sustained by faith. \n",
       "211                             Driven by conviction. \n",
       "212  And, devoted to one another and to this countr...\n",
       "213  May God bless America and may God protect our ...\n",
       "214                               Thank you, America. \n",
       "\n",
       "[207 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conda install -c conda-forge textblob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just testing how TextBlob works. Use it in a function that you define."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t\n",
      "e\n",
      "x\n",
      "t\n"
     ]
    }
   ],
   "source": [
    "for i in df1:\n",
    "    for a in i:\n",
    "        blob = TextBlob(a)\n",
    "        print(blob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TextBlob(\"t\")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_calc(text):\n",
    "    try:\n",
    "        return TextBlob(text).sentiment\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['sentiment'] = df1['text'].apply(sentiment_calc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  text   sentiment\n",
      "8                           This is America '92s day.   (0.0, 0.0)\n",
      "9                         This is democracy '92s day.   (0.0, 0.0)\n",
      "10                         A day of history and hope.   (0.0, 0.0)\n",
      "11                            Of renewal and resolve.   (0.0, 0.0)\n",
      "12   Through a crucible for the ages America has be...  (0.0, 0.0)\n",
      "..                                                 ...         ...\n",
      "210                               Sustained by faith.   (0.0, 0.0)\n",
      "211                             Driven by conviction.   (0.0, 0.0)\n",
      "212  And, devoted to one another and to this countr...  (0.5, 0.6)\n",
      "213  May God bless America and may God protect our ...  (0.0, 0.0)\n",
      "214                               Thank you, America.   (0.0, 0.0)\n",
      "\n",
      "[207 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polarity_calc(text):\n",
    "    try:\n",
    "        return TextBlob(text).sentiment.polarity\n",
    "    except:\n",
    "        return None\n",
    "    \n",
    "    \n",
    "def subjectivity_calc(text):\n",
    "    try:\n",
    "        return TextBlob(text).sentiment.subjectivity\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Polarity ranges from -1 (very negative) to +1 (very positive), beign 0 a neutral sentiment\n",
    "- Subjectivity ranges from 0 (objective) to 1 (subjective)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['polarity'] = df1['text'].apply(polarity_calc)\n",
    "df1['subjectivity'] = df1['text'].apply(subjectivity_calc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes Analyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package movie_reviews to\n",
      "[nltk_data]     /Users/pablofernandezrodriguez/nltk_data...\n",
      "[nltk_data]   Package movie_reviews is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/pablofernandezrodriguez/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from textblob.sentiments import NaiveBayesAnalyzer\n",
    "import nltk\n",
    "nltk.download('movie_reviews')\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Naivebayes(text):\n",
    "    try:\n",
    "        return TextBlob(text, analyzer=NaiveBayesAnalyzer()).sentiment#.classification\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['NaiveBayes_sentiment'] = df1['text'].apply(Naivebayes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>8</th>\n",
       "      <td>This is America '92s day.</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>(pos, 0.7141082680830629, 0.28589173191693695)</td>\n",
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       "      <th>9</th>\n",
