Sentiment_Analysis_on_Twitter_dataset.ipynb

{
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   "metadata": {
    "id": "X1PgjV5IKiVY"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "outputId": "ca18aab9-6599-4e45-833d-df324f426a19"
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    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserName</th>\n",
       "      <th>ScreenName</th>\n",
       "      <th>Location</th>\n",
       "      <th>TweetAt</th>\n",
       "      <th>OriginalTweet</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3799</td>\n",
       "      <td>48751</td>\n",
       "      <td>London</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>@MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3800</td>\n",
       "      <td>48752</td>\n",
       "      <td>UK</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>advice Talk to your neighbours family to excha...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3801</td>\n",
       "      <td>48753</td>\n",
       "      <td>Vagabonds</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Coronavirus Australia: Woolworths to give elde...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3802</td>\n",
       "      <td>48754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>My food stock is not the only one which is emp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3803</td>\n",
       "      <td>48755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Me, ready to go at supermarket during the #COV...</td>\n",
       "      <td>Extremely Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UserName  ScreenName   Location     TweetAt  \\\n",
       "0      3799       48751     London  16-03-2020   \n",
       "1      3800       48752         UK  16-03-2020   \n",
       "2      3801       48753  Vagabonds  16-03-2020   \n",
       "3      3802       48754        NaN  16-03-2020   \n",
       "4      3803       48755        NaN  16-03-2020   \n",
       "\n",
       "                                       OriginalTweet           Sentiment  \n",
       "0  @MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...             Neutral  \n",
       "1  advice Talk to your neighbours family to excha...            Positive  \n",
       "2  Coronavirus Australia: Woolworths to give elde...            Positive  \n",
       "3  My food stock is not the only one which is emp...            Positive  \n",
       "4  Me, ready to go at supermarket during the #COV...  Extremely Negative  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# opening the file\n",
    "df = pd.read_csv('Corona_NLP.csv', encoding='latin1')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oHZHDI1BKnwa",
    "outputId": "38004d21-fd7c-4b74-8a28-976874dcd50c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserName            0\n",
       "ScreenName          0\n",
       "Location         8590\n",
       "TweetAt             0\n",
       "OriginalTweet       0\n",
       "Sentiment           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "_ADrKYgvKnyq",
    "outputId": "cf110766-d1be-41e5-ef1d-3d5e917b499e"
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>UserName</th>\n",
       "      <th>ScreenName</th>\n",
       "      <th>Location</th>\n",
       "      <th>TweetAt</th>\n",
       "      <th>OriginalTweet</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3799</td>\n",
       "      <td>48751</td>\n",
       "      <td>London</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>@MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3800</td>\n",
       "      <td>48752</td>\n",
       "      <td>UK</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>advice Talk to your neighbours family to excha...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3801</td>\n",
       "      <td>48753</td>\n",
       "      <td>Vagabonds</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Coronavirus Australia: Woolworths to give elde...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3804</td>\n",
       "      <td>48756</td>\n",
       "      <td>ÃT: 36.319708,-82.363649</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>As news of the regionÂs first confirmed COVID...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3805</td>\n",
       "      <td>48757</td>\n",
       "      <td>35.926541,-78.753267</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Cashier at grocery store was sharing his insig...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>41147</th>\n",
       "      <td>44946</td>\n",
       "      <td>89898</td>\n",
       "      <td>Brooklyn, NY</td>\n",
       "      <td>14-04-2020</td>\n",
       "      <td>YÂall really shitting that much more at home?...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41149</th>\n",
       "      <td>44948</td>\n",
       "      <td>89900</td>\n",
       "      <td>Toronto, Ontario</td>\n",
       "      <td>14-04-2020</td>\n",
       "      <td>Still shocked by the number of #Toronto superm...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41150</th>\n",
