Efficient election results prediction from social media using Navie bayas.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification"
   ]
  },
  {
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   "id": "8f42bd1f",
   "metadata": {},
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     "text": [
      "Requirement already satisfied: torch in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (2.1.0)\n",
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      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from sympy->torch) (1.3.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install torch"
   ]
  },
  {
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   "execution_count": 55,
   "id": "02d1b348",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (3.8.1)\n",
      "Requirement already satisfied: click in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from nltk) (8.0.4)\n",
      "Requirement already satisfied: joblib in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from nltk) (1.2.0)\n",
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      "Requirement already satisfied: colorama in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from click->nltk) (0.4.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade nltk"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e642f98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: textblob in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (0.17.1)\n",
      "Requirement already satisfied: nltk>=3.1 in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from textblob) (3.8.1)\n",
      "Requirement already satisfied: click in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from nltk>=3.1->textblob) (8.0.4)\n",
      "Requirement already satisfied: joblib in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from nltk>=3.1->textblob) (1.2.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from nltk>=3.1->textblob) (2022.7.9)\n",
      "Requirement already satisfied: tqdm in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from nltk>=3.1->textblob) (4.65.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from click->nltk>=3.1->textblob) (0.4.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
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    }
   ],
   "source": [
    "pip install textblob"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2c6dbb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c44cab42",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"IndianElection19TwitterData.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "528c869c",
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       "      <th>0</th>\n",
       "      <td>2019-05-18 23:50:47+00:00</td>\n",
       "      <td>advosushildixit</td>\n",
       "      <td>@anjanaomkashyap I am seeing you as future #bj...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 23:20:00+00:00</td>\n",
       "      <td>airnewsalerts</td>\n",
       "      <td>Trinamool Congress Sitting MP Abhishek Banerje...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 23:00:03+00:00</td>\n",
       "      <td>jiaeur</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 22:53:54+00:00</td>\n",
       "      <td>PVenkatGandhi</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 22:20:48+00:00</td>\n",
       "      <td>TheNirbhay1</td>\n",
       "      <td>PM Modi creates a new record of being the only...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>142561</th>\n",
       "      <td>2019-03-06 10:18:02+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@mayankgandhi04 @mallesh_2004 Early morning #p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142562</th>\n",
       "      <td>2019-03-06 10:11:43+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@DrKumarVishwas Early morning #political move ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142563</th>\n",
       "      <td>2019-01-24 12:57:48+00:00</td>\n",
       "      <td>thota_J</td>\n",
       "      <td>CVoter predicts seat share for upcoming #LokSa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142564</th>\n",
       "      <td>2019-01-24 12:48:37+00:00</td>\n",
       "      <td>thota_J</td>\n",
       "      <td>CVoter predicts seat share for upcoming #LokSa...</td>\n",
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       "      <th>142565</th>\n",
       "      <td>2019-01-24 12:47:55+00:00</td>\n",
       "      <td>thota_J</td>\n",
       "      <td>CVoter predicts seat share for upcoming #LokSa...</td>\n",
       "    </tr>\n",
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       "<p>142566 rows × 3 columns</p>\n",
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       "                             Date             User  \\\n",
       "0       2019-05-18 23:50:47+00:00  advosushildixit   \n",
       "1       2019-05-18 23:20:00+00:00    airnewsalerts   \n",
       "2       2019-05-18 23:00:03+00:00           jiaeur   \n",
       "3       2019-05-18 22:53:54+00:00    PVenkatGandhi   \n",
       "4       2019-05-18 22:20:48+00:00      TheNirbhay1   \n",
       "...                           ...              ...   \n",
       "142561  2019-03-06 10:18:02+00:00       RAMANKAIRA   \n",
       "142562  2019-03-06 10:11:43+00:00       RAMANKAIRA   \n",
       "142563  2019-01-24 12:57:48+00:00          thota_J   \n",
       "142564  2019-01-24 12:48:37+00:00          thota_J   \n",
       "142565  2019-01-24 12:47:55+00:00          thota_J   \n",
       "\n",
       "                                                    Tweet  \n",
       "0       @anjanaomkashyap I am seeing you as future #bj...  \n",
       "1       Trinamool Congress Sitting MP Abhishek Banerje...  \n",
       "2       #LokSabhaElections2019 \\n23rd May 2019 will re...  \n",
       "3       #LokSabhaElections2019 \\n23rd May 2019 will re...  \n",
       "4       PM Modi creates a new record of being the only...  \n",
       "...                                                   ...  \n",
       "142561  @mayankgandhi04 @mallesh_2004 Early morning #p...  \n",
       "142562  @DrKumarVishwas Early morning #political move ...  \n",
       "142563  CVoter predicts seat share for upcoming #LokSa...  \n",
       "142564  CVoter predicts seat share for upcoming #LokSa...  \n",
       "142565  CVoter predicts seat share for upcoming #LokSa...  \n",
       "\n",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 142566 entries, 0 to 142565\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   Date    142566 non-null  object\n",
      " 1   User    142566 non-null  object\n",
      " 2   Tweet   142566 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 4.4+ MB\n"
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   "execution_count": 10,
   "id": "15255465",
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    {
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       "      <th>0</th>\n",
       "      <td>2019-05-18 23:50:47+00:00</td>\n",
       "      <td>advosushildixit</td>\n",
       "      <td>@anjanaomkashyap I am seeing you as future #bj...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 23:00:03+00:00</td>\n",
       "      <td>jiaeur</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 22:53:54+00:00</td>\n",
       "      <td>PVenkatGandhi</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 22:20:48+00:00</td>\n",
       "      <td>TheNirbhay1</td>\n",
       "      <td>PM Modi creates a new record of being the only...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 21:22:29+00:00</td>\n",
       "      <td>ShakeChilli</td>\n",
       "      <td>@abhijitmajumder Appointment of Successor! \\n\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>25678</th>\n",
       "      <td>2019-04-09 12:06:31+00:00</td>\n",
       "      <td>shv_indian</td>\n",
       "      <td>#1DDrive #ModiSpeaksToNews18 #LokSabhaElection...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25679</th>\n",
       "      <td>2019-03-29 15:42:44+00:00</td>\n",
       "      <td>SONUPINKOO</td>\n",
       "      <td>I am seriously worried about future of Indian ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25680</th>\n",
       "      <td>2019-03-29 13:28:51+00:00</td>\n",
       "      <td>SONUPINKOO</td>\n",
       "      <td>If @narendramodi is so Bad then Why does @INCI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25681</th>\n",
       "      <td>2019-03-23 05:06:36+00:00</td>\n",
       "      <td>SONUPINKOO</td>\n",
       "      <td>On this 23rd March (Pakistan Day), I Urge all ...</td>\n",
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       "    <tr>\n",
       "      <th>25682</th>\n",
       "      <td>2019-03-19 15:37:34+00:00</td>\n",
       "      <td>parthajit0101</td>\n",
       "      <td>@timesofindia #ArvindKejriwal, #AAP sirji pls ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25683 rows × 3 columns</p>\n",
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      "text/plain": [
       "                            Date             User  \\\n",
       "0      2019-05-18 23:50:47+00:00  advosushildixit   \n",
       "1      2019-05-18 23:00:03+00:00           jiaeur   \n",
       "2      2019-05-18 22:53:54+00:00    PVenkatGandhi   \n",
       "3      2019-05-18 22:20:48+00:00      TheNirbhay1   \n",
       "4      2019-05-18 21:22:29+00:00      ShakeChilli   \n",
       "...                          ...              ...   \n",