       "      <td>This is democracy '92s day.</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A day of history and hope.</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(pos, 0.722436249135278, 0.2775637508647214)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Of renewal and resolve.</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(pos, 0.833333333333334, 0.16666666666666688)</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Through a crucible for the ages America has be...</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(pos, 0.981129720113858, 0.01887027988614203)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Sustained by faith.</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(neg, 0.2999999999999994, 0.7000000000000001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Driven by conviction.</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(pos, 0.7281317494600437, 0.2718682505399568)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>And, devoted to one another and to this countr...</td>\n",
       "      <td>(0.5, 0.6)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>(pos, 0.8807337188765687, 0.11926628112343092)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>May God bless America and may God protect our ...</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(pos, 0.5592201902593549, 0.44077980974064507)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Thank you, America.</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(pos, 0.677530464268401, 0.32246953573159953)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  text   sentiment  polarity  \\\n",
       "8                           This is America '92s day.   (0.0, 0.0)       0.0   \n",
       "9                         This is democracy '92s day.   (0.0, 0.0)       0.0   \n",
       "10                         A day of history and hope.   (0.0, 0.0)       0.0   \n",
       "11                            Of renewal and resolve.   (0.0, 0.0)       0.0   \n",
       "12   Through a crucible for the ages America has be...  (0.0, 0.0)       0.0   \n",
       "..                                                 ...         ...       ...   \n",
       "210                               Sustained by faith.   (0.0, 0.0)       0.0   \n",
       "211                             Driven by conviction.   (0.0, 0.0)       0.0   \n",
       "212  And, devoted to one another and to this countr...  (0.5, 0.6)       0.5   \n",
       "213  May God bless America and may God protect our ...  (0.0, 0.0)       0.0   \n",
       "214                               Thank you, America.   (0.0, 0.0)       0.0   \n",
       "\n",
       "     subjectivity                            NaiveBayes_sentiment  \n",
       "8             0.0  (pos, 0.7141082680830629, 0.28589173191693695)  \n",
       "9             0.0  (pos, 0.9164115235197636, 0.08358847648023618)  \n",
       "10            0.0    (pos, 0.722436249135278, 0.2775637508647214)  \n",
       "11            0.0   (pos, 0.833333333333334, 0.16666666666666688)  \n",
       "12            0.0   (pos, 0.981129720113858, 0.01887027988614203)  \n",
       "..            ...                                             ...  \n",
       "210           0.0   (neg, 0.2999999999999994, 0.7000000000000001)  \n",
       "211           0.0   (pos, 0.7281317494600437, 0.2718682505399568)  \n",
       "212           0.6  (pos, 0.8807337188765687, 0.11926628112343092)  \n",
       "213           0.0  (pos, 0.5592201902593549, 0.44077980974064507)  \n",
       "214           0.0   (pos, 0.677530464268401, 0.32246953573159953)  \n",
       "\n",
       "[207 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([Sentiment(classification='pos', p_pos=0.7141082680830629, p_neg=0.28589173191693695),\n",
       "       Sentiment(classification='pos', p_pos=0.9164115235197636, p_neg=0.08358847648023618),\n",
       "       Sentiment(classification='pos', p_pos=0.722436249135278, p_neg=0.2775637508647214),\n",
       "       Sentiment(classification='pos', p_pos=0.833333333333334, p_neg=0.16666666666666688),\n",
       "       Sentiment(classification='pos', p_pos=0.981129720113858, p_neg=0.01887027988614203),\n",
       "       Sentiment(classification='pos', p_pos=0.9926613974766996, p_neg=0.007338602523298675),\n",
       "       Sentiment(classification='pos', p_pos=0.5902468626700333, p_neg=0.4097531373299665),\n",
       "       Sentiment(classification='pos', p_pos=0.9040807131052253, p_neg=0.09591928689477519),\n",
       "       Sentiment(classification='pos', p_pos=0.9140625000000001, p_neg=0.08593750000000006),\n",
