twitter-sentiment-analysis.ipynb

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   },
   "source": [
    "#  Importing Libraries\n"
   ]
  },
  {
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   "source": [
    "import numpy as np  #helps in working with data in the form arrays\n",
    "import pandas as pd #helps in performing data preprocessing and importing of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c74218b",
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     "status": "completed"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf #Module to work for NLP,You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network.\n",
    "import matplotlib.pyplot as plt #module for producing visualizations of datasets\n",
    "import nltk #Natural language toolkit\n",
    "nltk.download('stopwords') \n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer\n",
    "import re #module to work with regular expressions\n",
    "import pickle\n",
    "import seaborn as sns #module to work on visualizations\n",
    "from sklearn.svm import LinearSVC #scikit learn module - used for training the model and determining the algorithm to be used\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3666fe",
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     "start_time": "2022-10-14T19:56:54.486797",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Importing Dataset"
   ]
  },
  {
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   "execution_count": 3,
   "id": "a5c98446",
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810369</td>\n",
       "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>_TheSpecialOne_</td>\n",
       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0           1                             2         3                4  \\\n",
       "0  0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY  _TheSpecialOne_   \n",
       "1  0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY    scotthamilton   \n",
       "2  0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY         mattycus   \n",
       "3  0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY          ElleCTF   \n",
       "4  0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY           Karoli   \n",
       "\n",
       "                                                   5  \n",
       "0  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
       "1  is upset that he can't update his Facebook by ...  \n",
       "2  @Kenichan I dived many times for the ball. Man...  \n",
       "3    my whole body feels itchy and like its on fire   \n",
       "4  @nationwideclass no, it's not behaving at all....  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df = pd.read_csv('../input/sentiment140/training.1600000.processed.noemoticon.csv',\n",
    "                 encoding = 'latin',header=None)\n",
    "df.head()"
   ]
  },
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Analysing the Data"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600000, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.shape"
   ]
  },
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   "execution_count": 5,
   "id": "727c5959",
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   },
   "outputs": [],
   "source": [
    "df.columns=['sentiments','id','date','query','user','tweet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiments</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>query</th>\n",
       "      <th>user</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1467810369</td>\n",
       "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
       "      <td>NO_QUERY</td>\n",
       "      <td>_TheSpecialOne_</td>\n",
       "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</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",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   sentiments          id                          date     query  \\\n",
       "0           0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
       "1           0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
       "2           0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
       "3           0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "4           0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
       "\n",
       "              user                                              tweet  \n",
       "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
       "1    scotthamilton  is upset that he can't update his Facebook by ...  \n",
