Emotion Detector.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9132e06d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "import neattext.functions as nfx\n",
    "import string as st\n",
    "import re\n",
    "import nltk\n",
    "from nltk import PorterStemmer, WordNetLemmatizer\n",
    "import nltk.corpus\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5966fbbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('emotion_dataset_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d18d240d",
   "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>Unnamed: 0</th>\n",
       "      <th>Emotion</th>\n",
       "      <th>Text</th>\n",
       "      <th>Clean_Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Why ?</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>joy</td>\n",
       "      <td>Sage Act upgrade on my to do list for tommorow.</td>\n",
       "      <td>Sage Act upgrade list tommorow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>sadness</td>\n",
       "      <td>ON THE WAY TO MY HOMEGIRL BABY FUNERAL!!! MAN ...</td>\n",
       "      <td>WAY HOMEGIRL BABY FUNERAL MAN HATE FUNERALS SH...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>joy</td>\n",
       "      <td>Such an eye ! The true hazel eye-and so brill...</td>\n",
       "      <td>eye  true hazel eyeand brilliant  Regular feat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>joy</td>\n",
       "      <td>@Iluvmiasantos ugh babe.. hugggzzz for u .!  b...</td>\n",
       "      <td>ugh babe hugggzzz u  babe naamazed nga ako e...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Emotion                                               Text  \\\n",
       "0           0  neutral                                             Why ?    \n",
       "1           1      joy    Sage Act upgrade on my to do list for tommorow.   \n",
       "2           2  sadness  ON THE WAY TO MY HOMEGIRL BABY FUNERAL!!! MAN ...   \n",
       "3           3      joy   Such an eye ! The true hazel eye-and so brill...   \n",
       "4           4      joy  @Iluvmiasantos ugh babe.. hugggzzz for u .!  b...   \n",
       "\n",
       "                                          Clean_Text  \n",
       "0                                                NaN  \n",
       "1                     Sage Act upgrade list tommorow  \n",
       "2  WAY HOMEGIRL BABY FUNERAL MAN HATE FUNERALS SH...  \n",
       "3  eye  true hazel eyeand brilliant  Regular feat...  \n",
       "4    ugh babe hugggzzz u  babe naamazed nga ako e...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4ca739b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['Unnamed: 0','Clean_Text'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f98e31a0",
   "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>Emotion</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>Why ?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joy</td>\n",
       "      <td>Sage Act upgrade on my to do list for tommorow.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sadness</td>\n",
       "      <td>ON THE WAY TO MY HOMEGIRL BABY FUNERAL!!! MAN ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>joy</td>\n",
       "      <td>Such an eye ! The true hazel eye-and so brill...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>joy</td>\n",
       "      <td>@Iluvmiasantos ugh babe.. hugggzzz for u .!  b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Emotion                                               Text\n",
       "0  neutral                                             Why ? \n",
       "1      joy    Sage Act upgrade on my to do list for tommorow.\n",
       "2  sadness  ON THE WAY TO MY HOMEGIRL BABY FUNERAL!!! MAN ...\n",
       "3      joy   Such an eye ! The true hazel eye-and so brill...\n",
       "4      joy  @Iluvmiasantos ugh babe.. hugggzzz for u .!  b..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4dcb49a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Emotion'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b01a532b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34792, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "61d7dd0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion    0\n",
       "Text       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "846136ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b59a8456",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sentiment(text):\n",
    "    blob=TextBlob(text)\n",
    "    sentiment=blob.sentiment.polarity\n",
    "    if sentiment>0:\n",
    "        res='Positive'\n",
    "    elif sentiment<0:\n",
    "        res='Negative'\n",
    "    else:\n",
    "        res='Neutral'\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ecbc9af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Positive'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_sentiment('I love coding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "feebd055",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sentiment']=df['Text'].apply(get_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0ee3ab78",
   "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>Emotion</th>\n",
       "      <th>Text</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>Why ?</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joy</td>\n",
       "      <td>Sage Act upgrade on my to do list for tommorow.</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sadness</td>\n",