       "       Sentiment(classification='pos', p_pos=0.9087175078753806, p_neg=0.0912824921246198),\n",
       "       Sentiment(classification='pos', p_pos=0.9984596467046142, p_neg=0.0015403532953818463),\n",
       "       Sentiment(classification='pos', p_pos=0.9918054087755661, p_neg=0.008194591224436063),\n",
       "       Sentiment(classification='pos', p_pos=0.739787554951005, p_neg=0.26021244504899477),\n",
       "       Sentiment(classification='pos', p_pos=0.5440398276391807, p_neg=0.45596017236081887),\n",
       "       Sentiment(classification='pos', p_pos=0.9602584677651078, p_neg=0.03974153223489029),\n",
       "       Sentiment(classification='pos', p_pos=0.9787763792704849, p_neg=0.021223620729515516),\n",
       "       Sentiment(classification='pos', p_pos=0.9910362068240334, p_neg=0.008963793175965786),\n",
       "       Sentiment(classification='pos', p_pos=0.5820429036819045, p_neg=0.4179570963180954),\n",
       "       Sentiment(classification='pos', p_pos=0.8651502731093685, p_neg=0.1348497268906319),\n",
       "       Sentiment(classification='pos', p_pos=0.6820290013622865, p_neg=0.31797099863771405),\n",
       "       Sentiment(classification='pos', p_pos=0.963553305184212, p_neg=0.03644669481578732),\n",
       "       Sentiment(classification='neg', p_pos=0.3693216876810204, p_neg=0.6306783123189804),\n",
       "       Sentiment(classification='pos', p_pos=0.6468785187038657, p_neg=0.353121481296134),\n",
       "       Sentiment(classification='pos', p_pos=0.6602002845572944, p_neg=0.33979971544270543),\n",
       "       Sentiment(classification='pos', p_pos=0.6291064388961889, p_neg=0.3708935611038108),\n",
       "       Sentiment(classification='pos', p_pos=0.6137599481436133, p_neg=0.3862400518563866),\n",
       "       Sentiment(classification='pos', p_pos=0.5638468927497496, p_neg=0.43615310725025064),\n",
       "       Sentiment(classification='pos', p_pos=0.9529193524754956, p_neg=0.04708064752450435),\n",
       "       Sentiment(classification='neg', p_pos=0.030101810212901824, p_neg=0.9698981897870984),\n",
       "       Sentiment(classification='pos', p_pos=0.9372870039817178, p_neg=0.06271299601828163),\n",
       "       Sentiment(classification='pos', p_pos=0.5348651603930167, p_neg=0.46513483960698404),\n",
       "       Sentiment(classification='pos', p_pos=0.8523794812744852, p_neg=0.1476205187255136),\n",
       "       Sentiment(classification='pos', p_pos=0.7856332057986936, p_neg=0.2143667942013051),\n",
       "       Sentiment(classification='neg', p_pos=0.49140465646701753, p_neg=0.5085953435329832),\n",
       "       Sentiment(classification='pos', p_pos=0.9695084642270624, p_neg=0.03049153577293501),\n",
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       "       Sentiment(classification='pos', p_pos=0.9326190588275791, p_neg=0.06738094117241988),\n",
       "       Sentiment(classification='neg', p_pos=0.375, p_neg=0.6249999999999998),\n",
       "       Sentiment(classification='pos', p_pos=0.518750482346257, p_neg=0.48124951765374496),\n",
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       "       Sentiment(classification='pos', p_pos=0.5770058254998016, p_neg=0.42299417450019855),\n",
       "       Sentiment(classification='pos', p_pos=0.9131237632905148, p_neg=0.08687623670948454),\n",
       "       Sentiment(classification='pos', p_pos=0.8391888105239722, p_neg=0.16081118947602818),\n",
       "       Sentiment(classification='pos', p_pos=0.5543969358464074, p_neg=0.4456030641535926),\n",
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       "       Sentiment(classification='pos', p_pos=0.731894531044798, p_neg=0.26810546895520093),\n",
       "       Sentiment(classification='pos', p_pos=0.9956096003405883, p_neg=0.004390399659411418),\n",
       "       Sentiment(classification='neg', p_pos=0.2787081339712916, p_neg=0.7212918660287084),\n",
       "       Sentiment(classification='pos', p_pos=0.8166666666666667, p_neg=0.18333333333333332),\n",
       "       Sentiment(classification='pos', p_pos=0.5644611999885091, p_neg=0.43553880001149087),\n",
       "       Sentiment(classification='pos', p_pos=0.6791636411456611, p_neg=0.32083635885433914),\n",