       "2         mattycus  @Kenichan I dived many times for the ball. Man...  \n",
       "3          ElleCTF    my whole body feels itchy and like its on fire   \n",
       "4           Karoli  @nationwideclass no, it's not behaving at all....  "
      ]
     },
     "execution_count": 6,
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   "source": [
    "df.head()"
   ]
  },
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   },
   "outputs": [],
   "source": [
    "df=df[['sentiments','tweet']]"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Negative'), Text(1, 0, 'Positive')]"
      ]
     },
     "execution_count": 8,
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.groupby('sentiments').count().plot(kind='bar', title='Distribution of data',legend=False)\n",
    "ax.set_xticklabels(['Negative','Positive'], rotation=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef05b2e",
   "metadata": {
    "papermill": {
     "duration": 0.008749,
     "end_time": "2022-10-14T19:57:02.146388",
     "exception": false,
     "start_time": "2022-10-14T19:57:02.137639",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preprocessing the Data\n",
    "* Stemming \n",
    "* Lematization\n",
    "* removing Hyperlinks and useraccounts\n",
    "* removing Stopwords\n",
    "\n",
    "using NLTK for text processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9024d5ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:57:02.166778Z",
     "iopub.status.busy": "2022-10-14T19:57:02.165823Z",
     "iopub.status.idle": "2022-10-14T19:57:02.173893Z",
     "shell.execute_reply": "2022-10-14T19:57:02.173085Z"
    },
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     "end_time": "2022-10-14T19:57:02.176189",
     "exception": false,
     "start_time": "2022-10-14T19:57:02.155592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "stop_words = stopwords.words('english')\n",
    "stemmer = SnowballStemmer('english')\n",
    "\n",
    "text_cleaning_re = \"@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fa1329d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:57:02.195860Z",
     "iopub.status.busy": "2022-10-14T19:57:02.195428Z",
     "iopub.status.idle": "2022-10-14T19:57:02.202180Z",
     "shell.execute_reply": "2022-10-14T19:57:02.201196Z"
    },
    "papermill": {
     "duration": 0.018908,
     "end_time": "2022-10-14T19:57:02.204353",
     "exception": false,
     "start_time": "2022-10-14T19:57:02.185445",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def preprocess(text, stem=False):\n",
    "  text = re.sub(text_cleaning_re, ' ', str(text).lower()).strip()\n",
    "  tokens = []\n",
    "  for token in text.split():\n",
    "    if token not in stop_words:\n",
    "      if stem:\n",
    "        tokens.append(stemmer.stem(token))\n",
    "      else:\n",
    "        tokens.append(token)\n",
    "  return \" \".join(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1cb181e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:57:02.222976Z",
     "iopub.status.busy": "2022-10-14T19:57:02.222542Z",
     "iopub.status.idle": "2022-10-14T19:58:04.052670Z",
     "shell.execute_reply": "2022-10-14T19:58:04.051370Z"
    },
    "papermill": {
     "duration": 61.842962,
     "end_time": "2022-10-14T19:58:04.055674",
     "exception": false,
     "start_time": "2022-10-14T19:57:02.212712",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.tweet = df.tweet.apply(lambda x: preprocess(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "86290ba9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:58:04.074767Z",
     "iopub.status.busy": "2022-10-14T19:58:04.074392Z",
     "iopub.status.idle": "2022-10-14T19:58:04.388042Z",
     "shell.execute_reply": "2022-10-14T19:58:04.386931Z"
    },
    "papermill": {
     "duration": 0.326117,
     "end_time": "2022-10-14T19:58:04.390550",
     "exception": false,
     "start_time": "2022-10-14T19:58:04.064433",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tweet , sentiments = list(df['tweet']), list(df['sentiments'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a8712e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:58:04.410496Z",
     "iopub.status.busy": "2022-10-14T19:58:04.410107Z",
     "iopub.status.idle": "2022-10-14T19:58:04.420264Z",
     "shell.execute_reply": "2022-10-14T19:58:04.419035Z"
    },
    "papermill": {
     "duration": 0.022854,
     "end_time": "2022-10-14T19:58:04.422984",
     "exception": false,
     "start_time": "2022-10-14T19:58:04.400130",
     "status": "completed"
    },
    "tags": []
   },
