NLP Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "143c018b",
   "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>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14635</th>\n",
       "      <td>569587686496825344</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KristenReenders</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir thank you we got on a different f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 12:01:01 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14636</th>\n",
       "      <td>569587371693355008</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Customer Service Issue</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>itsropes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir leaving over 20 minutes Late Flig...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 11:59:46 -0800</td>\n",
       "      <td>Texas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14637</th>\n",
       "      <td>569587242672398336</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sanyabun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir Please bring American Airlines to...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 11:59:15 -0800</td>\n",
       "      <td>Nigeria,lagos</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14638</th>\n",
       "      <td>569587188687634433</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Customer Service Issue</td>\n",
       "      <td>0.6659</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SraJackson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir you have my money, you change my ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 11:59:02 -0800</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14639</th>\n",
       "      <td>569587140490866689</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>daviddtwu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir we have 8 ppl so we need 2 know h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 11:58:51 -0800</td>\n",
       "      <td>dallas, TX</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14640 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0      570306133677760513           neutral                        1.0000   \n",
       "1      570301130888122368          positive                        0.3486   \n",
       "2      570301083672813571           neutral                        0.6837   \n",
       "3      570301031407624196          negative                        1.0000   \n",
       "4      570300817074462722          negative                        1.0000   \n",
       "...                   ...               ...                           ...   \n",
       "14635  569587686496825344          positive                        0.3487   \n",
       "14636  569587371693355008          negative                        1.0000   \n",
       "14637  569587242672398336           neutral                        1.0000   \n",
       "14638  569587188687634433          negative                        1.0000   \n",
       "14639  569587140490866689           neutral                        0.6771   \n",
       "\n",
       "               negativereason  negativereason_confidence         airline  \\\n",
       "0                         NaN                        NaN  Virgin America   \n",
       "1                         NaN                     0.0000  Virgin America   \n",
       "2                         NaN                        NaN  Virgin America   \n",
       "3                  Bad Flight                     0.7033  Virgin America   \n",
       "4                  Can't Tell                     1.0000  Virgin America   \n",
       "...                       ...                        ...             ...   \n",
       "14635                     NaN                     0.0000        American   \n",
       "14636  Customer Service Issue                     1.0000        American   \n",
       "14637                     NaN                        NaN        American   \n",
       "14638  Customer Service Issue                     0.6659        American   \n",
       "14639                     NaN                     0.0000        American   \n",
       "\n",
       "      airline_sentiment_gold             name negativereason_gold  \\\n",
       "0                        NaN          cairdin                 NaN   \n",
       "1                        NaN         jnardino                 NaN   \n",
       "2                        NaN       yvonnalynn                 NaN   \n",
       "3                        NaN         jnardino                 NaN   \n",
       "4                        NaN         jnardino                 NaN   \n",
       "...                      ...              ...                 ...   \n",
       "14635                    NaN  KristenReenders                 NaN   \n",
       "14636                    NaN         itsropes                 NaN   \n",
       "14637                    NaN         sanyabun                 NaN   \n",
       "14638                    NaN       SraJackson                 NaN   \n",
       "14639                    NaN        daviddtwu                 NaN   \n",
       "\n",
       "       retweet_count                                               text  \\\n",
       "0                  0                @VirginAmerica What @dhepburn said.   \n",
       "1                  0  @VirginAmerica plus you've added commercials t...   \n",
       "2                  0  @VirginAmerica I didn't today... Must mean I n...   \n",
       "3                  0  @VirginAmerica it's really aggressive to blast...   \n",
       "4                  0  @VirginAmerica and it's a really big bad thing...   \n",
       "...              ...                                                ...   \n",
       "14635              0  @AmericanAir thank you we got on a different f...   \n",
       "14636              0  @AmericanAir leaving over 20 minutes Late Flig...   \n",
       "14637              0  @AmericanAir Please bring American Airlines to...   \n",
       "14638              0  @AmericanAir you have my money, you change my ...   \n",
       "14639              0  @AmericanAir we have 8 ppl so we need 2 know h...   \n",
       "\n",
       "      tweet_coord              tweet_created tweet_location  \\\n",
       "0             NaN  2015-02-24 11:35:52 -0800            NaN   \n",
       "1             NaN  2015-02-24 11:15:59 -0800            NaN   \n",
       "2             NaN  2015-02-24 11:15:48 -0800      Lets Play   \n",
       "3             NaN  2015-02-24 11:15:36 -0800            NaN   \n",
       "4             NaN  2015-02-24 11:14:45 -0800            NaN   \n",
       "...           ...                        ...            ...   \n",
       "14635         NaN  2015-02-22 12:01:01 -0800            NaN   \n",
       "14636         NaN  2015-02-22 11:59:46 -0800          Texas   \n",
       "14637         NaN  2015-02-22 11:59:15 -0800  Nigeria,lagos   \n",
       "14638         NaN  2015-02-22 11:59:02 -0800     New Jersey   \n",
       "14639         NaN  2015-02-22 11:58:51 -0800     dallas, TX   \n",
       "\n",
       "                    user_timezone  \n",
       "0      Eastern Time (US & Canada)  \n",
       "1      Pacific Time (US & Canada)  \n",
       "2      Central Time (US & Canada)  \n",
       "3      Pacific Time (US & Canada)  \n",
       "4      Pacific Time (US & Canada)  \n",
       "...                           ...  \n",
       "14635                         NaN  \n",
       "14636                         NaN  \n",
       "14637                         NaN  \n",
       "14638  Eastern Time (US & Canada)  \n",
       "14639                         NaN  \n",