       "       Sentiment(classification='pos', p_pos=0.568752011892349, p_neg=0.4312479881076508),\n",
       "       Sentiment(classification='pos', p_pos=0.7705049046611409, p_neg=0.22949509533885878),\n",
       "       Sentiment(classification='neg', p_pos=0.3308216516593277, p_neg=0.6691783483406716),\n",
       "       Sentiment(classification='pos', p_pos=0.598716377474053, p_neg=0.40128362252594524),\n",
       "       Sentiment(classification='pos', p_pos=0.7588712009915609, p_neg=0.2411287990084381),\n",
       "       Sentiment(classification='pos', p_pos=0.8583972244027925, p_neg=0.14160277559720663),\n",
       "       Sentiment(classification='neg', p_pos=0.40343476567005887, p_neg=0.5965652343299402),\n",
       "       Sentiment(classification='pos', p_pos=0.6301848792807483, p_neg=0.3698151207192523),\n",
       "       Sentiment(classification='pos', p_pos=0.5671772431716754, p_neg=0.43282275682832483),\n",
       "       Sentiment(classification='pos', p_pos=0.9988425227135787, p_neg=0.0011574772864176056),\n",
       "       Sentiment(classification='neg', p_pos=0.1308702802268272, p_neg=0.869129719773173),\n",
       "       Sentiment(classification='pos', p_pos=0.7714880137691736, p_neg=0.2285119862308258),\n",
       "       Sentiment(classification='pos', p_pos=0.9929764433290298, p_neg=0.007023556670967892),\n",
       "       Sentiment(classification='pos', p_pos=0.6999070919286586, p_neg=0.3000929080713403),\n",
       "       Sentiment(classification='neg', p_pos=0.4934382625723339, p_neg=0.506561737427666),\n",
       "       Sentiment(classification='pos', p_pos=0.6374721478798843, p_neg=0.36252785212011596),\n",
       "       Sentiment(classification='pos', p_pos=0.8851173149565047, p_neg=0.11488268504349619),\n",
       "       Sentiment(classification='pos', p_pos=0.8261571155861812, p_neg=0.17384288441381954),\n",
       "       Sentiment(classification='neg', p_pos=0.43841447119614335, p_neg=0.5615855288038551),\n",
       "       Sentiment(classification='pos', p_pos=0.954870374866071, p_neg=0.045129625133927806),\n",
       "       Sentiment(classification='pos', p_pos=0.8429846195765834, p_neg=0.15701538042341742),\n",
       "       Sentiment(classification='pos', p_pos=0.7492392114758922, p_neg=0.25076078852410855),\n",
       "       Sentiment(classification='pos', p_pos=0.8195357064868609, p_neg=0.18046429351313817),\n",
       "       Sentiment(classification='pos', p_pos=0.8822512445441036, p_neg=0.11774875545589585),\n",
       "       Sentiment(classification='neg', p_pos=0.25049186778128124, p_neg=0.7495081322187193),\n",
       "       Sentiment(classification='pos', p_pos=0.6680737462517837, p_neg=0.33192625374821727),\n",
       "       Sentiment(classification='pos', p_pos=0.6409804627166003, p_neg=0.3590195372833997),\n",
       "       Sentiment(classification='pos', p_pos=0.5021820448877805, p_neg=0.4978179551122194),\n",
       "       Sentiment(classification='pos', p_pos=0.5434284578155276, p_neg=0.45657154218447193),\n",
       "       Sentiment(classification='pos', p_pos=0.533311252192583, p_neg=0.4666887478074166),\n",
       "       Sentiment(classification='pos', p_pos=0.5283802035490972, p_neg=0.47161979645090296),\n",
       "       Sentiment(classification='pos', p_pos=0.782828524524631, p_neg=0.21717147547537022),\n",
       "       Sentiment(classification='pos', p_pos=0.5011518633837386, p_neg=0.4988481366162608),\n",
       "       Sentiment(classification='neg', p_pos=0.38725337051162756, p_neg=0.6127466294883708),\n",
       "       Sentiment(classification='pos', p_pos=0.8033177729769299, p_neg=0.19668222702307087),\n",
       "       Sentiment(classification='pos', p_pos=0.6329309254302645, p_neg=0.36706907456973514),\n",
       "       Sentiment(classification='pos', p_pos=0.6717063079642839, p_neg=0.3282936920357161),\n",
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       "       Sentiment(classification='pos', p_pos=0.9898596908625948, p_neg=0.010140309137407884),\n",
       "       Sentiment(classification='pos', p_pos=0.5506854732401787, p_neg=0.44931452675982075),\n",
       "       Sentiment(classification='pos', p_pos=0.9235521068600006, p_neg=0.07644789313999878),\n",
