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Twitter_sentiment_Analysis.ipynb

@@ -0,0 +1,1190 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import re\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem.porter import PorterStemmer\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\KIIT\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "nltk.download('stopwords')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stopwords.words('english'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv(\"training.1600000.processed.noemoticon.csv\" , encoding= 'ISO-8859-1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>0</th>\n",
+       "      <th>1467810369</th>\n",
+       "      <th>Mon Apr 06 22:19:45 PDT 2009</th>\n",
+       "      <th>NO_QUERY</th>\n",
+       "      <th>_TheSpecialOne_</th>\n",
+       "      <th>@switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810672</td>\n",
+       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>scotthamilton</td>\n",
+       "      <td>is upset that he can't update his Facebook by ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810917</td>\n",
+       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>mattycus</td>\n",
+       "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811184</td>\n",
+       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>ElleCTF</td>\n",
+       "      <td>my whole body feels itchy and like its on fire</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811193</td>\n",
+       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>Karoli</td>\n",
+       "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811372</td>\n",
+       "      <td>Mon Apr 06 22:20:00 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>joy_wolf</td>\n",
+       "      <td>@Kwesidei not the whole crew</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY _TheSpecialOne_  \\\n",
+       "0  0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   scotthamilton   \n",
+       "1  0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY        mattycus   \n",
+       "2  0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY         ElleCTF   \n",
+       "3  0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY          Karoli   \n",
+       "4  0  1467811372  Mon Apr 06 22:20:00 PDT 2009  NO_QUERY        joy_wolf   \n",
+       "\n",
+       "  @switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D  \n",
+       "0  is upset that he can't update his Facebook by ...                                                                   \n",
+       "1  @Kenichan I dived many times for the ball. Man...                                                                   \n",
+       "2    my whole body feels itchy and like its on fire                                                                    \n",
+       "3  @nationwideclass no, it's not behaving at all....                                                                   \n",
+       "4                      @Kwesidei not the whole crew                                                                    "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_names = ['target' , 'id' , 'date' , 'flag' , 'user' , 'text']\n",
+    "dataset.columns = col_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>target</th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>user</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810672</td>\n",
+       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>scotthamilton</td>\n",
+       "      <td>is upset that he can't update his Facebook by ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810917</td>\n",
+       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>mattycus</td>\n",
+       "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811184</td>\n",
+       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>ElleCTF</td>\n",
+       "      <td>my whole body feels itchy and like its on fire</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811193</td>\n",
+       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>Karoli</td>\n",
+       "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811372</td>\n",
+       "      <td>Mon Apr 06 22:20:00 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>joy_wolf</td>\n",
+       "      <td>@Kwesidei not the whole crew</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   target          id                          date      flag           user  \\\n",
+       "0       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY  scotthamilton   \n",
+       "1       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY       mattycus   \n",
+       "2       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY        ElleCTF   \n",
+       "3       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY         Karoli   \n",
+       "4       0  1467811372  Mon Apr 06 22:20:00 PDT 2009  NO_QUERY       joy_wolf   \n",
+       "\n",
+       "                                                text  \n",
+       "0  is upset that he can't update his Facebook by ...  \n",
+       "1  @Kenichan I dived many times for the ball. Man...  \n",
+       "2    my whole body feels itchy and like its on fire   \n",
+       "3  @nationwideclass no, it's not behaving at all....  \n",
+       "4                      @Kwesidei not the whole crew   "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1599999, 6)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "target    0\n",
+       "id        0\n",
+       "date      0\n",
+       "flag      0\n",
+       "user      0\n",
+       "text      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking for missing values\n",
+    "dataset.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "target\n",
+       "4    800000\n",