       "      <td>ON THE WAY TO MY HOMEGIRL BABY FUNERAL!!! MAN ...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>joy</td>\n",
       "      <td>Such an eye ! The true hazel eye-and so brill...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>joy</td>\n",
       "      <td>@Iluvmiasantos ugh babe.. hugggzzz for u .!  b...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Emotion                                               Text Sentiment\n",
       "0  neutral                                             Why ?    Neutral\n",
       "1      joy    Sage Act upgrade on my to do list for tommorow.   Neutral\n",
       "2  sadness  ON THE WAY TO MY HOMEGIRL BABY FUNERAL!!! MAN ...  Negative\n",
       "3      joy   Such an eye ! The true hazel eye-and so brill...  Positive\n",
       "4      joy  @Iluvmiasantos ugh babe.. hugggzzz for u .!  b...   Neutral"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36932f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    13616\n",
       "Neutral     12371\n",
       "Negative     8805\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4596b175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Emotion   Sentiment\n",
       "anger     Negative     1787\n",
       "          Neutral      1386\n",
       "          Positive     1124\n",
       "disgust   Negative      325\n",
       "          Neutral       249\n",
       "          Positive      282\n",
       "fear      Negative     1534\n",
       "          Neutral      1843\n",
       "          Positive     2033\n",
       "joy       Negative     1682\n",
       "          Neutral      3648\n",
       "          Positive     5715\n",
       "neutral   Negative      178\n",
       "          Neutral      1523\n",
       "          Positive      553\n",
       "sadness   Negative     2630\n",
       "          Neutral      2127\n",
       "          Positive     1965\n",
       "shame     Negative       46\n",
       "          Neutral        50\n",
       "          Positive       50\n",
       "surprise  Negative      623\n",
       "          Neutral      1545\n",
       "          Positive     1894\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Emotion','Sentiment']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d4436f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "def clean_text(text):\n",
    "        text=text.lower()\n",
    "        text = re.sub(r'\\d+', '', text)\n",
    "        translator = str.maketrans('', '', st.punctuation)\n",
    "        text=text.translate(translator)\n",
    "        text=\" \".join(text.split())\n",
    "        data=text\n",
    "        stop_words = set(stopwords.words(\"english\"))\n",
    "        word_tokens = word_tokenize(text)\n",
    "        text = [word for word in word_tokens if word not in stop_words]\n",
    "        text=\" \".join(text)\n",
    "        if len(text)==0:\n",
    "            text=data\n",
    "        return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5925804b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Clean_Text']=df['Text'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "92154af1",
   "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>Emotion</th>\n",
       "      <th>Text</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Clean_Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>Why ?</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>why</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joy</td>\n",
       "      <td>Sage Act upgrade on my to do list for tommorow.</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>sage act upgrade list tommorow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sadness</td>\n",
       "      <td>ON THE WAY TO MY HOMEGIRL BABY FUNERAL!!! MAN ...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>way homegirl baby funeral man hate funerals re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>joy</td>\n",
       "      <td>Such an eye ! The true hazel eye-and so brill...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>eye true hazel eyeand brilliant regular featur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>joy</td>\n",
       "      <td>@Iluvmiasantos ugh babe.. hugggzzz for u .!  b...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>iluvmiasantos ugh babe hugggzzz u babe naamaze...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Emotion                                               Text Sentiment  \\\n",
       "0  neutral                                             Why ?    Neutral   \n",
       "1      joy    Sage Act upgrade on my to do list for tommorow.   Neutral   \n",
       "2  sadness  ON THE WAY TO MY HOMEGIRL BABY FUNERAL!!! MAN ...  Negative   \n",
       "3      joy   Such an eye ! The true hazel eye-and so brill...  Positive   \n",
       "4      joy  @Iluvmiasantos ugh babe.. hugggzzz for u .!  b...   Neutral   \n",
       "\n",
       "                                          Clean_Text  \n",
       "0                                                why  \n",
       "1                     sage act upgrade list tommorow  \n",
       "2  way homegirl baby funeral man hate funerals re...  \n",
       "3  eye true hazel eyeand brilliant regular featur...  \n",
       "4  iluvmiasantos ugh babe hugggzzz u babe naamaze...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "82c99378",
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_df=df[['Text','Clean_Text','Emotion']]\n",