       "       Sentiment(classification='pos', p_pos=0.8272324424097139, p_neg=0.17276755759028706),\n",
       "       Sentiment(classification='pos', p_pos=0.9774379570671139, p_neg=0.022562042932886908),\n",
       "       Sentiment(classification='pos', p_pos=0.6691455300217476, p_neg=0.3308544699782523),\n",
       "       Sentiment(classification='neg', p_pos=0.11808795894607732, p_neg=0.8819120410539183),\n",
       "       Sentiment(classification='pos', p_pos=0.9588174703619169, p_neg=0.04118252963808362),\n",
       "       Sentiment(classification='neg', p_pos=0.4476447021592812, p_neg=0.5523552978407187),\n",
       "       Sentiment(classification='pos', p_pos=0.5154947983272251, p_neg=0.4845052016727747),\n",
       "       Sentiment(classification='pos', p_pos=0.6829499671531674, p_neg=0.31705003284683253),\n",
       "       Sentiment(classification='pos', p_pos=0.622691891514538, p_neg=0.37730810848546203),\n",
       "       Sentiment(classification='pos', p_pos=0.5405467377538097, p_neg=0.4594532622461904),\n",
       "       Sentiment(classification='pos', p_pos=0.8816296105744904, p_neg=0.11837038942551088),\n",
       "       Sentiment(classification='pos', p_pos=0.5422736188624265, p_neg=0.45772638113757225),\n",
       "       Sentiment(classification='pos', p_pos=0.655670337662117, p_neg=0.3443296623378834),\n",
       "       Sentiment(classification='pos', p_pos=0.9997552816435051, p_neg=0.0002447183564962214),\n",
       "       Sentiment(classification='pos', p_pos=0.7219419387082624, p_neg=0.2780580612917386),\n",
       "       Sentiment(classification='pos', p_pos=0.8530993386749683, p_neg=0.1469006613250312),\n",
       "       Sentiment(classification='pos', p_pos=0.6803197449760572, p_neg=0.3196802550239427),\n",
       "       Sentiment(classification='pos', p_pos=0.9648694406573619, p_neg=0.035130559342639124),\n",
       "       Sentiment(classification='pos', p_pos=0.8776161737651675, p_neg=0.12238382623483192),\n",
       "       Sentiment(classification='neg', p_pos=0.4482678501676925, p_neg=0.5517321498323073),\n",
       "       Sentiment(classification='neg', p_pos=0.4703389830508474, p_neg=0.5296610169491525),\n",
       "       Sentiment(classification='neg', p_pos=0.39552238805970147, p_neg=0.6044776119402985),\n",
       "       Sentiment(classification='pos', p_pos=0.7307692307692308, p_neg=0.2692307692307694),\n",
       "       Sentiment(classification='pos', p_pos=0.596774193548387, p_neg=0.4032258064516129),\n",
       "       Sentiment(classification='pos', p_pos=0.5352941176470589, p_neg=0.46470588235294125),\n",
       "       Sentiment(classification='pos', p_pos=0.6375000000000001, p_neg=0.3625000000000001),\n",
       "       Sentiment(classification='pos', p_pos=0.5490616923047899, p_neg=0.4509383076952105),\n",
       "       Sentiment(classification='pos', p_pos=0.7325463641263864, p_neg=0.2674536358736142),\n",
       "       Sentiment(classification='pos', p_pos=0.7290281641843576, p_neg=0.2709718358156425),\n",
       "       Sentiment(classification='pos', p_pos=0.9239109919006102, p_neg=0.07608900809939088),\n",
       "       Sentiment(classification='pos', p_pos=0.9992080561276117, p_neg=0.0007919438723826113),\n",
       "       Sentiment(classification='pos', p_pos=0.9917985640559835, p_neg=0.008201435944016042),\n",
       "       Sentiment(classification='pos', p_pos=0.7063884160544022, p_neg=0.29361158394559567),\n",
       "       Sentiment(classification='neg', p_pos=0.48365122615803813, p_neg=0.5163487738419619),\n",
       "       Sentiment(classification='pos', p_pos=0.9148004380148581, p_neg=0.08519956198514535),\n",
       "       Sentiment(classification='pos', p_pos=0.9963095753048756, p_neg=0.0036904246951221216),\n",
       "       Sentiment(classification='pos', p_pos=0.8021051399335459, p_neg=0.19789486006645468),\n",
       "       Sentiment(classification='pos', p_pos=0.9696583254426875, p_neg=0.030341674557312872),\n",
       "       Sentiment(classification='pos', p_pos=0.9072736029988177, p_neg=0.09272639700118142),\n",
       "       Sentiment(classification='neg', p_pos=0.47639470570715414, p_neg=0.5236052942928456),\n",