       "25678  2019-04-09 12:06:31+00:00       shv_indian   \n",
       "25679  2019-03-29 15:42:44+00:00       SONUPINKOO   \n",
       "25680  2019-03-29 13:28:51+00:00       SONUPINKOO   \n",
       "25681  2019-03-23 05:06:36+00:00       SONUPINKOO   \n",
       "25682  2019-03-19 15:37:34+00:00    parthajit0101   \n",
       "\n",
       "                                                   Tweet  \n",
       "0      @anjanaomkashyap I am seeing you as future #bj...  \n",
       "1      #LokSabhaElections2019 \\n23rd May 2019 will re...  \n",
       "2      #LokSabhaElections2019 \\n23rd May 2019 will re...  \n",
       "3      PM Modi creates a new record of being the only...  \n",
       "4      @abhijitmajumder Appointment of Successor! \\n\\...  \n",
       "...                                                  ...  \n",
       "25678  #1DDrive #ModiSpeaksToNews18 #LokSabhaElection...  \n",
       "25679  I am seriously worried about future of Indian ...  \n",
       "25680  If @narendramodi is so Bad then Why does @INCI...  \n",
       "25681  On this 23rd March (Pakistan Day), I Urge all ...  \n",
       "25682  @timesofindia #ArvindKejriwal, #AAP sirji pls ...  \n",
       "\n",
       "[25683 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modi_keywords = [\"Modi\", \"PM\", \"modi\", \"#PMModi\", \"modi ji\", \"narendra modi\", \"@narendramodi\", \"#Vote4Modi\"]\n",
    "modi_df = pd.DataFrame(columns=[\"Date\", \"User\", \"Tweet\"])\n",
    "\n",
    "def contains_modi_keywords(tweet):\n",
    "    words = tweet.split()\n",
    "    for keyword in modi_keywords:\n",
    "        if keyword in words:\n",
    "            return True\n",
    "\n",
    "for row in df.values:\n",
    "    if contains_modi_keywords(str(row[2])):\n",
    "        modi_df = pd.concat([modi_df, pd.DataFrame({\"Date\": [row[0]], \"User\": [row[1]], \"Tweet\": [row[2]]})], ignore_index=True)\n",
    "\n",
    "modi_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae3dc3d9",
   "metadata": {},
   "outputs": [
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       "      <td>2019-05-18 19:49:52+00:00</td>\n",
       "      <td>Sunnysweet16</td>\n",
       "      <td>Wonder why no academic or journalist asks INC ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 18:56:52+00:00</td>\n",
       "      <td>drnitinchaube</td>\n",
       "      <td>Congrats for the change #australiavotes2019 an...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 18:54:01+00:00</td>\n",
       "      <td>mrvivek07</td>\n",
       "      <td>Peopel Say “Govt Ne 70 Years Kya kiya”.\\nUnse ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 18:52:03+00:00</td>\n",
       "      <td>JosephPravinP</td>\n",
       "      <td>@ajaymaken @RahulGandhi And as a final touch, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 18:31:10+00:00</td>\n",
       "      <td>VandanaMegastar</td>\n",
       "      <td>#LokSabhaElections2019 Anyone not having mass ...</td>\n",
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       "      <th>...</th>\n",
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       "      <th>14143</th>\n",
       "      <td>2019-03-23 04:53:54+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>@quizzicalguy In this #LokSabhaElections2019, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14144</th>\n",
       "      <td>2019-03-17 08:53:28+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>@AnumaVidisha @RahulGandhi @ArvindKejriwal whe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14145</th>\n",
       "      <td>2019-03-06 10:20:09+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@sherryontopp Early morning #political move af...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14146</th>\n",
       "      <td>2019-03-06 10:18:02+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@mayankgandhi04 @mallesh_2004 Early morning #p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14147</th>\n",
       "      <td>2019-03-06 10:11:43+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@DrKumarVishwas Early morning #political move ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14148 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Date             User  \\\n",
       "0      2019-05-18 19:49:52+00:00     Sunnysweet16   \n",
       "1      2019-05-18 18:56:52+00:00    drnitinchaube   \n",
       "2      2019-05-18 18:54:01+00:00        mrvivek07   \n",
       "3      2019-05-18 18:52:03+00:00    JosephPravinP   \n",
       "4      2019-05-18 18:31:10+00:00  VandanaMegastar   \n",
       "...                          ...              ...   \n",
       "14143  2019-03-23 04:53:54+00:00         SunjayJK   \n",
       "14144  2019-03-17 08:53:28+00:00         SunjayJK   \n",
       "14145  2019-03-06 10:20:09+00:00       RAMANKAIRA   \n",
       "14146  2019-03-06 10:18:02+00:00       RAMANKAIRA   \n",
       "14147  2019-03-06 10:11:43+00:00       RAMANKAIRA   \n",
       "\n",
       "                                                   Tweet  \n",
       "0      Wonder why no academic or journalist asks INC ...  \n",
       "1      Congrats for the change #australiavotes2019 an...  \n",
       "2      Peopel Say “Govt Ne 70 Years Kya kiya”.\\nUnse ...  \n",
       "3      @ajaymaken @RahulGandhi And as a final touch, ...  \n",
       "4      #LokSabhaElections2019 Anyone not having mass ...  \n",
       "...                                                  ...  \n",
       "14143  @quizzicalguy In this #LokSabhaElections2019, ...  \n",
       "14144  @AnumaVidisha @RahulGandhi @ArvindKejriwal whe...  \n",
       "14145  @sherryontopp Early morning #political move af...  \n",
       "14146  @mayankgandhi04 @mallesh_2004 Early morning #p...  \n",
       "14147  @DrKumarVishwas Early morning #political move ...  \n",
       "\n",
       "[14148 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "rahul = [\"rahul\", \"Rahul\", \"RahulGandhi\", \"gandhi\", \"@RahulGandhi\", \"Gandhi\", \"#Vote4Rahul\", \"#Vote4Gandhi\", \"#Vote4RahulGandhi\"]\n",
    "\n",
    "rahul_df = pd.DataFrame(columns=[\"Date\", \"User\", \"Tweet\"])\n",
    "\n",
    "def israhul(tweet):\n",
    "    t = tweet.split()\n",
    "    for i in rahul:\n",
    "        if i in t:\n",
    "            return True\n",
    "\n",
    "for row in df.values:\n",
    "    if israhul(str(row[2])):\n",
    "        rahul_df = pd.concat([rahul_df, pd.DataFrame({\"Date\": [row[0]], \"User\": [row[1]], \"Tweet\": [row[2]]})], ignore_index=True)\n",
    "\n",
    "rahul_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "340cba49",
   "metadata": {},
   "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>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-18 20:05:42+00:00</td>\n",
       "      <td>Kevin95958680</td>\n",
       "      <td>@ArvindKejriwal you actually have the audacity...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 19:33:41+00:00</td>\n",
       "      <td>Jaydeep_Bapat</td>\n",
       "      <td>#KejriwalAssassinationClaim is fake. He's smar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 19:30:30+00:00</td>\n",
       "      <td>Jaydeep_Bapat</td>\n",
       "      <td>#KejriwalAssassinationClaim is as genuine as #...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 19:27:54+00:00</td>\n",
       "      <td>sheikh541</td>\n",
       "      <td>#BJP was renamed as Safroon Party then again i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 17:01:35+00:00</td>\n",
       "      <td>VijayMittal2512</td>\n",
       "      <td>@Ahmed1Hilal Calculation of @ArvindKejriwal 's...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>2019-04-12 16:27:42+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>@VishalDadlani @ECISVEEP 🙈🙉🙊\\n😴😴😴\\n#ElectionCo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>2019-03-17 08:53:28+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>@AnumaVidisha @RahulGandhi @ArvindKejriwal whe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>2019-03-06 10:20:09+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@sherryontopp Early morning #political move af...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>2019-03-06 10:18:02+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@mayankgandhi04 @mallesh_2004 Early morning #p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>2019-03-06 10:11:43+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@DrKumarVishwas Early morning #political move ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1919 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Date             User  \\\n",
       "0     2019-05-18 20:05:42+00:00    Kevin95958680   \n",
       "1     2019-05-18 19:33:41+00:00    Jaydeep_Bapat   \n",
       "2     2019-05-18 19:30:30+00:00    Jaydeep_Bapat   \n",
       "3     2019-05-18 19:27:54+00:00        sheikh541   \n",
       "4     2019-05-18 17:01:35+00:00  VijayMittal2512   \n",
       "...                         ...              ...   \n",
       "1914  2019-04-12 16:27:42+00:00         SunjayJK   \n",
       "1915  2019-03-17 08:53:28+00:00         SunjayJK   \n",
       "1916  2019-03-06 10:20:09+00:00       RAMANKAIRA   \n",
       "1917  2019-03-06 10:18:02+00:00       RAMANKAIRA   \n",
       "1918  2019-03-06 10:11:43+00:00       RAMANKAIRA   \n",
       "\n",
       "                                                  Tweet  \n",