    "mod_df.to_csv('mod_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3c371619",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9c8e9c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0     0\n",
       "Text           0\n",
       "Clean_Text    17\n",
       "Emotion        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl=pd.read_csv('mod_df.csv')\n",
    "dl.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "176642e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_keywords(text,num=50):\n",
    "    tokens=[tok for tok in text.split()]\n",
    "    most_common_tokens=Counter(tokens).most_common(num)\n",
    "    return dict(most_common_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6c3c5071",
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_list=df['Emotion'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a41e7f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['neutral', 'joy', 'sadness', 'fear', 'surprise', 'anger', 'shame', 'disgust']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8a431e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "joy_list=df[df['Emotion']=='joy']['Clean_Text'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bf722704",
   "metadata": {},
   "outputs": [],
   "source": [
    "joy_docx=' '.join(joy_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b2baeabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract_keywords(joy_docx)\n",
    "keyword_joy=extract_keywords(joy_docx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5071e212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'day': 791,\n",
       " 'time': 705,\n",
       " 'im': 694,\n",
       " 'amp': 681,\n",
       " 'christmas': 622,\n",
       " 'love': 591,\n",
       " 'work': 556,\n",
       " 'today': 523,\n",
       " 'get': 514,\n",
       " 'like': 497,\n",
       " 'tomorrow': 492,\n",
       " 'happy': 486,\n",
       " 'got': 460,\n",
       " 'good': 431,\n",
       " 'one': 406,\n",
       " 'back': 405,\n",
       " 'joy': 397,\n",
       " 'go': 374,\n",
       " 'going': 366,\n",
       " 'night': 349,\n",
       " 'school': 341,\n",
       " 'morning': 325,\n",
       " 'home': 322,\n",
       " 'life': 304,\n",
       " 'first': 298,\n",
       " 'see': 278,\n",
       " 'feel': 269,\n",
       " 'dont': 267,\n",
       " 'great': 256,\n",
       " 'hours': 254,\n",
       " 'week': 238,\n",
       " 'family': 237,\n",
       " 'year': 234,\n",
       " 'cant': 231,\n",
       " 'last': 230,\n",
       " 'much': 229,\n",
       " 'god': 224,\n",
       " 'tonight': 221,\n",
       " 'days': 219,\n",
       " 'long': 218,\n",
       " 'people': 217,\n",
       " 'new': 216,\n",
       " 'well': 211,\n",
       " 'oh': 209,\n",
       " 'sleep': 192,\n",
       " 'na': 191,\n",
       " 'next': 189,\n",
       " 'know': 188,\n",
       " 'hour': 184,\n",
       " 'feeling': 180}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_joy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ff5d4e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "906196f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cafa289a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df['Clean_Text']\n",
    "y=df['Emotion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ee8bb9de",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv=CountVectorizer()\n",
    "X=cv.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5d0f57fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "...     X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6c6f9b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nv_model=MultinomialNB()\n",
    "nv_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7394e991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5764675143703187"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nv_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ff03da5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=nv_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "388887b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hates running day']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_text='he hates running all day'\n",
    "sample_text=clean_text(sample_text)\n",
    "sample_text=[sample_text]\n",
    "sample_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7198fe47",
   "metadata": {},
   "outputs": [],
   "source": [
    "vect=cv.transform(sample_text).toarray()\n",
    "emotion=nv_model.predict(vect)\n",
    "emotion[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4e4479dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion[0]\n",
    "preb_proba=nv_model.predict_proba(vect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "422b5842",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_for_all=dict(zip(nv_model.classes_,preb_proba[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "934cf0ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(pred_for_all,index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a0a97b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.plotting._core.PlotAccessor object at 0x00000225862A1BA0>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "be62ecc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='bar', title = 'Scores of emotion', colormap = 'viridis');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "feb74cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle_out = open(\"Emotion_Detector.pkl\", \"wb\")\n",
    "pickle.dump(nv_model, pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19f8f2a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}