   "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>sentiments</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>awww bummer shoulda got david carr third day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>upset update facebook texting might cry result...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>dived many times ball managed save 50 rest go ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>whole body feels itchy like fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>behaving mad see</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sentiments                                              tweet\n",
       "0           0       awww bummer shoulda got david carr third day\n",
       "1           0  upset update facebook texting might cry result...\n",
       "2           0  dived many times ball managed save 50 rest go ...\n",
       "3           0                   whole body feels itchy like fire\n",
       "4           0                                   behaving mad see"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6cff1ca",
   "metadata": {
    "papermill": {
     "duration": 0.00855,
     "end_time": "2022-10-14T19:58:04.440338",
     "exception": false,
     "start_time": "2022-10-14T19:58:04.431788",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train and Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9269dfd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:58:04.459655Z",
     "iopub.status.busy": "2022-10-14T19:58:04.459275Z",
     "iopub.status.idle": "2022-10-14T19:58:05.395828Z",
     "shell.execute_reply": "2022-10-14T19:58:05.394874Z"
    },
    "papermill": {
     "duration": 0.949373,
     "end_time": "2022-10-14T19:58:05.398517",
     "exception": false,
     "start_time": "2022-10-14T19:58:04.449144",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(tweet,sentiments,test_size=0.05,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c37d0b1",
   "metadata": {
    "papermill": {
     "duration": 0.00851,
     "end_time": "2022-10-14T19:58:05.415908",
     "exception": false,
     "start_time": "2022-10-14T19:58:05.407398",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# TF-IDF Vectorizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6cf65d86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:58:05.435253Z",
     "iopub.status.busy": "2022-10-14T19:58:05.434835Z",
     "iopub.status.idle": "2022-10-14T19:58:58.419211Z",
     "shell.execute_reply": "2022-10-14T19:58:58.418017Z"
    },
    "papermill": {
     "duration": 53.003725,
     "end_time": "2022-10-14T19:58:58.428391",
     "exception": false,
     "start_time": "2022-10-14T19:58:05.424666",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(max_features=50000, ngram_range=(1, 2))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectoriser=TfidfVectorizer(ngram_range=(1,2),max_features=50000)\n",
    "vectoriser.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7bf3f9c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:58:58.448089Z",
     "iopub.status.busy": "2022-10-14T19:58:58.447647Z",
     "iopub.status.idle": "2022-10-14T19:59:27.777551Z",
     "shell.execute_reply": "2022-10-14T19:59:27.776588Z"
    },
    "papermill": {
     "duration": 29.342921,
     "end_time": "2022-10-14T19:59:27.780384",
     "exception": false,
     "start_time": "2022-10-14T19:58:58.437463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = vectoriser.transform(X_train)\n",
    "X_test  = vectoriser.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4febec6",
   "metadata": {
    "papermill": {
     "duration": 0.008571,
     "end_time": "2022-10-14T19:59:27.798079",
     "exception": false,
     "start_time": "2022-10-14T19:59:27.789508",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Creating Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "caf95690",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:59:27.818033Z",
     "iopub.status.busy": "2022-10-14T19:59:27.817051Z",
     "iopub.status.idle": "2022-10-14T19:59:27.826690Z",
     "shell.execute_reply": "2022-10-14T19:59:27.825327Z"
    },
    "papermill": {
     "duration": 0.022603,
     "end_time": "2022-10-14T19:59:27.829475",
     "exception": false,
     "start_time": "2022-10-14T19:59:27.806872",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def model_Evaluate(model):\n",
    "    y_pred = model.predict(X_test)\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    categories  = ['Negative','Positive']\n",
    "    group_names = ['True Neg','False Pos', 'False Neg','True Pos']\n",