       "0     @ArvindKejriwal you actually have the audacity...  \n",
       "1     #KejriwalAssassinationClaim is fake. He's smar...  \n",
       "2     #KejriwalAssassinationClaim is as genuine as #...  \n",
       "3     #BJP was renamed as Safroon Party then again i...  \n",
       "4     @Ahmed1Hilal Calculation of @ArvindKejriwal 's...  \n",
       "...                                                 ...  \n",
       "1914  @VishalDadlani @ECISVEEP 🙈🙉🙊\\n😴😴😴\\n#ElectionCo...  \n",
       "1915  @AnumaVidisha @RahulGandhi @ArvindKejriwal whe...  \n",
       "1916  @sherryontopp Early morning #political move af...  \n",
       "1917  @mayankgandhi04 @mallesh_2004 Early morning #p...  \n",
       "1918  @DrKumarVishwas Early morning #political move ...  \n",
       "\n",
       "[1919 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "other_party = [\"Mamta Banerjee\", \"@ArvindKejriwal\", \"#KejriwalAssassinationClaim\", \"West Bengal\", \"@IYCTelangana\", \"Telangana\", \"Tamil Nadu\"]\n",
    "\n",
    "partys = pd.DataFrame(columns=[\"Date\", \"User\", \"Tweet\"])\n",
    "\n",
    "def parteses(tweet):\n",
    "    t = tweet.split()\n",
    "    for i in other_party:\n",
    "        if i in t:\n",
    "            return True\n",
    "\n",
    "for row in df.values:\n",
    "    if parteses(str(row[2])):\n",
    "        partys = pd.concat([partys, pd.DataFrame({\"Date\": [row[0]], \"User\": [row[1]], \"Tweet\": [row[2]]})], ignore_index=True)\n",
    "\n",
    "partys\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ca4d5cb",
   "metadata": {},
   "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>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-04-07 14:33:24+00:00</td>\n",
       "      <td>Narendransp</td>\n",
       "      <td>How long time u do politics with hindu Mr Modi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-04-04 15:35:46+00:00</td>\n",
       "      <td>kundra64</td>\n",
       "      <td>#Nota can be converted if #BJP thinks about sm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04-01 19:13:22+00:00</td>\n",
       "      <td>indianancient00</td>\n",
       "      <td>@SunandaBLR @swamiyogeshji @ShefVaidya @Aabhas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-27 07:48:38+00:00</td>\n",
       "      <td>devnc</td>\n",
       "      <td>@dhruv_rathee If this party #BJP comes to powe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-03-06 15:33:43+00:00</td>\n",
       "      <td>parasaran</td>\n",
       "      <td>@Actor_Siddharth As you have turned critical o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-02-18 12:50:18+00:00</td>\n",
       "      <td>mvthaker</td>\n",
       "      <td>#BJP Alliance in Maharashtra is not correct mo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-04-25 08:14:46+00:00</td>\n",
       "      <td>satyadev17</td>\n",
       "      <td>Nota should be replaced by negative voting...i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-04-17 14:39:33+00:00</td>\n",
       "      <td>Dhichkyaaon</td>\n",
       "      <td>Tomorrow's a big day, vote carefully India. No...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-04-16 08:55:13+00:00</td>\n",
       "      <td>shubhamr528</td>\n",
       "      <td>#LokSabhaElections2019 #BanAzamKhan #SayNoToNO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-04-16 03:24:48+00:00</td>\n",
       "      <td>highratedghabru</td>\n",
       "      <td>Do not Go for NOTA.\\n Its a waste of our vote ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-04-11 04:27:35+00:00</td>\n",
       "      <td>bejodjoda</td>\n",
       "      <td>Nota is certainly bad idea and waste of one va...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-03-20 02:43:42+00:00</td>\n",
       "      <td>subodhs_93</td>\n",
       "      <td>@rose_k01 @crazymadddy If u want to change in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-01-09 09:54:54+00:00</td>\n",
       "      <td>Mission_Mach</td>\n",
       "      <td>Dear Nota warriors, remember that having a hun...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Date             User  \\\n",
       "0   2019-04-07 14:33:24+00:00      Narendransp   \n",
       "1   2019-04-04 15:35:46+00:00         kundra64   \n",
       "2   2019-04-01 19:13:22+00:00  indianancient00   \n",
       "3   2019-03-27 07:48:38+00:00            devnc   \n",
       "4   2019-03-06 15:33:43+00:00        parasaran   \n",
       "5   2019-02-18 12:50:18+00:00         mvthaker   \n",
       "6   2019-04-25 08:14:46+00:00       satyadev17   \n",
       "7   2019-04-17 14:39:33+00:00      Dhichkyaaon   \n",
       "8   2019-04-16 08:55:13+00:00      shubhamr528   \n",
       "9   2019-04-16 03:24:48+00:00  highratedghabru   \n",
       "10  2019-04-11 04:27:35+00:00        bejodjoda   \n",
       "11  2019-03-20 02:43:42+00:00       subodhs_93   \n",
       "12  2019-01-09 09:54:54+00:00     Mission_Mach   \n",
       "\n",
       "                                                Tweet  \n",
       "0   How long time u do politics with hindu Mr Modi...  \n",
       "1   #Nota can be converted if #BJP thinks about sm...  \n",
       "2   @SunandaBLR @swamiyogeshji @ShefVaidya @Aabhas...  \n",
       "3   @dhruv_rathee If this party #BJP comes to powe...  \n",
       "4   @Actor_Siddharth As you have turned critical o...  \n",
       "5   #BJP Alliance in Maharashtra is not correct mo...  \n",
       "6   Nota should be replaced by negative voting...i...  \n",
       "7   Tomorrow's a big day, vote carefully India. No...  \n",
       "8   #LokSabhaElections2019 #BanAzamKhan #SayNoToNO...  \n",
       "9   Do not Go for NOTA.\\n Its a waste of our vote ...  \n",
       "10  Nota is certainly bad idea and waste of one va...  \n",
       "11  @rose_k01 @crazymadddy If u want to change in ...  \n",
       "12  Dear Nota warriors, remember that having a hun...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "No_vote = [\"Nota\", \"Dont vote\"]\n",
    "\n",
    "Votes = pd.DataFrame(columns=[\"Date\", \"User\", \"Tweet\"])\n",
    "\n",
    "def Votes_(tweet):\n",
    "    t = tweet.split()\n",
    "    for i in No_vote:\n",
    "        if i in t:\n",
    "            return True\n",
    "\n",
    "for row in df.values:\n",
    "    if Votes_(str(row[2])):\n",
    "        Votes = pd.concat([Votes, pd.DataFrame({\"Date\": [row[0]], \"User\": [row[1]], \"Tweet\": [row[2]]})], ignore_index=True)\n",
    "\n",
    "Votes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3323efae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import text\n",
    "import string\n",
    "stop = text.ENGLISH_STOP_WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8f7d6586",
   "metadata": {},
   "outputs": [],
   "source": [
    "Votes['Tweet'] = Votes['Tweet'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
    "modi_df['Tweet'] = modi_df['Tweet'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
    "rahul_df['Tweet'] = rahul_df['Tweet'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
    "partys['Tweet'] = partys['Tweet'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ce3675e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punctuations(text):\n",
    "    for punctuation in string.punctuation:\n",
    "        text = text.replace(punctuation, '')\n",
    "    return text\n",
    "\n",
    "modi_df['Tweet'] = modi_df['Tweet'].apply(remove_punctuations)\n",
    "rahul_df['Tweet'] = rahul_df['Tweet'].apply(remove_punctuations)\n",
    "Votes['Tweet'] = Votes['Tweet'].apply(remove_punctuations)\n",
    "partys['Tweet'] = partys['Tweet'].apply(remove_punctuations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8784d488",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: vaderSentiment in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (3.3.2)\n",
      "Requirement already satisfied: requests in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from vaderSentiment) (2.31.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (1.26.16)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\niteesh.bv\\anaconda3\\lib\\site-packages (from requests->vaderSentiment) (2023.7.22)\n"
     ]
    }
   ],
   "source": [
    "!pip install vaderSentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7d056acd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "\n",
    "analyzer = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a26404d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_polarity(text):\n",
    "    sentiment = analyzer.polarity_scores(text)\n",
    "    return sentiment['pos'], sentiment['neg'], sentiment['neu']\n",
    "modi_df['pos'], modi_df['neg'], modi_df['neu'] = zip(*modi_df['Tweet'].apply(calculate_polarity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ed685053",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_polarity(text):\n",
    "    sentiment = analyzer.polarity_scores(text)\n",
    "    return sentiment['pos'], sentiment['neg'], sentiment['neu']\n",
    "rahul_df['pos'], rahul_df['neg'], rahul_df['neu'] = zip(*rahul_df['Tweet'].apply(calculate_polarity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d6fb855b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_polarity(text):\n",
    "    sentiment = analyzer.polarity_scores(text)\n",
    "    return sentiment['pos'], sentiment['neg'], sentiment['neu']\n",
    "partys['pos'], partys['neg'], partys['neu'] = zip(*partys['Tweet'].apply(calculate_polarity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a65c1336",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_polarity(text):\n",