       "\n",
       "[14640 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "# Read the dataset into a pandas dataframe\n",
    "data = pd.read_csv(\"Tweets.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e28d7ac",
   "metadata": {},
   "outputs": [
    {
     "ename": "LookupError",
     "evalue": "\n**********************************************************************\n  Resource \u001b[93mstopwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('stopwords')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/stopwords\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\sobanerj/nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLookupError\u001b[0m                               Traceback (most recent call last)",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py:84\u001b[0m, in \u001b[0;36mLazyCorpusLoader.__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 84\u001b[0m     root \u001b[38;5;241m=\u001b[39m \u001b[43mnltk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mzip_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     85\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m:\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\nltk\\data.py:583\u001b[0m, in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    582\u001b[0m resource_not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 583\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m(resource_not_found)\n",
      "\u001b[1;31mLookupError\u001b[0m: \n**********************************************************************\n  Resource \u001b[93mstopwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('stopwords')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/stopwords.zip/stopwords/\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\sobanerj/nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mLookupError\u001b[0m                               Traceback (most recent call last)",
      "Input \u001b[1;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m train_test_split(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m], data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mairline_sentiment\u001b[39m\u001b[38;5;124m'\u001b[39m], test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Preprocess the input text data\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m stop_words \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(\u001b[43mstopwords\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwords\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124menglish\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m      6\u001b[0m ps \u001b[38;5;241m=\u001b[39m PorterStemmer()\n\u001b[0;32m      8\u001b[0m X_train \u001b[38;5;241m=\u001b[39m X_train\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([ps\u001b[38;5;241m.\u001b[39mstem(word) \u001b[38;5;28;01mfor\u001b[39;00m word \u001b[38;5;129;01min\u001b[39;00m x\u001b[38;5;241m.\u001b[39mlower()\u001b[38;5;241m.\u001b[39msplit() \u001b[38;5;28;01mif\u001b[39;00m word \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m stop_words]))\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py:121\u001b[0m, in \u001b[0;36mLazyCorpusLoader.__getattr__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attr \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__bases__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    119\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLazyCorpusLoader object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__bases__\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 121\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__load\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    122\u001b[0m \u001b[38;5;66;03m# This looks circular, but its not, since __load() changes our\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;66;03m# __class__ to something new:\u001b[39;00m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, attr)\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py:86\u001b[0m, in \u001b[0;36mLazyCorpusLoader.__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     84\u001b[0m             root \u001b[38;5;241m=\u001b[39m nltk\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubdir\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mzip_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     85\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m:\n\u001b[1;32m---> 86\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m     88\u001b[0m \u001b[38;5;66;03m# Load the corpus.\u001b[39;00m\n\u001b[0;32m     89\u001b[0m corpus \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__reader_cls(root, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__kwargs)\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\nltk\\corpus\\util.py:81\u001b[0m, in \u001b[0;36mLazyCorpusLoader.__load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     80\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 81\u001b[0m         root \u001b[38;5;241m=\u001b[39m \u001b[43mnltk\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubdir\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     82\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     83\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32m~\\Anaconda3\\lib\\site-packages\\nltk\\data.py:583\u001b[0m, in \u001b[0;36mfind\u001b[1;34m(resource_name, paths)\u001b[0m\n\u001b[0;32m    581\u001b[0m sep \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m*\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m70\u001b[39m\n\u001b[0;32m    582\u001b[0m resource_not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mmsg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 583\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mLookupError\u001b[39;00m(resource_not_found)\n",
      "\u001b[1;31mLookupError\u001b[0m: \n**********************************************************************\n  Resource \u001b[93mstopwords\u001b[0m not found.\n  Please use the NLTK Downloader to obtain the resource:\n\n  \u001b[31m>>> import nltk\n  >>> nltk.download('stopwords')\n  \u001b[0m\n  For more information see: https://www.nltk.org/data.html\n\n  Attempted to load \u001b[93mcorpora/stopwords\u001b[0m\n\n  Searched in:\n    - 'C:\\\\Users\\\\sobanerj/nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\share\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\Anaconda3\\\\lib\\\\nltk_data'\n    - 'C:\\\\Users\\\\sobanerj\\\\AppData\\\\Roaming\\\\nltk_data'\n    - 'C:\\\\nltk_data'\n    - 'D:\\\\nltk_data'\n    - 'E:\\\\nltk_data'\n**********************************************************************\n"
     ]
    }
   ],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(data['text'], data['airline_sentiment'], test_size=0.2, random_state=42)\n",
    "\n",
    "# Preprocess the input text data\n",
    "stop_words = set(stopwords.words(\"english\"))\n",
    "ps = PorterStemmer()\n",
    "\n",
    "X_train = X_train.apply(lambda x: \" \".join([ps.stem(word) for word in x.lower().split() if word not in stop_words]))\n",
    "X_test = X_test.apply(lambda x: \" \".join([ps.stem(word) for word in x.lower().split() if word not in stop_words]))\n",
    "\n",
    "# Vectorize the input text data\n",
    "cv = CountVectorizer()\n",
    "X_train = cv.fit_transform(X_train)\n",
    "X_test = cv.transform(X_test)\n",
    "\n",
    "# Build the classification model\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the testing set\n",
    "y_pred = clf.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy of the model:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57d1ebf0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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