       "       Sentiment(classification='pos', p_pos=0.5239759633638765, p_neg=0.4760240366361239),\n",
       "       Sentiment(classification='pos', p_pos=0.5126417595740697, p_neg=0.48735824042593073),\n",
       "       Sentiment(classification='pos', p_pos=0.917230689337342, p_neg=0.0827693106626587),\n",
       "       Sentiment(classification='pos', p_pos=0.8034645990877027, p_neg=0.1965354009122972),\n",
       "       Sentiment(classification='pos', p_pos=0.855633197921165, p_neg=0.14436680207883498),\n",
       "       Sentiment(classification='pos', p_pos=0.9782390858483174, p_neg=0.02176091415168294),\n",
       "       Sentiment(classification='pos', p_pos=0.8526902636681496, p_neg=0.14730973633184946),\n",
       "       Sentiment(classification='pos', p_pos=0.9246911242796393, p_neg=0.07530887572035957),\n",
       "       Sentiment(classification='pos', p_pos=0.5730243940788201, p_neg=0.4269756059211798),\n",
       "       Sentiment(classification='pos', p_pos=0.7177349426639936, p_neg=0.28226505733600626),\n",
       "       Sentiment(classification='pos', p_pos=0.8798919838719611, p_neg=0.1201080161280364),\n",
       "       Sentiment(classification='pos', p_pos=0.6199491724325221, p_neg=0.3800508275674796),\n",
       "       Sentiment(classification='pos', p_pos=0.9619136880883717, p_neg=0.03808631191162877),\n",
       "       Sentiment(classification='pos', p_pos=0.6555150864568093, p_neg=0.3444849135431911),\n",
       "       Sentiment(classification='pos', p_pos=0.6932483924707775, p_neg=0.3067516075292202),\n",
       "       Sentiment(classification='pos', p_pos=0.5209476551866736, p_neg=0.4790523448133262),\n",
       "       Sentiment(classification='pos', p_pos=0.8815343103169424, p_neg=0.11846568968305697),\n",
       "       Sentiment(classification='pos', p_pos=0.6513250313883056, p_neg=0.3486749686116949),\n",
       "       Sentiment(classification='pos', p_pos=0.6686184082072802, p_neg=0.3313815917927196),\n",
       "       Sentiment(classification='pos', p_pos=0.8887782091913721, p_neg=0.11122179080862672),\n",
       "       Sentiment(classification='pos', p_pos=0.5, p_neg=0.5),\n",
       "       Sentiment(classification='neg', p_pos=0.3716177865396082, p_neg=0.6283822134603915),\n",
       "       Sentiment(classification='pos', p_pos=0.9177208200727573, p_neg=0.0822791799272422),\n",
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       "       Sentiment(classification='neg', p_pos=0.4916666666666667, p_neg=0.5083333333333334),\n",
       "       Sentiment(classification='neg', p_pos=0.4836751437869132, p_neg=0.5163248562130861),\n",
       "       Sentiment(classification='pos', p_pos=0.7954545454545454, p_neg=0.2045454545454549),\n",
       "       Sentiment(classification='pos', p_pos=0.7816521978434018, p_neg=0.21834780215659869),\n",
       "       Sentiment(classification='pos', p_pos=0.7439480907119591, p_neg=0.25605190928804183),\n",
       "       Sentiment(classification='pos', p_pos=0.7298297650570981, p_neg=0.27017023494290093),\n",
       "       Sentiment(classification='pos', p_pos=0.5990668085628233, p_neg=0.40093319143717615),\n",
       "       Sentiment(classification='neg', p_pos=0.49746257747316014, p_neg=0.5025374225268397),\n",
       "       Sentiment(classification='pos', p_pos=0.5405060903547526, p_neg=0.4594939096452473),\n",
       "       Sentiment(classification='pos', p_pos=0.931449129344336, p_neg=0.06855087065566547),\n",
       "       Sentiment(classification='pos', p_pos=0.6748967484740641, p_neg=0.3251032515259362),\n",
       "       Sentiment(classification='pos', p_pos=0.810926981292829, p_neg=0.18907301870717033),\n",
       "       Sentiment(classification='pos', p_pos=0.8037651661085927, p_neg=0.19623483389140722),\n",
       "       Sentiment(classification='pos', p_pos=0.6095171366091667, p_neg=0.3904828633908332),\n",
       "       Sentiment(classification='pos', p_pos=0.5624097078777998, p_neg=0.4375902921221993),\n",
       "       Sentiment(classification='pos', p_pos=0.7315646839852846, p_neg=0.2684353160147147),\n",
       "       Sentiment(classification='pos', p_pos=0.56108026561814, p_neg=0.4389197343818589),\n",
       "       Sentiment(classification='pos', p_pos=0.9952143822724083, p_neg=0.004785617727595365),\n",