    "    sentiment = analyzer.polarity_scores(text)\n",
    "    return sentiment['pos'], sentiment['neg'], sentiment['neu']\n",
    "Votes['pos'], Votes['neg'], Votes['neu'] = zip(*Votes['Tweet'].apply(calculate_polarity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "df94c450",
   "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>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>pos</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>Emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-18 23:50:47+00:00</td>\n",
       "      <td>advosushildixit</td>\n",
       "      <td>anjanaomkashyap I seeing future bjp spokespers...</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.693</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 23:00:03+00:00</td>\n",
       "      <td>jiaeur</td>\n",
       "      <td>LokSabhaElections2019 23rd May 2019 reveal Eve...</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.772</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 22:53:54+00:00</td>\n",
       "      <td>PVenkatGandhi</td>\n",
       "      <td>LokSabhaElections2019 23rd May 2019 reveal Eve...</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.772</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 22:20:48+00:00</td>\n",
       "      <td>TheNirbhay1</td>\n",
       "      <td>PM Modi creates new record PM democratic count...</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.687</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 21:22:29+00:00</td>\n",
       "      <td>ShakeChilli</td>\n",
       "      <td>abhijitmajumder Appointment Successor God forb...</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.667</td>\n",
       "      <td>neutral</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25678</th>\n",
       "      <td>2019-04-09 12:06:31+00:00</td>\n",
       "      <td>shv_indian</td>\n",
       "      <td>1DDrive ModiSpeaksToNews18 LokSabhaElections20...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.864</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25679</th>\n",
       "      <td>2019-03-29 15:42:44+00:00</td>\n",
       "      <td>SONUPINKOO</td>\n",
       "      <td>I seriously worried future Indian democracy na...</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.604</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25680</th>\n",
       "      <td>2019-03-29 13:28:51+00:00</td>\n",
       "      <td>SONUPINKOO</td>\n",
       "      <td>If narendramodi Bad Why does INCIndia needs Ma...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.844</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25681</th>\n",
       "      <td>2019-03-23 05:06:36+00:00</td>\n",
       "      <td>SONUPINKOO</td>\n",
       "      <td>On 23rd March Pakistan Day I Urge Indians Dere...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25682</th>\n",
       "      <td>2019-03-19 15:37:34+00:00</td>\n",
       "      <td>parthajit0101</td>\n",
       "      <td>timesofindia ArvindKejriwal AAP sirji pls deci...</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.650</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25683 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Date             User  \\\n",
       "0      2019-05-18 23:50:47+00:00  advosushildixit   \n",
       "1      2019-05-18 23:00:03+00:00           jiaeur   \n",
       "2      2019-05-18 22:53:54+00:00    PVenkatGandhi   \n",
       "3      2019-05-18 22:20:48+00:00      TheNirbhay1   \n",
       "4      2019-05-18 21:22:29+00:00      ShakeChilli   \n",
       "...                          ...              ...   \n",
       "25678  2019-04-09 12:06:31+00:00       shv_indian   \n",
       "25679  2019-03-29 15:42:44+00:00       SONUPINKOO   \n",
       "25680  2019-03-29 13:28:51+00:00       SONUPINKOO   \n",
       "25681  2019-03-23 05:06:36+00:00       SONUPINKOO   \n",
       "25682  2019-03-19 15:37:34+00:00    parthajit0101   \n",
       "\n",
       "                                                   Tweet    pos    neg    neu  \\\n",
       "0      anjanaomkashyap I seeing future bjp spokespers...  0.307  0.000  0.693   \n",
       "1      LokSabhaElections2019 23rd May 2019 reveal Eve...  0.228  0.000  0.772   \n",
       "2      LokSabhaElections2019 23rd May 2019 reveal Eve...  0.228  0.000  0.772   \n",
       "3      PM Modi creates new record PM democratic count...  0.313  0.000  0.687   \n",
       "4      abhijitmajumder Appointment Successor God forb...  0.221  0.113  0.667   \n",
       "...                                                  ...    ...    ...    ...   \n",
       "25678  1DDrive ModiSpeaksToNews18 LokSabhaElections20...  0.000  0.136  0.864   \n",
       "25679  I seriously worried future Indian democracy na...  0.284  0.112  0.604   \n",
       "25680  If narendramodi Bad Why does INCIndia needs Ma...  0.000  0.156  0.844   \n",
       "25681  On 23rd March Pakistan Day I Urge Indians Dere...  0.000  0.000  1.000   \n",
       "25682  timesofindia ArvindKejriwal AAP sirji pls deci...  0.184  0.166  0.650   \n",
       "\n",
       "       Emotion  \n",
       "0      neutral  \n",
       "1      neutral  \n",
       "2      neutral  \n",
       "3      neutral  \n",
       "4      neutral  \n",
       "...        ...  \n",
       "25678  neutral  \n",
       "25679  neutral  \n",
       "25680  neutral  \n",
       "25681  neutral  \n",
       "25682  neutral  \n",
       "\n",
       "[25683 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion=[]\n",
    "for i in range(0,25683):\n",
    "    emotion.append(max(modi_df['pos'][i],modi_df['neu'][i],modi_df['neg'][i]))\n",
    "modi_df['Emotion']=emotion\n",
    "for i in range(0,25683):\n",
    "    if modi_df['Emotion'][i]==modi_df['pos'][i]:\n",
    "        modi_df['Emotion'][i]='positive'\n",
    "    elif modi_df['Emotion'][i]==modi_df['neg'][i]:\n",
    "        modi_df['Emotion'][i]='negative' \n",
    "    elif modi_df['Emotion'][i]==modi_df['neu'][i]:\n",
    "        modi_df['Emotion'][i]='neutral'\n",
    "modi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "900f0557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion\n",
       "neutral     24965\n",
       "positive      555\n",
       "negative      163\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modi_df['Emotion'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8db31b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>pos</th>\n",
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       "      <th>neu</th>\n",
       "      <th>Emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-18 19:49:52+00:00</td>\n",
       "      <td>Sunnysweet16</td>\n",
       "      <td>Wonder academic journalist asks INC India Rahu...</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.769</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 18:56:52+00:00</td>\n",
       "      <td>drnitinchaube</td>\n",
       "      <td>Congrats change australiavotes2019 ScottMorris...</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.848</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 18:54:01+00:00</td>\n",
       "      <td>mrvivek07</td>\n",
       "      <td>Peopel Say “Govt Ne 70 Years Kya kiya” Unse pu...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 18:52:03+00:00</td>\n",
       "      <td>JosephPravinP</td>\n",
       "      <td>ajaymaken RahulGandhi And final touch Modi ji ...</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.682</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 18:31:10+00:00</td>\n",
       "      <td>VandanaMegastar</td>\n",
       "      <td>LokSabhaElections2019 Anyone having mass backi...</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.785</td>\n",
       "      <td>neutral</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14143</th>\n",
       "      <td>2019-03-23 04:53:54+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>quizzicalguy In LokSabhaElections2019 India Ec...</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.798</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14144</th>\n",
       "      <td>2019-03-17 08:53:28+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>AnumaVidisha RahulGandhi ArvindKejriwal Spokes...</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.721</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14145</th>\n",
       "      <td>2019-03-06 10:20:09+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>sherryontopp Early morning political detailed ...</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.903</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14146</th>\n",
       "      <td>2019-03-06 10:18:02+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>mayankgandhi04 mallesh2004 Early morning polit...</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.906</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14147</th>\n",
       "      <td>2019-03-06 10:11:43+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>DrKumarVishwas Early morning political detaile...</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.903</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14148 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Date             User  \\\n",
       "0      2019-05-18 19:49:52+00:00     Sunnysweet16   \n",
       "1      2019-05-18 18:56:52+00:00    drnitinchaube   \n",