    "    group_percentages = ['{0:.2%}'.format(value) for value in cf_matrix.flatten() / np.sum(cf_matrix)]\n",
    "\n",
    "    labels = [f'{v1}\\n{v2}' for v1, v2 in zip(group_names,group_percentages)]\n",
    "    labels = np.asarray(labels).reshape(2,2)\n",
    "\n",
    "    sns.heatmap(cf_matrix, annot = labels, cmap = 'Blues',fmt = '',xticklabels = categories, yticklabels = categories)\n",
    "\n",
    "    plt.xlabel(\"Predicted values\", fontdict = {'size':14}, labelpad = 10)\n",
    "    plt.ylabel(\"Actual values\"   , fontdict = {'size':14}, labelpad = 10)\n",
    "    plt.title (\"Confusion Matrix\", fontdict = {'size':18}, pad = 20)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e93f9bb5",
   "metadata": {
    "papermill": {
     "duration": 0.008669,
     "end_time": "2022-10-14T19:59:27.847083",
     "exception": false,
     "start_time": "2022-10-14T19:59:27.838414",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b69f6a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T19:59:27.867278Z",
     "iopub.status.busy": "2022-10-14T19:59:27.866475Z",
     "iopub.status.idle": "2022-10-14T20:01:22.810955Z",
     "shell.execute_reply": "2022-10-14T20:01:22.809865Z"
    },
    "papermill": {
     "duration": 114.957105,
     "end_time": "2022-10-14T20:01:22.813541",
     "exception": false,
     "start_time": "2022-10-14T19:59:27.856436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.77      0.79     39989\n",
      "           4       0.78      0.81      0.79     40011\n",
      "\n",
      "    accuracy                           0.79     80000\n",
      "   macro avg       0.79      0.79      0.79     80000\n",
      "weighted avg       0.79      0.79      0.79     80000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LRmodel = LogisticRegression(C = 2, max_iter = 1000, n_jobs=-1) \n",
    "LRmodel.fit(X_train, y_train)\n",
    "model_Evaluate(LRmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5c290c",
   "metadata": {
    "papermill": {
     "duration": 0.009046,
     "end_time": "2022-10-14T20:01:22.832000",
     "exception": false,
     "start_time": "2022-10-14T20:01:22.822954",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Linear Support Vector Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2387e9d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T20:01:22.852844Z",
     "iopub.status.busy": "2022-10-14T20:01:22.851779Z",
     "iopub.status.idle": "2022-10-14T20:01:54.226227Z",
     "shell.execute_reply": "2022-10-14T20:01:54.225100Z"
    },
    "papermill": {
     "duration": 31.387699,
     "end_time": "2022-10-14T20:01:54.228777",
     "exception": false,
     "start_time": "2022-10-14T20:01:22.841078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.76      0.78     39989\n",
      "           4       0.77      0.81      0.79     40011\n",
      "\n",
      "    accuracy                           0.79     80000\n",
      "   macro avg       0.79      0.79      0.79     80000\n",
      "weighted avg       0.79      0.79      0.79     80000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SVCmodel = LinearSVC()\n",
    "SVCmodel.fit(X_train, y_train)\n",
    "model_Evaluate(SVCmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9c510d",
   "metadata": {
    "papermill": {
     "duration": 0.009276,
     "end_time": "2022-10-14T20:01:54.247848",
     "exception": false,
     "start_time": "2022-10-14T20:01:54.238572",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c37aa98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T20:01:54.268831Z",
     "iopub.status.busy": "2022-10-14T20:01:54.268417Z",
     "iopub.status.idle": "2022-10-14T20:01:55.502855Z",
     "shell.execute_reply": "2022-10-14T20:01:55.501725Z"
    },
    "papermill": {
     "duration": 1.247634,
     "end_time": "2022-10-14T20:01:55.505155",
     "exception": false,
     "start_time": "2022-10-14T20:01:54.257521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.76      0.77     39989\n",
      "           4       0.77      0.80      0.78     40011\n",
      "\n",
      "    accuracy                           0.78     80000\n",
      "   macro avg       0.78      0.78      0.78     80000\n",
      "weighted avg       0.78      0.78      0.78     80000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BNBmodel = BernoulliNB(alpha = 2)\n",
    "BNBmodel.fit(X_train, y_train)\n",
    "model_Evaluate(BNBmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77036632",
   "metadata": {
    "papermill": {
     "duration": 0.009776,
     "end_time": "2022-10-14T20:01:55.525074",
     "exception": false,