       "      <td>44949</td>\n",
       "      <td>89901</td>\n",
       "      <td>OHIO</td>\n",
       "      <td>14-04-2020</td>\n",
       "      <td>I never that weÂd be in a situation &amp;amp; wor...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41152</th>\n",
       "      <td>44951</td>\n",
       "      <td>89903</td>\n",
       "      <td>Wellington City, New Zealand</td>\n",
       "      <td>14-04-2020</td>\n",
       "      <td>Airline pilots offering to stock supermarket s...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41156</th>\n",
       "      <td>44955</td>\n",
       "      <td>89907</td>\n",
       "      <td>i love you so much || he/him</td>\n",
       "      <td>14-04-2020</td>\n",
       "      <td>@TartiiCat Well new/used Rift S are going for ...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32567 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       UserName  ScreenName                      Location     TweetAt  \\\n",
       "0          3799       48751                        London  16-03-2020   \n",
       "1          3800       48752                            UK  16-03-2020   \n",
       "2          3801       48753                     Vagabonds  16-03-2020   \n",
       "5          3804       48756     ÃT: 36.319708,-82.363649  16-03-2020   \n",
       "6          3805       48757          35.926541,-78.753267  16-03-2020   \n",
       "...         ...         ...                           ...         ...   \n",
       "41147     44946       89898                  Brooklyn, NY  14-04-2020   \n",
       "41149     44948       89900              Toronto, Ontario  14-04-2020   \n",
       "41150     44949       89901                          OHIO  14-04-2020   \n",
       "41152     44951       89903  Wellington City, New Zealand  14-04-2020   \n",
       "41156     44955       89907  i love you so much || he/him  14-04-2020   \n",
       "\n",
       "                                           OriginalTweet Sentiment  \n",
       "0      @MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...   Neutral  \n",
       "1      advice Talk to your neighbours family to excha...  Positive  \n",
       "2      Coronavirus Australia: Woolworths to give elde...  Positive  \n",
       "5      As news of the regionÂs first confirmed COVID...  Positive  \n",
       "6      Cashier at grocery store was sharing his insig...  Positive  \n",
       "...                                                  ...       ...  \n",
       "41147  YÂall really shitting that much more at home?...  Negative  \n",
       "41149  Still shocked by the number of #Toronto superm...  Negative  \n",
       "41150  I never that weÂd be in a situation &amp; wor...  Positive  \n",
       "41152  Airline pilots offering to stock supermarket s...   Neutral  \n",
       "41156  @TartiiCat Well new/used Rift S are going for ...  Negative  \n",
       "\n",
       "[32567 rows x 6 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing the null from the data\n",
    "df1 = df.dropna()\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OX84kipfKn0u",
    "outputId": "21f732eb-61f5-40cd-893b-db7f5353a655"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserName         0\n",
       "ScreenName       0\n",
       "Location         0\n",
       "TweetAt          0\n",
       "OriginalTweet    0\n",
       "Sentiment        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# null values removed\n",
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "zMNOoM7yKn2g"
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZxgZCddOKn4n",
    "outputId": "f4a445d4-7347-4e4d-b002-b86e1ed9098a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\motap\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "QcRBkXDGKn7_"
   },
   "outputs": [],
   "source": [
    "sw = stopwords.words('english')\n",
    "lm = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QdDU9MiQK_CA",
    "outputId": "2a1f00f8-5381-4c82-8526-2cf0344c8a2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment\n",
       "Positive              9110\n",
       "Negative              7763\n",
       "Neutral               6172\n",
       "Extremely Positive    5273\n",
       "Extremely Negative    4249\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gSYIfSGTLSPZ",
    "outputId": "14d4ae67-b2f3-4052-d89d-01c362015eeb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\motap\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EfDhHgmAK_EO",
    "outputId": "fb35bee2-3269-45f6-cfa6-a429bdfe7c90"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\motap\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WGl02JhAK_Gk",
    "outputId": "8af7ea29-9ed7-4127-ad7f-93a61bb4d390"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\motap\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('omw-1.4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ot_RxQ_SK_JK"
   },
   "source": [
    "### preprocessing the covid tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "htUREC-9K_La"
   },
   "outputs": [],
   "source": [
    "msg = []\n",