       "2      2019-05-18 18:54:01+00:00        mrvivek07   \n",
       "3      2019-05-18 18:52:03+00:00    JosephPravinP   \n",
       "4      2019-05-18 18:31:10+00:00  VandanaMegastar   \n",
       "...                          ...              ...   \n",
       "14143  2019-03-23 04:53:54+00:00         SunjayJK   \n",
       "14144  2019-03-17 08:53:28+00:00         SunjayJK   \n",
       "14145  2019-03-06 10:20:09+00:00       RAMANKAIRA   \n",
       "14146  2019-03-06 10:18:02+00:00       RAMANKAIRA   \n",
       "14147  2019-03-06 10:11:43+00:00       RAMANKAIRA   \n",
       "\n",
       "                                                   Tweet    pos    neg    neu  \\\n",
       "0      Wonder academic journalist asks INC India Rahu...  0.093  0.138  0.769   \n",
       "1      Congrats change australiavotes2019 ScottMorris...  0.152  0.000  0.848   \n",
       "2      Peopel Say “Govt Ne 70 Years Kya kiya” Unse pu...  0.000  0.000  1.000   \n",
       "3      ajaymaken RahulGandhi And final touch Modi ji ...  0.318  0.000  0.682   \n",
       "4      LokSabhaElections2019 Anyone having mass backi...  0.215  0.000  0.785   \n",
       "...                                                  ...    ...    ...    ...   \n",
       "14143  quizzicalguy In LokSabhaElections2019 India Ec...  0.202  0.000  0.798   \n",
       "14144  AnumaVidisha RahulGandhi ArvindKejriwal Spokes...  0.141  0.138  0.721   \n",
       "14145  sherryontopp Early morning political detailed ...  0.097  0.000  0.903   \n",
       "14146  mayankgandhi04 mallesh2004 Early morning polit...  0.094  0.000  0.906   \n",
       "14147  DrKumarVishwas Early morning political detaile...  0.097  0.000  0.903   \n",
       "\n",
       "       Emotion  \n",
       "0      neutral  \n",
       "1      neutral  \n",
       "2      neutral  \n",
       "3      neutral  \n",
       "4      neutral  \n",
       "...        ...  \n",
       "14143  neutral  \n",
       "14144  neutral  \n",
       "14145  neutral  \n",
       "14146  neutral  \n",
       "14147  neutral  \n",
       "\n",
       "[14148 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion=[]\n",
    "for i in range(0,14148):\n",
    "    emotion.append(max(rahul_df['pos'][i],rahul_df['neu'][i],rahul_df['neg'][i]))\n",
    "rahul_df['Emotion']=emotion\n",
    "for i in range(0,14148):\n",
    "    if rahul_df['Emotion'][i]== rahul_df['pos'][i]:\n",
    "        rahul_df['Emotion'][i]='positive'\n",
    "    elif rahul_df['Emotion'][i]== rahul_df['neg'][i]:\n",
    "        rahul_df['Emotion'][i]='negative' \n",
    "    elif rahul_df['Emotion'][i]== rahul_df['neu'][i]:\n",
    "        rahul_df['Emotion'][i]='neutral'\n",
    "rahul_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dbbc3029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion\n",
       "neutral     13788\n",
       "positive      247\n",
       "negative      113\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rahul_df['Emotion'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "574546d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>pos</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>Emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-18 20:05:42+00:00</td>\n",
       "      <td>Kevin95958680</td>\n",
       "      <td>ArvindKejriwal actually audacity think matter ...</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.800</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 19:33:41+00:00</td>\n",
       "      <td>Jaydeep_Bapat</td>\n",
       "      <td>KejriwalAssassinationClaim fake Hes smart know...</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.414</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 19:30:30+00:00</td>\n",
       "      <td>Jaydeep_Bapat</td>\n",
       "      <td>KejriwalAssassinationClaim genuine kejriwal Iv...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.843</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 19:27:54+00:00</td>\n",
       "      <td>sheikh541</td>\n",
       "      <td>BJP renamed Safroon Party named Modi Party bod...</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.649</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 17:01:35+00:00</td>\n",
       "      <td>VijayMittal2512</td>\n",
       "      <td>Ahmed1Hilal Calculation ArvindKejriwal s Vote ...</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.906</td>\n",
       "      <td>neutral</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>2019-04-12 16:27:42+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>VishalDadlani ECISVEEP 🙈🙉🙊 😴😴😴 ElectionCommiss...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.907</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>2019-03-17 08:53:28+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>AnumaVidisha RahulGandhi ArvindKejriwal Spokes...</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.721</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>2019-03-06 10:20:09+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>sherryontopp Early morning political detailed ...</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.903</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>2019-03-06 10:18:02+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>mayankgandhi04 mallesh2004 Early morning polit...</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.906</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>2019-03-06 10:11:43+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>DrKumarVishwas Early morning political detaile...</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.903</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1919 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Date             User  \\\n",
       "0     2019-05-18 20:05:42+00:00    Kevin95958680   \n",
       "1     2019-05-18 19:33:41+00:00    Jaydeep_Bapat   \n",
       "2     2019-05-18 19:30:30+00:00    Jaydeep_Bapat   \n",
       "3     2019-05-18 19:27:54+00:00        sheikh541   \n",
       "4     2019-05-18 17:01:35+00:00  VijayMittal2512   \n",
       "...                         ...              ...   \n",
       "1914  2019-04-12 16:27:42+00:00         SunjayJK   \n",
       "1915  2019-03-17 08:53:28+00:00         SunjayJK   \n",
       "1916  2019-03-06 10:20:09+00:00       RAMANKAIRA   \n",
       "1917  2019-03-06 10:18:02+00:00       RAMANKAIRA   \n",
       "1918  2019-03-06 10:11:43+00:00       RAMANKAIRA   \n",
       "\n",
       "                                                  Tweet    pos    neg    neu  \\\n",
       "0     ArvindKejriwal actually audacity think matter ...  0.055  0.145  0.800   \n",
       "1     KejriwalAssassinationClaim fake Hes smart know...  0.185  0.401  0.414   \n",
       "2     KejriwalAssassinationClaim genuine kejriwal Iv...  0.000  0.157  0.843   \n",
       "3     BJP renamed Safroon Party named Modi Party bod...  0.351  0.000  0.649   \n",
       "4     Ahmed1Hilal Calculation ArvindKejriwal s Vote ...  0.094  0.000  0.906   \n",
       "...                                                 ...    ...    ...    ...   \n",
       "1914  VishalDadlani ECISVEEP 🙈🙉🙊 😴😴😴 ElectionCommiss...  0.000  0.093  0.907   \n",
       "1915  AnumaVidisha RahulGandhi ArvindKejriwal Spokes...  0.141  0.138  0.721   \n",
       "1916  sherryontopp Early morning political detailed ...  0.097  0.000  0.903   \n",
       "1917  mayankgandhi04 mallesh2004 Early morning polit...  0.094  0.000  0.906   \n",
       "1918  DrKumarVishwas Early morning political detaile...  0.097  0.000  0.903   \n",
       "\n",
       "      Emotion  \n",
       "0     neutral  \n",
       "1     neutral  \n",
       "2     neutral  \n",
       "3     neutral  \n",
       "4     neutral  \n",
       "...       ...  \n",
       "1914  neutral  \n",
       "1915  neutral  \n",
       "1916  neutral  \n",
       "1917  neutral  \n",
       "1918  neutral  \n",
       "\n",
       "[1919 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion=[]\n",
    "for i in range(0,1919):\n",
    "    emotion.append(max(partys['pos'][i],partys['neu'][i],partys['neg'][i]))\n",
    "partys['Emotion']=emotion\n",
    "for i in range(0,1919):\n",
    "    if partys['Emotion'][i]== partys['pos'][i]:\n",
    "        partys['Emotion'][i]='positive'\n",
    "    elif partys['Emotion'][i]== partys['neg'][i]:\n",
    "        partys['Emotion'][i]='negative' \n",
    "    elif partys['Emotion'][i]== partys['neu'][i]:\n",
    "        partys['Emotion'][i]='neutral'\n",
    "partys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0da32db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion\n",
       "neutral     1891\n",
       "positive      15\n",
       "negative      13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partys['Emotion'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "75f8fa67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>pos</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>Emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-04-07 14:33:24+00:00</td>\n",
       "      <td>Narendransp</td>\n",
       "      <td>How long time u politics hindu Mr Modi Plz mak...</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.605</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-04-04 15:35:46+00:00</td>\n",
       "      <td>kundra64</td>\n",
       "      <td>Nota converted BJP thinks small amp middle cla...</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.772</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04-01 19:13:22+00:00</td>\n",