     "start_time": "2022-10-14T20:01:55.515298",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Saving the model and vectorizer files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b999c15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T20:01:55.547390Z",
     "iopub.status.busy": "2022-10-14T20:01:55.546496Z",
     "iopub.status.idle": "2022-10-14T20:01:58.681232Z",
     "shell.execute_reply": "2022-10-14T20:01:58.680041Z"
    },
    "papermill": {
     "duration": 3.149225,
     "end_time": "2022-10-14T20:01:58.684333",
     "exception": false,
     "start_time": "2022-10-14T20:01:55.535108",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "file = open('vectoriser','wb')\n",
    "pickle.dump(vectoriser, file)\n",
    "file.close()\n",
    "\n",
    "file = open('Sentiment-LR.pickle','wb')\n",
    "pickle.dump(LRmodel, file)\n",
    "file.close()\n",
    "\n",
    "file = open('Sentiment-SVC.pickle','wb')\n",
    "pickle.dump(SVCmodel, file)\n",
    "file.close()\n",
    "\n",
    "file = open('Sentiment-BNB.pickle','wb')\n",
    "pickle.dump(BNBmodel, file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9a7e67",
   "metadata": {
    "papermill": {
     "duration": 0.010176,
     "end_time": "2022-10-14T20:01:58.704770",
     "exception": false,
     "start_time": "2022-10-14T20:01:58.694594",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Testing the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4954b596",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-14T20:01:58.727704Z",
     "iopub.status.busy": "2022-10-14T20:01:58.727263Z",
     "iopub.status.idle": "2022-10-14T20:02:00.566376Z",
     "shell.execute_reply": "2022-10-14T20:02:00.565258Z"
    },
    "papermill": {
     "duration": 1.854298,
     "end_time": "2022-10-14T20:02:00.569577",
     "exception": false,
     "start_time": "2022-10-14T20:01:58.715279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression \n",
      "\n",
      "                          tweet sentiment\n",
      "0                  I hate Data   Negative\n",
      "1                  I love Data   Positive\n",
      "2  He passed away at the age 70  Negative \n",
      "\n",
      "BNB Model \n",
      "\n",
      "                          tweet sentiment\n",
      "0                  I hate Data   Negative\n",
      "1                  I love Data   Positive\n",
      "2  He passed away at the age 70  Negative \n",
      "\n",
      "SVC Model \n",
      "\n",
      "                          tweet sentiment\n",
      "0                  I hate Data   Negative\n",
      "1                  I love Data   Positive\n",
      "2  He passed away at the age 70  Negative \n",
      "\n"
     ]
    }
   ],
   "source": [
    "file = open('./vectoriser', 'rb')\n",
    "vectoriser = pickle.load(file)\n",
    "file.close()\n",
    "file = open('./Sentiment-LR.pickle', 'rb')\n",
    "LRmodel = pickle.load(file)\n",
    "file.close()\n",
    "file = open('./Sentiment-BNB.pickle', 'rb')\n",
    "BNBmodel = pickle.load(file)\n",
    "file.close()\n",
    "file = open('./Sentiment-SVC.pickle', 'rb')\n",
    "SVCmodel = pickle.load(file)\n",
    "file.close()\n",
    "    \n",
    "\n",
    "def predict1(vectoriser, model, tweet):\n",
    "    textdata = vectoriser.transform(tweet)\n",
    "    sentiment = model.predict(textdata)\n",
    "    data = []\n",
    "    for tweet, pred in zip(tweet, sentiment):\n",
    "        data.append((tweet,pred))\n",
    "    df = pd.DataFrame(data, columns = ['tweet','sentiment'])\n",
    "    df = df.replace([0,4], [\"Negative\",\"Positive\"])\n",
    "    return df\n",
    "\n",
    "def predict2(vectoriser, model, tweet):\n",
    "    textdata = vectoriser.transform(tweet)\n",
    "    sentiment = model.predict(textdata)\n",
    "    data = []\n",
    "    for tweet, pred in zip(tweet, sentiment):\n",
    "        data.append((tweet,pred))\n",
    "    df = pd.DataFrame(data, columns = ['tweet','sentiment'])\n",
    "    df = df.replace([0,4], [\"Negative\",\"Positive\"])\n",
    "    return df\n",
    "\n",
    "tweet = [\"I hate Data \",\"I love Data \",\"He passed away at the age 70\"]\n",
    "\n",
    "print(\"Logistic Regression \\n\")\n",
    "df = predict1(vectoriser, LRmodel, tweet)\n",
    "print(df.head(), \"\\n\")\n",
    "print(\"BNB Model \\n\")\n",
    "df = predict2(vectoriser, BNBmodel,tweet)\n",
    "print(df.head(), \"\\n\")\n",
    "print(\"SVC Model \\n\")\n",
    "df = predict2(vectoriser, SVCmodel,tweet)\n",
    "print(df.head(),'\\n' )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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