+       "0    799999\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Distribution of tweets\n",
+    "dataset['target'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Converting 0 to -ve and 4 to +ve\n",
+    "dataset['target'] = dataset['target'].map({0:0 , 4:1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "target\n",
+       "1    800000\n",
+       "0    799999\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset['target'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Stemming\n",
+    "\n",
+    "stremmer = PorterStemmer()\n",
+    "\n",
+    "def stemming(content):\n",
+    "    stemmed_content = re.sub('[^a-zA-Z]',' ',content) # removing not a-z and A-Z\n",
+    "    stemmed_content = stemmed_content.lower()\n",
+    "    stemmed_content = stemmed_content.split()\n",
+    "    stemmed_content = [stremmer.stem(word) for word in stemmed_content if not word in stopwords.words('english')]\n",
+    "    stemmed_content = ' '.join(stemmed_content)\n",
+    "    return stemmed_content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset['text'] = dataset['text'].apply(stemming)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>target</th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>flag</th>\n",
+       "      <th>user</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810672</td>\n",
+       "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>scotthamilton</td>\n",
+       "      <td>upset updat facebook text might cri result sch...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467810917</td>\n",
+       "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>mattycus</td>\n",
+       "      <td>kenichan dive mani time ball manag save rest g...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811184</td>\n",
+       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>ElleCTF</td>\n",
+       "      <td>whole bodi feel itchi like fire</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811193</td>\n",
+       "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>Karoli</td>\n",
+       "      <td>nationwideclass behav mad see</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1467811372</td>\n",
+       "      <td>Mon Apr 06 22:20:00 PDT 2009</td>\n",
+       "      <td>NO_QUERY</td>\n",
+       "      <td>joy_wolf</td>\n",
+       "      <td>kwesidei whole crew</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   target          id                          date      flag           user  \\\n",
+       "0       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY  scotthamilton   \n",
+       "1       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY       mattycus   \n",
+       "2       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY        ElleCTF   \n",
+       "3       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY         Karoli   \n",
+       "4       0  1467811372  Mon Apr 06 22:20:00 PDT 2009  NO_QUERY       joy_wolf   \n",
+       "\n",
+       "                                                text  \n",
+       "0  upset updat facebook text might cri result sch...  \n",
+       "1  kenichan dive mani time ball manag save rest g...  \n",
+       "2                    whole bodi feel itchi like fire  \n",
+       "3                      nationwideclass behav mad see  \n",
+       "4                                kwesidei whole crew  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = dataset['text']\n",
+    "y = dataset['target']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# splitting the dataset\n",
+    "x_train , x_test , y_train , y_test = train_test_split(x , y , test_size = 0.2 , random_state = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert textual data to numerical data\n",
+    "vectorizer = TfidfVectorizer()\n",
+    "x_train = vectorizer.fit_transform(x_train)\n",
+    "x_test = vectorizer.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  (0, 145591)\t0.48328892862950174\n",
+      "  (0, 384310)\t0.38648598535906226\n",
+      "  (0, 160355)\t0.18966194768681632\n",
+      "  (0, 422796)\t0.4213995220282958\n",
+      "  (0, 246262)\t0.516206150117446\n",
+      "  (0, 393595)\t0.18633353695642413\n",
+      "  (0, 149660)\t0.12602103676347354\n",
+      "  (0, 150562)\t0.187752051036393\n",
+      "  (0, 443991)\t0.22625223143666687\n",
+      "  (1, 172128)\t0.6067414559564506\n",
+      "  (1, 418051)\t0.7948992424350689\n",
+      "  (2, 406965)\t0.6931768888241752\n",
+      "  (2, 275790)\t0.3769717187165907\n",
+      "  (2, 290673)\t0.24841016587340456\n",
+      "  (2, 150650)\t0.20986098127991223\n",
+      "  (2, 42279)\t0.5211994648067829\n",
+      "  (3, 175231)\t0.30748407834013664\n",
+      "  (3, 89478)\t0.5137960384023271\n",
+      "  (3, 135304)\t0.18399221471225605\n",
+      "  (3, 292469)\t0.3352332134067401\n",
+      "  (3, 399931)\t0.21912347276618377\n",
+      "  (3, 317428)\t0.5137960384023271\n",
+      "  (3, 175234)\t0.4280552121498152\n",
+      "  (4, 408579)\t0.14704998873675024\n",
+      "  (4, 300289)\t0.2058593651486058\n",
+      "  :\t:\n",
+      "  (1279995, 101591)\t0.8081360486674279\n",
+      "  (1279995, 248952)\t0.5889958631808858\n",
+      "  (1279996, 277402)\t0.6930282733228941\n",
+      "  (1279996, 133848)\t0.34541074396262944\n",
+      "  (1279996, 435543)\t0.2695787059712405\n",
+      "  (1279996, 230940)\t0.28709000004756496\n",
+      "  (1279996, 384176)\t0.22284929416293517\n",
+      "  (1279996, 168384)\t0.22632455016071848\n",
+      "  (1279996, 445127)\t0.19037698208802128\n",
+      "  (1279996, 170080)\t0.2583579928589749\n",
+      "  (1279996, 408579)\t0.2035510397723402\n",
+      "  (1279997, 22582)\t0.40592321055556474\n",
+      "  (1279997, 407667)\t0.4517041173506153\n",