       "      <td>indianancient00</td>\n",
       "      <td>SunandaBLR swamiyogeshji ShefVaidya Aabhas24 T...</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.679</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-27 07:48:38+00:00</td>\n",
       "      <td>devnc</td>\n",
       "      <td>dhruvrathee If party BJP comes power Hitlerish...</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0.670</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-03-06 15:33:43+00:00</td>\n",
       "      <td>parasaran</td>\n",
       "      <td>ActorSiddharth As turned critical BJP Please t...</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.753</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-02-18 12:50:18+00:00</td>\n",
       "      <td>mvthaker</td>\n",
       "      <td>BJP Alliance Maharashtra correct narendramodi ...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-04-25 08:14:46+00:00</td>\n",
       "      <td>satyadev17</td>\n",
       "      <td>Nota replaced negative votingit advantage demo...</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.711</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-04-17 14:39:33+00:00</td>\n",
       "      <td>Dhichkyaaon</td>\n",
       "      <td>Tomorrows big day vote carefully India Nota In...</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.816</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-04-16 08:55:13+00:00</td>\n",
       "      <td>shubhamr528</td>\n",
       "      <td>LokSabhaElections2019 BanAzamKhan SayNoToNOTA ...</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.867</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-04-16 03:24:48+00:00</td>\n",
       "      <td>highratedghabru</td>\n",
       "      <td>Do Go NOTA Its waste vote votebis important No...</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.664</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-04-11 04:27:35+00:00</td>\n",
       "      <td>bejodjoda</td>\n",
       "      <td>Nota certainly bad idea waste valuable vote In...</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.337</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-03-20 02:43:42+00:00</td>\n",
       "      <td>subodhs_93</td>\n",
       "      <td>rosek01 crazymadddy If u want change u hv invo...</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.733</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-01-09 09:54:54+00:00</td>\n",
       "      <td>Mission_Mach</td>\n",
       "      <td>Dear Nota warriors remember having hung assemb...</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.643</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Date             User  \\\n",
       "0   2019-04-07 14:33:24+00:00      Narendransp   \n",
       "1   2019-04-04 15:35:46+00:00         kundra64   \n",
       "2   2019-04-01 19:13:22+00:00  indianancient00   \n",
       "3   2019-03-27 07:48:38+00:00            devnc   \n",
       "4   2019-03-06 15:33:43+00:00        parasaran   \n",
       "5   2019-02-18 12:50:18+00:00         mvthaker   \n",
       "6   2019-04-25 08:14:46+00:00       satyadev17   \n",
       "7   2019-04-17 14:39:33+00:00      Dhichkyaaon   \n",
       "8   2019-04-16 08:55:13+00:00      shubhamr528   \n",
       "9   2019-04-16 03:24:48+00:00  highratedghabru   \n",
       "10  2019-04-11 04:27:35+00:00        bejodjoda   \n",
       "11  2019-03-20 02:43:42+00:00       subodhs_93   \n",
       "12  2019-01-09 09:54:54+00:00     Mission_Mach   \n",
       "\n",
       "                                                Tweet    pos    neg    neu  \\\n",
       "0   How long time u politics hindu Mr Modi Plz mak...  0.345  0.050  0.605   \n",
       "1   Nota converted BJP thinks small amp middle cla...  0.097  0.131  0.772   \n",
       "2   SunandaBLR swamiyogeshji ShefVaidya Aabhas24 T...  0.222  0.100  0.679   \n",
       "3   dhruvrathee If party BJP comes power Hitlerish...  0.129  0.201  0.670   \n",
       "4   ActorSiddharth As turned critical BJP Please t...  0.124  0.124  0.753   \n",
       "5   BJP Alliance Maharashtra correct narendramodi ...  0.000  0.000  1.000   \n",
       "6   Nota replaced negative votingit advantage demo...  0.102  0.188  0.711   \n",
       "7   Tomorrows big day vote carefully India Nota In...  0.184  0.000  0.816   \n",
       "8   LokSabhaElections2019 BanAzamKhan SayNoToNOTA ...  0.133  0.000  0.867   \n",
       "9   Do Go NOTA Its waste vote votebis important No...  0.204  0.133  0.664   \n",
       "10  Nota certainly bad idea waste valuable vote In...  0.309  0.354  0.337   \n",
       "11  rosek01 crazymadddy If u want change u hv invo...  0.194  0.073  0.733   \n",
       "12  Dear Nota warriors remember having hung assemb...  0.152  0.205  0.643   \n",
       "\n",
       "     Emotion  \n",
       "0    neutral  \n",
       "1    neutral  \n",
       "2    neutral  \n",
       "3    neutral  \n",
       "4    neutral  \n",
       "5    neutral  \n",
       "6    neutral  \n",
       "7    neutral  \n",
       "8    neutral  \n",
       "9    neutral  \n",
       "10  negative  \n",
       "11   neutral  \n",
       "12   neutral  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion=[]\n",
    "for i in range(0,13):\n",
    "    emotion.append(max(Votes['pos'][i],Votes['neu'][i],Votes['neg'][i]))\n",
    "Votes['Emotion']=emotion\n",
    "for i in range(0,13):\n",
    "    if Votes['Emotion'][i]== Votes['pos'][i]:\n",
    "        Votes['Emotion'][i]='positive'\n",
    "    elif Votes['Emotion'][i]== Votes['neg'][i]:\n",
    "        Votes['Emotion'][i]='negative' \n",
    "    elif Votes['Emotion'][i]== Votes['neu'][i]:\n",
    "        Votes['Emotion'][i]='neutral'\n",
    "Votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cc77be94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion\n",
       "neutral     12\n",
       "negative     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Votes['Emotion'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "04c48f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "NaMo_ref = [\"Modi\",\"PM\",\"modi\", \"#PMModi\",\"modi ji\", \"narendra modi\", \"@narendramodi\",\"#Vote4Modi\"]\n",
    "RaGa_ref = [\"rahul\", \"Rahul\",\"RahulGandhi\", \"gandhi\",\"@RahulGandhi\",\"Gandhi\",\"#Vote4Rahul\",\"#Vote4Gandhi\",\"#Vote4RahulGandhi\"]\n",
    "vote_no = [\"Nota\",\"Dont vote\"]\n",
    "partyother = [\"Mamta Banerjee\",\"@ArvindKejriwal\",\"#KejriwalAssassinationClaim\",\"West Bengal\",\"@IYCTelangana\",\"Telangana\",\"Tamil Nadu\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d04002c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def identify_subject(tweet, refs):\n",
    "    flag = 0 \n",
    "    for ref in refs:\n",
    "        if tweet.find(ref) != -1:\n",
    "            flag = 1\n",
    "    return flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "606b4960",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['NaModi'] = df['Tweet'].apply(lambda x: identify_subject(x, NaMo_ref))\n",
    "df['RaGandhi'] = df['Tweet'].apply(lambda x: identify_subject(x, RaGa_ref))\n",
    "df['other_party'] = df['Tweet'].apply(lambda x: identify_subject(x, partyother))\n",
    "df['No_vote'] = df['Tweet'].apply(lambda x: identify_subject(x, vote_no))\n",
    "df=df[(df['other_party'] | df['NaModi']==1) | (df['RaGandhi']==1) | df['No_vote']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a7c53d84",
   "metadata": {},
   "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>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>NaModi</th>\n",
       "      <th>RaGandhi</th>\n",
       "      <th>other_party</th>\n",
       "      <th>No_vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-18 23:50:47+00:00</td>\n",
       "      <td>advosushildixit</td>\n",
       "      <td>@anjanaomkashyap I am seeing you as future #bj...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 23:20:00+00:00</td>\n",
       "      <td>airnewsalerts</td>\n",
       "      <td>Trinamool Congress Sitting MP Abhishek Banerje...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 23:00:03+00:00</td>\n",
       "      <td>jiaeur</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 22:53:54+00:00</td>\n",
       "      <td>PVenkatGandhi</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 22:20:48+00:00</td>\n",
       "      <td>TheNirbhay1</td>\n",
       "      <td>PM Modi creates a new record of being the only...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142555</th>\n",
       "      <td>2019-03-19 15:37:34+00:00</td>\n",
       "      <td>parthajit0101</td>\n",
       "      <td>@timesofindia #ArvindKejriwal, #AAP sirji pls ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142556</th>\n",
       "      <td>2019-03-17 08:53:28+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>@AnumaVidisha @RahulGandhi @ArvindKejriwal whe...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142560</th>\n",
       "      <td>2019-03-06 10:20:09+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@sherryontopp Early morning #political move af...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142561</th>\n",
       "      <td>2019-03-06 10:18:02+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@mayankgandhi04 @mallesh_2004 Early morning #p...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142562</th>\n",