       "       Sentiment(classification='pos', p_pos=0.5665666445482774, p_neg=0.43343335545172385),\n",
       "       Sentiment(classification='pos', p_pos=0.6635242293881835, p_neg=0.33647577061181577),\n",
       "       Sentiment(classification='pos', p_pos=0.5767923887189942, p_neg=0.4232076112810062),\n",
       "       Sentiment(classification='pos', p_pos=0.989476894662012, p_neg=0.010523105337986583),\n",
       "       Sentiment(classification='neg', p_pos=0.4301047622718003, p_neg=0.5698952377281996),\n",
       "       Sentiment(classification='pos', p_pos=0.6028776715234788, p_neg=0.39712232847652135),\n",
       "       Sentiment(classification='pos', p_pos=0.791213480306636, p_neg=0.20878651969336334),\n",
       "       Sentiment(classification='pos', p_pos=0.925009250183107, p_neg=0.07499074981689252),\n",
       "       Sentiment(classification='pos', p_pos=0.7212618355676383, p_neg=0.2787381644323616),\n",
       "       Sentiment(classification='neg', p_pos=0.4391462137123509, p_neg=0.5608537862876485),\n",
       "       Sentiment(classification='pos', p_pos=0.655866467143899, p_neg=0.3441335328561012),\n",
       "       Sentiment(classification='pos', p_pos=0.8349084049871056, p_neg=0.1650915950128949),\n",
       "       Sentiment(classification='neg', p_pos=0.42831278360911207, p_neg=0.5716872163908879),\n",
       "       Sentiment(classification='pos', p_pos=0.6829383123102414, p_neg=0.3170616876897584),\n",
       "       Sentiment(classification='pos', p_pos=0.7056118067652278, p_neg=0.29438819323477305),\n",
       "       Sentiment(classification='pos', p_pos=0.6891310794780542, p_neg=0.31086892052194526),\n",
       "       Sentiment(classification='pos', p_pos=0.8942766295707476, p_neg=0.1057233704292528),\n",
       "       Sentiment(classification='pos', p_pos=0.9106995711320485, p_neg=0.08930042886795153),\n",
       "       Sentiment(classification='pos', p_pos=0.6806177085457357, p_neg=0.31938229145426456),\n",
       "       Sentiment(classification='pos', p_pos=0.8441947701549471, p_neg=0.15580522984505155),\n",
       "       Sentiment(classification='pos', p_pos=0.5473378219802667, p_neg=0.4526621780197337),\n",
       "       Sentiment(classification='pos', p_pos=0.9932579499484657, p_neg=0.0067420500515343465),\n",
       "       Sentiment(classification='pos', p_pos=0.913436520367085, p_neg=0.08656347963291679),\n",
       "       Sentiment(classification='pos', p_pos=0.7180178398014966, p_neg=0.28198216019850225),\n",
       "       Sentiment(classification='pos', p_pos=0.7208088983118092, p_neg=0.2791911016881899),\n",
       "       Sentiment(classification='neg', p_pos=0.2999999999999994, p_neg=0.7000000000000001),\n",
       "       Sentiment(classification='pos', p_pos=0.7281317494600437, p_neg=0.2718682505399568),\n",
       "       Sentiment(classification='pos', p_pos=0.8807337188765687, p_neg=0.11926628112343092),\n",
       "       Sentiment(classification='pos', p_pos=0.5592201902593549, p_neg=0.44077980974064507),\n",
       "       Sentiment(classification='pos', p_pos=0.677530464268401, p_neg=0.32246953573159953)],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.NaiveBayes_sentiment.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Biden_sent = df1.sort_values(by='polarity', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>NaiveBayes_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>'93The work and prayers uc0 u8232 of centurie...</td>\n",
       "      <td>(1.0, 0.3)</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.300</td>\n",
       "      <td>(pos, 0.9952143822724083, 0.004785617727595365)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>If we do this then when our days are through o...</td>\n",
       "      <td>(1.0, 0.3)</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.300</td>\n",
       "      <td>(pos, 0.6635242293881835, 0.33647577061181577)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>I promise you this: as the Bible says weeping ...</td>\n",
       "      <td>(0.8, 0.2)</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.200</td>\n",