+      "  (1279997, 365896)\t0.34128528334674657\n",
+      "  (1279997, 78807)\t0.20434235294380243\n",
+      "  (1279997, 318283)\t0.48408216042272795\n",
+      "  (1279997, 278738)\t0.20662639845796468\n",
+      "  (1279997, 31095)\t0.1879300266675478\n",
+      "  (1279997, 267587)\t0.18767777014427442\n",
+      "  (1279997, 334582)\t0.19548006690275818\n",
+      "  (1279997, 243236)\t0.23915227399663266\n",
+      "  (1279997, 241760)\t0.17315132700092342\n",
+      "  (1279998, 360147)\t0.7967059461608392\n",
+      "  (1279998, 393318)\t0.47775281405037406\n",
+      "  (1279998, 150849)\t0.37015116374112683\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\KIIT\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Training the model\n",
+    "model = LogisticRegression()\n",
+    "model.fit(x_train , y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.775615625\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Testing the model\n",
+    "y_pred = model.predict(x_test)\n",
+    "print(accuracy_score(y_test , y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to predict the sentiment\n",
+    "def predict_sentiment(text):\n",
+    "    text = re.sub('[^a-zA-Z]',' ',text) # removing not a-z and A-Z\n",
+    "    text = text.lower()\n",
+    "    text = text.split() \n",
+    "    text = [stremmer.stem(word) for word in text if not word in stopwords.words('english')]\n",
+    "    text = ' '.join(text)\n",
+    "    text = [text]\n",
+    "    text = vectorizer.transform(text)   \n",
+    "    sentiment = model.predict(text)\n",
+    "    if sentiment == 0:\n",
+    "        return \"Negative\"\n",
+    "    else:\n",
+    "        return \"Positive\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Negative\n",
+      "Positive\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Testing the model\n",
+    "print(predict_sentiment(\"I hate you\"))\n",
+    "print(predict_sentiment(\"I love you\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the model\n",
+    "import pickle\n",
+    "pickle.dump(model , open('model.pkl' , 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump(vectorizer , open('vectorizer.pkl' , 'wb'))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

@@ -0,0 +1,89 @@
+import streamlit as st
+import pickle
+import re
+from sklearn.feature_extraction.text import TfidfVectorizer
+from nltk.corpus import stopwords
+import nltk
+from ntscraper import Nitter
+
+# Download stopwords once, using Streamlit's caching
+@st.cache_resource
+def load_stopwords():
+    nltk.download('stopwords')
+    return stopwords.words('english')
+
+# Load model and vectorizer once
+@st.cache_resource
+def load_model_and_vectorizer():
+    with open('model.pkl', 'rb') as model_file:
+        model = pickle.load(model_file)
+    with open('vectorizer.pkl', 'rb') as vectorizer_file:
+        vectorizer = pickle.load(vectorizer_file)
+    return model, vectorizer
+
+# Define sentiment prediction function
+def predict_sentiment(text, model, vectorizer, stop_words):
+    # Preprocess text
+    text = re.sub('[^a-zA-Z]', ' ', text)
+    text = text.lower()
+    text = text.split()
+    text = [word for word in text if word not in stop_words]
+    text = ' '.join(text)
+    text = [text]
+    text = vectorizer.transform(text)
+    
+    # Predict sentiment
+    sentiment = model.predict(text)
+    return "Negative" if sentiment == 0 else "Positive"
+
+# Initialize Nitter scraper
+@st.cache_resource
+def initialize_scraper():
+    return Nitter(log_level=1)
+
+# Function to create a colored card
+def create_card(tweet_text, sentiment):
+    color = "green" if sentiment == "Positive" else "red"
+    card_html = f"""
+    <div style="background-color: {color}; padding: 10px; border-radius: 5px; margin: 10px 0;">
+        <h5 style="color: white;">{sentiment} Sentiment</h5>
+        <p style="color: white;">{tweet_text}</p>
+    </div>
+    """
+    return card_html
+
+# Main app logic
+def main():
+    st.title("Twitter Sentiment Analysis")
+
+    # Load stopwords, model, vectorizer, and scraper only once
+    stop_words = load_stopwords()
+    model, vectorizer = load_model_and_vectorizer()
+    scraper = initialize_scraper()
+
+    # User input: either text input or Twitter username
+    option = st.selectbox("Choose an option", ["Input text", "Get tweets from user"])
+    
+    if option == "Input text":
+        text_input = st.text_area("Enter text to analyze sentiment")
+        if st.button("Analyze"):
+            sentiment = predict_sentiment(text_input, model, vectorizer, stop_words)
+            st.write(f"Sentiment: {sentiment}")
+
+    elif option == "Get tweets from user":
+        username = st.text_input("Enter Twitter username")
+        if st.button("Fetch Tweets"):
+            tweets_data = scraper.get_tweets(username, mode='user', number=5)
+            if 'tweets' in tweets_data:  # Check if the 'tweets' key exists
+                for tweet in tweets_data['tweets']:
+                    tweet_text = tweet['text']  # Access the text of the tweet
+                    sentiment = predict_sentiment(tweet_text, model, vectorizer, stop_words)  # Predict sentiment of the tweet text
+                    
+                    # Create and display the colored card for the tweet
+                    card_html = create_card(tweet_text, sentiment)
+                    st.markdown(card_html, unsafe_allow_html=True)
+            else:
+                st.write("No tweets found or an error occurred.")
+
+if __name__ == "__main__":
+    main()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68f9b697c666945be1754523f1a9009c96ef21527243a17524f95aaf8620b8b8
+size 3687998

vectorizer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:11ac9f2bf806dfe7ff5976f837b176078352f728240b629a12692e48a1df628b
+size 14777888