       "      <td>2019-03-06 10:11:43+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@DrKumarVishwas Early morning #political move ...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80273 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Date             User  \\\n",
       "0       2019-05-18 23:50:47+00:00  advosushildixit   \n",
       "1       2019-05-18 23:20:00+00:00    airnewsalerts   \n",
       "2       2019-05-18 23:00:03+00:00           jiaeur   \n",
       "3       2019-05-18 22:53:54+00:00    PVenkatGandhi   \n",
       "4       2019-05-18 22:20:48+00:00      TheNirbhay1   \n",
       "...                           ...              ...   \n",
       "142555  2019-03-19 15:37:34+00:00    parthajit0101   \n",
       "142556  2019-03-17 08:53:28+00:00         SunjayJK   \n",
       "142560  2019-03-06 10:20:09+00:00       RAMANKAIRA   \n",
       "142561  2019-03-06 10:18:02+00:00       RAMANKAIRA   \n",
       "142562  2019-03-06 10:11:43+00:00       RAMANKAIRA   \n",
       "\n",
       "                                                    Tweet  NaModi  RaGandhi  \\\n",
       "0       @anjanaomkashyap I am seeing you as future #bj...       1         0   \n",
       "1       Trinamool Congress Sitting MP Abhishek Banerje...       1         0   \n",
       "2       #LokSabhaElections2019 \\n23rd May 2019 will re...       1         0   \n",
       "3       #LokSabhaElections2019 \\n23rd May 2019 will re...       1         0   \n",
       "4       PM Modi creates a new record of being the only...       1         0   \n",
       "...                                                   ...     ...       ...   \n",
       "142555  @timesofindia #ArvindKejriwal, #AAP sirji pls ...       1         0   \n",
       "142556  @AnumaVidisha @RahulGandhi @ArvindKejriwal whe...       1         1   \n",
       "142560  @sherryontopp Early morning #political move af...       0         1   \n",
       "142561  @mayankgandhi04 @mallesh_2004 Early morning #p...       0         1   \n",
       "142562  @DrKumarVishwas Early morning #political move ...       0         1   \n",
       "\n",
       "        other_party  No_vote  \n",
       "0                 0        0  \n",
       "1                 0        0  \n",
       "2                 0        0  \n",
       "3                 0        0  \n",
       "4                 0        0  \n",
       "...             ...      ...  \n",
       "142555            0        0  \n",
       "142556            1        0  \n",
       "142560            1        0  \n",
       "142561            1        0  \n",
       "142562            1        0  \n",
       "\n",
       "[80273 rows x 7 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "94f2657b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "value_counts = df['NaModi'].value_counts()\n",
    "percentage_1 = (value_counts[1] / len(df)) * 100\n",
    "percentage_0 = (value_counts[0] / len(df)) * 100\n",
    "plt.figure(figsize=(8, 6))\n",
    "percentages = [percentage_1, percentage_0]\n",
    "labels = ['Winning (1)', 'Losing (0)']\n",
    "plt.bar(labels, percentages, color=['green', 'red'])\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Percentage of Winning (1) and Losing (0) for NaModi')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7e5040e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "value_counts = df['RaGandhi'].value_counts()\n",
    "percentage_1 = (value_counts[1] / len(df)) * 100\n",
    "percentage_0 = (value_counts[0] / len(df)) * 100\n",
    "plt.figure(figsize=(8, 6))\n",
    "percentages = [percentage_1, percentage_0]\n",
    "labels = ['Winning (1)', 'Losing (0)']\n",
    "plt.bar(labels, percentages, color=['green', 'red'])\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Percentage of Winning (1) and Losing (0) for RaGandhi')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "719e8e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "value_counts = df['other_party'].value_counts()\n",
    "percentage_1 = (value_counts[1] / len(df)) * 100\n",
    "percentage_0 = (value_counts[0] / len(df)) * 100\n",
    "plt.figure(figsize=(8, 6))\n",
    "percentages = [percentage_1, percentage_0]\n",
    "labels = ['Winning (1)', 'Losing (0)']\n",
    "plt.bar(labels, percentages, color=['green', 'blue'])\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Percentage of Winning (1) and Losing (0) for Other Partys')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "401b0ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "value_counts = df['No_vote'].value_counts()\n",
    "percentage_1 = (value_counts[1] / len(df)) * 100\n",
    "percentage_0 = (value_counts[0] / len(df)) * 100\n",
    "plt.figure(figsize=(8, 6))\n",
    "percentages = [percentage_1, percentage_0]\n",
    "labels = ['Winning (1)', 'Losing (0)']\n",
    "plt.bar(labels, percentages, color=['blue', 'yellow'])\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Percentage of Winning (1) and Losing (0) for Nota')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "992ae32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4e6f3733",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package brown to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\brown.zip.\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping taggers\\averaged_perceptron_tagger.zip.\n",
      "[nltk_data] Downloading package conll2000 to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\conll2000.zip.\n",
      "[nltk_data] Downloading package movie_reviews to\n",
      "[nltk_data]     C:\\Users\\Niteesh.bv\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\movie_reviews.zip.\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from textblob import Word, TextBlob\n",
    "nltk.download('omw-1.4')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "stop_words = stopwords.words('english')\n",
    "!python -m textblob.download_corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a003b9d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "ps=PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "05b9aa8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>NaModi</th>\n",
       "      <th>RaGandhi</th>\n",
       "      <th>other_party</th>\n",
       "      <th>No_vote</th>\n",
       "      <th>Processed Tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-18 23:50:47+00:00</td>\n",
       "      <td>advosushildixit</td>\n",
       "      <td>@anjanaomkashyap I am seeing you as future #bj...</td>\n",
       "      <td>1</td>\n",
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       "      <td>@anjanaomkashyap I seeing future #bjp spokespe...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 23:20:00+00:00</td>\n",
       "      <td>airnewsalerts</td>\n",
       "      <td>Trinamool Congress Sitting MP Abhishek Banerje...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Trinamool Congress Sitting MP Abhishek Banerje...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 23:00:03+00:00</td>\n",
       "      <td>jiaeur</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>#LokSabhaElections2019 23rd May 2019 reveal .....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 22:53:54+00:00</td>\n",
       "      <td>PVenkatGandhi</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>#LokSabhaElections2019 23rd May 2019 reveal .....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 22:20:48+00:00</td>\n",
       "      <td>TheNirbhay1</td>\n",
       "      <td>PM Modi creates a new record of being the only...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PM Modi creates new record PM democratic count...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Date             User  \\\n",
       "0  2019-05-18 23:50:47+00:00  advosushildixit   \n",
       "1  2019-05-18 23:20:00+00:00    airnewsalerts   \n",
       "2  2019-05-18 23:00:03+00:00           jiaeur   \n",
       "3  2019-05-18 22:53:54+00:00    PVenkatGandhi   \n",
       "4  2019-05-18 22:20:48+00:00      TheNirbhay1   \n",
       "\n",
       "                                               Tweet  NaModi  RaGandhi  \\\n",
       "0  @anjanaomkashyap I am seeing you as future #bj...       1         0   \n",
       "1  Trinamool Congress Sitting MP Abhishek Banerje...       1         0   \n",
       "2  #LokSabhaElections2019 \\n23rd May 2019 will re...       1         0   \n",
       "3  #LokSabhaElections2019 \\n23rd May 2019 will re...       1         0   \n",
       "4  PM Modi creates a new record of being the only...       1         0   \n",
       "\n",
       "   other_party  No_vote                                    Processed Tweet  \n",
       "0            0        0  @anjanaomkashyap I seeing future #bjp spokespe...  \n",
       "1            0        0  Trinamool Congress Sitting MP Abhishek Banerje...  \n",
       "2            0        0  #LokSabhaElections2019 23rd May 2019 reveal .....  \n",
       "3            0        0  #LokSabhaElections2019 23rd May 2019 reveal .....  \n",
       "4            0        0  PM Modi creates new record PM democratic count...  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def preprocess_tweets(tweet):\n",
    "    processed_tweet = tweet\n",
    "    processed_tweet.replace('[^\\w\\s]', '')\n",
    "    processed_tweet = \" \".join(word for word in processed_tweet.split() if word not in stop_words)\n",
    "    processed_tweet = \" \".join(Word(word).lemmatize() for word in processed_tweet.split())\n",