    "for i in df1['OriginalTweet']:\n",
    "    t = re.sub('[^A-Za-z0-9]',' ',i)  # removal of punctuations\n",
    "    t = t.lower()                     # conversion of words into lower case\n",
    "    t = word_tokenize(t)              # tokenization of words\n",
    "    t= [i for i in t if i not in sw]  # removing the stop words\n",
    "    t = [lm.lemmatize(i) for i in t]  # lemmatization of words\n",
    "    t = \" \".join(t)                   # joining the sentences\n",
    "    msg.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2jUojA9XLY7J",
    "outputId": "ae96e261-9b4b-491b-b890-da86b8a982e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['menyrbie phil gahan chrisitv http co ifz9fan2pa http co xx6ghgfzcc http co i2nlzdxno8', 'advice talk neighbour family exchange phone number create contact list phone number neighbour school employer chemist gp set online shopping account po adequate supply regular med order', 'coronavirus australia woolworth give elderly disabled dedicated shopping hour amid covid 19 outbreak http co binca9vp8p', 'news region first confirmed covid 19 case came sullivan county last week people flocked area store purchase cleaning supply hand sanitizer food toilet paper good tim dodson report http co cfxch7a2lu', 'cashier grocery store sharing insight covid 19 prove credibility commented civics class know talking http co iefdnehgdo', 'supermarket today buy toilet paper rebel toiletpapercrisis covid 19 http co evxkqlidaz', 'due covid 19 retail store classroom atlanta open walk business class next two week beginning monday march 16 continue process online phone order normal thank understanding http co kw91zj5o5i', 'corona prevention stop buy thing cash use online payment method corona spread note also prefer online shopping home time fight covid 19 govindia indiafightscorona', 'month crowding supermarket restaurant however reducing hour closing mall mean everyone using entrance dependent single supermarket manila lockdown covid2019 philippine http co hxws9lanf9', 'due covid 19 situation increased demand food product wait time may longer online order particularly beef share freezer pack thank patience time']\n"
     ]
    }
   ],
   "source": [
    "print(msg[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V9029gGVLY9t"
   },
   "source": [
    "### Transform the words into vectors using Count Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "u_J5J1wXLZAa"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "uV5TF_XZLZD8"
   },
   "outputs": [],
   "source": [
    "cv = CountVectorizer(max_features=2000)\n",
    "sm = cv.fit_transform(msg).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "txsuTs-rK_Oy",
    "outputId": "e2d7e853-89d7-4cea-aca8-0349acd70d6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n"
     ]
    }
   ],
   "source": [
    "print(len(cv.get_feature_names_out()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "31VxI3KrLkiX",
    "outputId": "efd57fbd-d920-4fc9-e070-8a2e66ebd1f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32567, 2000)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TsXGs4pGLk4v",
    "outputId": "48edb582-d5f0-424c-b6d5-80d8b0efb213"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "2000\n"
     ]
    }
   ],
   "source": [
    "print(len(sm[0]))\n",
    "print(len(sm[87]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nsOIz7RkLk7N"
   },
   "source": [
    "### Converting the 'Extremely Positive' and 'Extremely Negative' Sentiments to 'Positive' and 'Negative' sentiments respectively\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "6P8E6Y2RLk9N"
   },
   "outputs": [],
   "source": [
    "df2 = df1.replace(to_replace =\"Extremely Positive\",\n",
    "                 value =\"Positive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "id": "eSe9K6_jLk_w"
   },
   "outputs": [],
   "source": [
    "df3 =df2.replace(to_replace =\"Extremely Negative\",\n",
    "                 value =\"Negative\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "nfTjaulbLlCB",
    "outputId": "1aadd4e5-9ccf-4458-a5b7-3e51f59a3ad8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>UserName</th>\n",
       "      <th>ScreenName</th>\n",
       "      <th>Location</th>\n",
       "      <th>TweetAt</th>\n",
       "      <th>OriginalTweet</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3799</td>\n",
       "      <td>48751</td>\n",
       "      <td>London</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>@MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3800</td>\n",
       "      <td>48752</td>\n",
       "      <td>UK</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>advice Talk to your neighbours family to excha...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3801</td>\n",
       "      <td>48753</td>\n",
       "      <td>Vagabonds</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Coronavirus Australia: Woolworths to give elde...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3804</td>\n",
       "      <td>48756</td>\n",