       "      <td>(pos, 0.9246911242796393, 0.07530887572035957)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Through the Civil War, the Great Depression, W...</td>\n",
       "      <td>(0.8, 0.75)</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.750</td>\n",
       "      <td>(pos, 0.9929764433290298, 0.007023556670967892)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Here we stand looking out to the great Mall wh...</td>\n",
       "      <td>(0.8, 0.75)</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.750</td>\n",
       "      <td>(pos, 0.9235521068600006, 0.07644789313999878)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Uniting to fight the common foes we face:</td>\n",
       "      <td>(-0.3, 0.5)</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.500</td>\n",
       "      <td>(pos, 0.731894531044798, 0.26810546895520093)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Recent weeks and months have taught us a painf...</td>\n",
       "      <td>(-0.35, 0.575)</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.575</td>\n",
       "      <td>(pos, 0.7325463641263864, 0.2674536358736142)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>They healed a broken land. uc0 u8232 My fellow...</td>\n",
       "      <td>(-0.4, 0.4)</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.400</td>\n",
       "      <td>(pos, 0.989476894662012, 0.010523105337986583)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>We have never, ever, ever failed in America wh...</td>\n",
       "      <td>(-0.5, 0.3)</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.300</td>\n",
       "      <td>(pos, 0.6680737462517837, 0.33192625374821727)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Anger, resentment, hatred.</td>\n",
       "      <td>(-0.7, 0.2)</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>0.200</td>\n",
       "      <td>(pos, 0.9956096003405883, 0.004390399659411418)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  text       sentiment  \\\n",
       "184   '93The work and prayers uc0 u8232 of centurie...      (1.0, 0.3)   \n",
       "186  If we do this then when our days are through o...      (1.0, 0.3)   \n",
       "149  I promise you this: as the Bible says weeping ...      (0.8, 0.2)   \n",
       "73   Through the Civil War, the Great Depression, W...     (0.8, 0.75)   \n",
       "101  Here we stand looking out to the great Mall wh...     (0.8, 0.75)   \n",
       "..                                                 ...             ...   \n",
       "55          Uniting to fight the common foes we face:      (-0.3, 0.5)   \n",
       "129  Recent weeks and months have taught us a painf...  (-0.35, 0.575)   \n",
       "188  They healed a broken land. uc0 u8232 My fellow...     (-0.4, 0.4)   \n",
       "86   We have never, ever, ever failed in America wh...     (-0.5, 0.3)   \n",
       "56                         Anger, resentment, hatred.      (-0.7, 0.2)   \n",
       "\n",
       "     polarity  subjectivity                             NaiveBayes_sentiment  \n",
       "184      1.00         0.300  (pos, 0.9952143822724083, 0.004785617727595365)  \n",
       "186      1.00         0.300   (pos, 0.6635242293881835, 0.33647577061181577)  \n",
       "149      0.80         0.200   (pos, 0.9246911242796393, 0.07530887572035957)  \n",
       "73       0.80         0.750  (pos, 0.9929764433290298, 0.007023556670967892)  \n",
       "101      0.80         0.750   (pos, 0.9235521068600006, 0.07644789313999878)  \n",
       "..        ...           ...                                              ...  \n",
       "55      -0.30         0.500    (pos, 0.731894531044798, 0.26810546895520093)  \n",
       "129     -0.35         0.575    (pos, 0.7325463641263864, 0.2674536358736142)  \n",
       "188     -0.40         0.400   (pos, 0.989476894662012, 0.010523105337986583)  \n",
       "86      -0.50         0.300   (pos, 0.6680737462517837, 0.33192625374821727)  \n",
       "56      -0.70         0.200  (pos, 0.9956096003405883, 0.004390399659411418)  \n",
       "\n",
       "[207 rows x 5 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Biden_sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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