    "    return(processed_tweet)\n",
    "\n",
    "df['Processed Tweet'] = df['Tweet'].apply(lambda x: preprocess_tweets(x))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8655dc9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "def analyze_sentiment(tweet):\n",
    "    sentiment = analyzer.polarity_scores(tweet)\n",
    "    if sentiment['compound'] >= 0.05:\n",
    "        return 'Positive'\n",
    "    elif sentiment['compound'] <= -0.05:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'\n",
    "df['Sentiment'] = df['Tweet'].apply(analyze_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ae82a6c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>NaModi</th>\n",
       "      <th>RaGandhi</th>\n",
       "      <th>other_party</th>\n",
       "      <th>No_vote</th>\n",
       "      <th>Processed Tweet</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-18 23:50:47+00:00</td>\n",
       "      <td>advosushildixit</td>\n",
       "      <td>@anjanaomkashyap I am seeing you as future #bj...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>@anjanaomkashyap I seeing future #bjp spokespe...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-18 23:20:00+00:00</td>\n",
       "      <td>airnewsalerts</td>\n",
       "      <td>Trinamool Congress Sitting MP Abhishek Banerje...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Trinamool Congress Sitting MP Abhishek Banerje...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-18 23:00:03+00:00</td>\n",
       "      <td>jiaeur</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>#LokSabhaElections2019 23rd May 2019 reveal .....</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-18 22:53:54+00:00</td>\n",
       "      <td>PVenkatGandhi</td>\n",
       "      <td>#LokSabhaElections2019 \\n23rd May 2019 will re...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>#LokSabhaElections2019 23rd May 2019 reveal .....</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-18 22:20:48+00:00</td>\n",
       "      <td>TheNirbhay1</td>\n",
       "      <td>PM Modi creates a new record of being the only...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PM Modi creates new record PM democratic count...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142555</th>\n",
       "      <td>2019-03-19 15:37:34+00:00</td>\n",
       "      <td>parthajit0101</td>\n",
       "      <td>@timesofindia #ArvindKejriwal, #AAP sirji pls ...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>@timesofindia #ArvindKejriwal, #AAP sirji pls ...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142556</th>\n",
       "      <td>2019-03-17 08:53:28+00:00</td>\n",
       "      <td>SunjayJK</td>\n",
       "      <td>@AnumaVidisha @RahulGandhi @ArvindKejriwal whe...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>@AnumaVidisha @RahulGandhi @ArvindKejriwal @Sp...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142560</th>\n",
       "      <td>2019-03-06 10:20:09+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@sherryontopp Early morning #political move af...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>@sherryontopp Early morning #political move de...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142561</th>\n",
       "      <td>2019-03-06 10:18:02+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@mayankgandhi04 @mallesh_2004 Early morning #p...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>@mayankgandhi04 @mallesh_2004 Early morning #p...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142562</th>\n",
       "      <td>2019-03-06 10:11:43+00:00</td>\n",
       "      <td>RAMANKAIRA</td>\n",
       "      <td>@DrKumarVishwas Early morning #political move ...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>@DrKumarVishwas Early morning #political move ...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80273 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Date             User  \\\n",
       "0       2019-05-18 23:50:47+00:00  advosushildixit   \n",
       "1       2019-05-18 23:20:00+00:00    airnewsalerts   \n",
       "2       2019-05-18 23:00:03+00:00           jiaeur   \n",
       "3       2019-05-18 22:53:54+00:00    PVenkatGandhi   \n",
       "4       2019-05-18 22:20:48+00:00      TheNirbhay1   \n",
       "...                           ...              ...   \n",
       "142555  2019-03-19 15:37:34+00:00    parthajit0101   \n",
       "142556  2019-03-17 08:53:28+00:00         SunjayJK   \n",
       "142560  2019-03-06 10:20:09+00:00       RAMANKAIRA   \n",
       "142561  2019-03-06 10:18:02+00:00       RAMANKAIRA   \n",
       "142562  2019-03-06 10:11:43+00:00       RAMANKAIRA   \n",
       "\n",
       "                                                    Tweet  NaModi  RaGandhi  \\\n",
       "0       @anjanaomkashyap I am seeing you as future #bj...       1         0   \n",
       "1       Trinamool Congress Sitting MP Abhishek Banerje...       1         0   \n",
       "2       #LokSabhaElections2019 \\n23rd May 2019 will re...       1         0   \n",
       "3       #LokSabhaElections2019 \\n23rd May 2019 will re...       1         0   \n",
       "4       PM Modi creates a new record of being the only...       1         0   \n",
       "...                                                   ...     ...       ...   \n",
       "142555  @timesofindia #ArvindKejriwal, #AAP sirji pls ...       1         0   \n",
       "142556  @AnumaVidisha @RahulGandhi @ArvindKejriwal whe...       1         1   \n",
       "142560  @sherryontopp Early morning #political move af...       0         1   \n",
       "142561  @mayankgandhi04 @mallesh_2004 Early morning #p...       0         1   \n",
       "142562  @DrKumarVishwas Early morning #political move ...       0         1   \n",
       "\n",
       "        other_party  No_vote  \\\n",
       "0                 0        0   \n",
       "1                 0        0   \n",
       "2                 0        0   \n",
       "3                 0        0   \n",
       "4                 0        0   \n",
       "...             ...      ...   \n",
       "142555            0        0   \n",
       "142556            1        0   \n",
       "142560            1        0   \n",
       "142561            1        0   \n",
       "142562            1        0   \n",
       "\n",
       "                                          Processed Tweet Sentiment  \n",
       "0       @anjanaomkashyap I seeing future #bjp spokespe...  Positive  \n",
       "1       Trinamool Congress Sitting MP Abhishek Banerje...   Neutral  \n",
       "2       #LokSabhaElections2019 23rd May 2019 reveal .....  Negative  \n",
       "3       #LokSabhaElections2019 23rd May 2019 reveal .....  Negative  \n",
       "4       PM Modi creates new record PM democratic count...  Positive  \n",
       "...                                                   ...       ...  \n",
       "142555  @timesofindia #ArvindKejriwal, #AAP sirji pls ...  Negative  \n",
       "142556  @AnumaVidisha @RahulGandhi @ArvindKejriwal @Sp...  Positive  \n",
       "142560  @sherryontopp Early morning #political move de...  Positive  \n",
       "142561  @mayankgandhi04 @mallesh_2004 Early morning #p...  Positive  \n",
       "142562  @DrKumarVishwas Early morning #political move ...  Positive  \n",
       "\n",
       "[80273 rows x 9 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "44076e3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X = df[[\"NaModi\", \"RaGandhi\", \"other_party\", \"No_vote\"]]\n",
    "y = df[\"Sentiment\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3b43dafa",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "y = label_encoder.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "81cacfb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "0d99d0b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#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>GaussianNB()</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\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GaussianNB()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "11dfdb77",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ffa1399d",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "report = classification_report(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6feab0b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 49.89722827779508\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ac941b4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusuion matrix of Navie Bayas\n",
      " [[   8  216 4657]\n",
      " [   7  182 2811]\n",
      " [   8  345 7821]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print('Confusuion matrix of Navie Bayas\\n',confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "05f64784",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.35      0.00      0.00      4881\n",
      "           1       0.24      0.06      0.10      3000\n",
      "           2       0.51      0.96      0.67      8174\n",
      "\n",
      "    accuracy                           0.50     16055\n",
      "   macro avg       0.37      0.34      0.26     16055\n",
      "weighted avg       0.41      0.50      0.36     16055\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report:\\n\", report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fc383c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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