       "      <td>ÃT: 36.319708,-82.363649</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>As news of the regionÂs first confirmed COVID...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3805</td>\n",
       "      <td>48757</td>\n",
       "      <td>35.926541,-78.753267</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Cashier at grocery store was sharing his insig...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3806</td>\n",
       "      <td>48758</td>\n",
       "      <td>Austria</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Was at the supermarket today. Didn't buy toile...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3807</td>\n",
       "      <td>48759</td>\n",
       "      <td>Atlanta, GA USA</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Due to COVID-19 our retail store and classroom...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3808</td>\n",
       "      <td>48760</td>\n",
       "      <td>BHAVNAGAR,GUJRAT</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>For corona prevention,we should stop to buy th...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3809</td>\n",
       "      <td>48761</td>\n",
       "      <td>Makati, Manila</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>All month there hasn't been crowding in the su...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3810</td>\n",
       "      <td>48762</td>\n",
       "      <td>Pitt Meadows, BC, Canada</td>\n",
       "      <td>16-03-2020</td>\n",
       "      <td>Due to the Covid-19 situation, we have increas...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UserName  ScreenName                   Location     TweetAt  \\\n",
       "0       3799       48751                     London  16-03-2020   \n",
       "1       3800       48752                         UK  16-03-2020   \n",
       "2       3801       48753                  Vagabonds  16-03-2020   \n",
       "5       3804       48756  ÃT: 36.319708,-82.363649  16-03-2020   \n",
       "6       3805       48757       35.926541,-78.753267  16-03-2020   \n",
       "7       3806       48758                    Austria  16-03-2020   \n",
       "8       3807       48759            Atlanta, GA USA  16-03-2020   \n",
       "9       3808       48760           BHAVNAGAR,GUJRAT  16-03-2020   \n",
       "10      3809       48761             Makati, Manila  16-03-2020   \n",
       "11      3810       48762  Pitt Meadows, BC, Canada   16-03-2020   \n",
       "\n",
       "                                        OriginalTweet Sentiment  \n",
       "0   @MeNyrbie @Phil_Gahan @Chrisitv https://t.co/i...   Neutral  \n",
       "1   advice Talk to your neighbours family to excha...  Positive  \n",
       "2   Coronavirus Australia: Woolworths to give elde...  Positive  \n",
       "5   As news of the regionÂs first confirmed COVID...  Positive  \n",
       "6   Cashier at grocery store was sharing his insig...  Positive  \n",
       "7   Was at the supermarket today. Didn't buy toile...   Neutral  \n",
       "8   Due to COVID-19 our retail store and classroom...  Positive  \n",
       "9   For corona prevention,we should stop to buy th...  Negative  \n",
       "10  All month there hasn't been crowding in the su...   Neutral  \n",
       "11  Due to the Covid-19 situation, we have increas...  Positive  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-IiJ1FZ_LlFa",
    "outputId": "7af858bb-11ac-4056-ef8e-e1b982013cbf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment\n",
       "Positive    14383\n",
       "Negative    12012\n",
       "Neutral      6172\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0reSkOqWsL7l"
   },
   "source": [
    "### converting the positive ,negative and neutral text into binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "d3uGt6prL6E7"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "3rN9oXxnL6HR"
   },
   "outputs": [],
   "source": [
    "lb = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "Khts6hCrL6Jh"
   },
   "outputs": [],
   "source": [
    "df3['Sentiment'] = lb.fit_transform(df3['Sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b8V-rlKiL6La",
    "outputId": "85eeceb0-1c8b-4046-d424-f7b17724db07"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment\n",
       "2    14383\n",
       "0    12012\n",
       "1     6172\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "UqiHszE3skKG"
   },
   "outputs": [],
   "source": [
    "# denoting postive tweets with - 2\n",
    "# denoting negative tweets with - 0\n",
    "# denoting neutral tweets with - 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7NflDEfkL6Nz",
    "outputId": "526dcbe5-5311-439d-e19e-0dd4d8f0e4ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "x = sm\n",
    "y = df3['Sentiment']\n",
    "print(type(x))\n",
    "print(type(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AW9lB7SBtDmh"
   },
   "source": [
    "### splitting the 20% of data for test data and 80% for training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "uupaVwP0L6RH"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vrLNb-QeMKDQ",
    "outputId": "78272bfc-cd07-441c-f63e-89309681aecb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26053, 2000)\n",
      "(6514, 2000)\n",
      "(26053,)\n",
      "(6514,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GP5PM_rGtLaJ"
   },
   "source": [
    "### now using Multinomial Naïve Bayes Classification and predicting the sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "nVmpWFaRMKFm"
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "Jy1RJaedMKIH"
   },
   "outputs": [],
   "source": [
    "m1 = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1HjTGxjLMKKS",
    "outputId": "f9b573c1-6ef5-4d3a-9b2d-da8b27c6a4d4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9IAsqzioMKNz",
    "outputId": "25ad3a04-52b8-4702-e730-98ec0a08795a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score 0.7096303688634706\n",
      "testing score 0.6799201719373656\n"
     ]
    }
   ],
   "source": [
    "print(\"training score\",m1.score(x_train,y_train))\n",
    "print(\"testing score\",m1.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9TOXmA22MbM0",
    "outputId": "f3e69463-10a2-42c5-aa43-101b17f7dda1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted sentiment [1 2 2 ... 2 2 0]\n"
     ]
    }
   ],
   "source": [
    "ypred_m1 = m1.predict(x_test)\n",
    "print(\"The predicted sentiment\", ypred_m1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EYxbtsiltpXN"
   },
   "source": [
    "### Computing Confusion matrix and classification report for Multinomial Naïve Bayes Classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "iz6x2V9hMbPK"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y07783LeMbRt",
    "outputId": "f842df9d-8957-406f-8471-8ab14f8006e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1703,  247,  521],\n",
       "       [ 282,  739,  301],\n",
       "       [ 454,  280, 1987]], dtype=int64)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(ypred_m1,y_test)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "77pdvFRtMbVQ",
    "outputId": "e530b8b5-036f-46eb-9967-dc2db7e9435b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.70      0.69      2439\n",
      "           1       0.56      0.58      0.57      1266\n",
      "           2       0.73      0.71      0.72      2809\n",
      "\n",
      "    accuracy                           0.68      6514\n",
      "   macro avg       0.66      0.66      0.66      6514\n",
      "weighted avg       0.68      0.68      0.68      6514\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,ypred_m1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "plgFlF5RuQe7"
   },
   "source": [
    "### now using logistic regression and predicting the sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "WEPz0fLtO9In"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "id": "3MEJiEb9O9KM"
   },
   "outputs": [],
   "source": [
    "m3 = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TQVM6oK5O9QC",
    "outputId": "42b00e56-8996-4367-bfad-e34f02d9dce7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted sentiment [1 2 2 ... 2 2 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\motap\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# prediction\n",
    "m3.fit(x_train,y_train) # ml model will be trained on training data\n",
    "ypred_m3 = m3.predict(x_test) # ml model trained on training data is used to generate the predictions on the test data\n",
    "print(\"The predicted sentiment\",ypred_m3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5WmGBH5DUg2F",
    "outputId": "a7d94f1a-2100-4e40-fa51-465c6922e738"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score 0.8354508118067018\n",
      "testing score 0.7789376727049432\n"
     ]
    }
   ],
   "source": [
    "#  checking  accuracy\n",
    "print(\"training score\",m3.score(x_train,y_train))\n",
    "print(\"testing score\",m3.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "id": "DayJPoHwua_p"
   },
   "outputs": [],
   "source": [
    "# Computing Confusion matrix and classification report for logisticregression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ir5Pj7JbVirM",
    "outputId": "ca8400e7-ec50-4b68-f43d-885c147b9c59"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1894,  174,  337],\n",
       "       [ 223,  914,  206],\n",
       "       [ 322,  178, 2266]], dtype=int64)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm2 = confusion_matrix(ypred_m3,y_test)\n",
    "cm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7I66bAndVitV",
    "outputId": "8f6933c6-15a9-4ff2-f386-f0fb64fbc3ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.78      0.78      2439\n",
      "           1       0.68      0.72      0.70      1266\n",
      "           2       0.82      0.81      0.81      2809\n",
      "\n",
      "    accuracy                           0.78      6514\n",
      "   macro avg       0.76      0.77      0.77      6514\n",
      "weighted avg       0.78      0.78      0.78      6514\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,ypred_m3))"
   ]
  }
 ],
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