sentiment_analysis_602_final_project (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "04104b4e"
   },
   "source": [
    "# Tweeter sentiment Analysis\n",
    "\n",
    "- The notebook is dedicated to the covid-19 tweet sentiment analysis.\n",
    "- The data is taken from the source is - https://www.kaggle.com/heyytanay/covidindia-tweets-with-sentiment\n",
    "\n",
    "- The data consists of tweets which contained the word \"covidindia\" from the beginning of the pandemic to 28-04-2021. Along with the tweet and it's metadata columns, there's also a sentiment column which has 2 possible values: NEGATIVE and POSITIVE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d36a0224"
   },
   "source": [
    "## 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J9nU0gw6whTO",
    "outputId": "e7f4ae77-e8f9-47ad-e97a-34e0731d9086"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting fasttext\n",
      "  Downloading fasttext-0.9.2.tar.gz (68 kB)\n",
      "\u001b[?25l\r",
      "\u001b[K     |████▊                           | 10 kB 22.7 MB/s eta 0:00:01\r",
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      "\u001b[K     |████████████████████████████████| 68 kB 4.7 MB/s \n",
      "\u001b[?25hCollecting pybind11>=2.2\n",
      "  Using cached pybind11-2.8.1-py2.py3-none-any.whl (208 kB)\n",
      "Requirement already satisfied: setuptools>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from fasttext) (57.4.0)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from fasttext) (1.19.5)\n",
      "Building wheels for collected packages: fasttext\n",
      "  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for fasttext: filename=fasttext-0.9.2-cp37-cp37m-linux_x86_64.whl size=3122730 sha256=09c18c2d91214590558d018903d2ba08fa9661fe231df75d57e0a423a6863b02\n",
      "  Stored in directory: /root/.cache/pip/wheels/4e/ca/bf/b020d2be95f7641801a6597a29c8f4f19e38f9c02a345bab9b\n",
      "Successfully built fasttext\n",
      "Installing collected packages: pybind11, fasttext\n",
      "Successfully installed fasttext-0.9.2 pybind11-2.8.1\n"
     ]
    }
   ],
   "source": [
    "!pip install fasttext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "18fb4d70",
    "outputId": "bb3a2d42-8bb5-43dc-d515-3548595df88b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import os\n",
    "import re\n",
    "import nltk\n",
    "import string\n",
    "import operator\n",
    "import fasttext\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "from gensim.parsing.preprocessing import remove_stopwords\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score, classification_report\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "nltk.download('stopwords')\n",
    "nltk.download('vader_lexicon')\n",
    "stopwords = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sSIDd2A8I2MB"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.backend import clear_session\n",
    "from tensorflow.keras.callbacks import EarlyStopping \n",
    "from tensorflow.keras.layers import Dense, Input, Dropout, Flatten, Embedding, SpatialDropout1D, LSTM, Activation, Bidirectional, Concatenate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a8c54c8b"
   },
   "source": [
    "## 2. Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "id": "972bb484",
    "outputId": "b50abf97-4ce1-426e-dd17-c1bcc491f338"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the original dataset is (9655, 10).\n"
     ]
    },
    {
     "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>created_at</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>tweet</th>\n",
       "      <th>language</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-27 23:55:06 UTC</td>\n",
       "      <td>23772644</td>\n",
       "      <td>bbcindonesia</td>\n",
       "      <td>BBC News Indonesia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Varian baru Covid India sudah masuk Indonesia,...</td>\n",
       "      <td>in</td>\n",
       "      <td>15</td>\n",
       "      <td>[]</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-27 23:35:39 UTC</td>\n",
       "      <td>17128975</td>\n",
       "      <td>cnnindonesia</td>\n",
       "      <td>CNN Indonesia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Badai Covid India hingga Junta Myanmar Pikir-p...</td>\n",
       "      <td>in</td>\n",
       "      <td>0</td>\n",
       "      <td>['kilasinternasional']</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-27 23:18:00 UTC</td>\n",
       "      <td>61377303</td>\n",
       "      <td>lintas_mnctv</td>\n",
       "      <td>Lintas_MNCTV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peringatan WHO: Krisis Covid India Bisa Makin ...</td>\n",
       "      <td>in</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-27 23:16:27 UTC</td>\n",
       "      <td>576348125</td>\n",
       "      <td>nagma_morarji</td>\n",
       "      <td>Nagma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>German Army working around the clock to help I...</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>['covidemergency2021', 'covidsecondwaveinindia...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-27 22:04:08 UTC</td>\n",
       "      <td>559652727</td>\n",
       "      <td>pkashwan</td>\n",
       "      <td>Prakash Kashwan प्रकाश कसवाँ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TBC, this is an old report. The point is that ...</td>\n",
       "      <td>en</td>\n",
       "      <td>0</td>\n",
       "      <td>['covidindiainfo', 'covidindia2021', 'covidind...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                created_at  ...  sentiment\n",
       "0  2021-04-27 23:55:06 UTC  ...   NEGATIVE\n",
       "1  2021-04-27 23:35:39 UTC  ...   NEGATIVE\n",
       "2  2021-04-27 23:18:00 UTC  ...   NEGATIVE\n",
       "3  2021-04-27 23:16:27 UTC  ...   NEGATIVE\n",
       "4  2021-04-27 22:04:08 UTC  ...   NEGATIVE\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"tweets.csv\") ## reading dataset\n",
    "print(\"Shape of the original dataset is {}.\".format(df.shape)) ## getting shape\n",
    "df.head() ## getting the top 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ab54819e"
   },
   "source": [
    "- The dataset consists of 10 columns. Each of them represents the details about the tweets such as user_id, username, language on which tweet has been posted etc.\n",
    "- We are going to deal with only english language here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "721a0b47"
   },
   "source": [
    "### 2.1 Language distribution and selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c60a25b8",
    "outputId": "6191f12c-91f5-40b6-e0fa-f5dd7b908939",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "en     5825\n",
       "hi     1265\n",
       "ta     1116\n",
       "und     333\n",
       "bn      215\n",
       "ml      168\n",
       "mr      165\n",
       "kn      164\n",
       "te       89\n",
       "gu       74\n",
       "it       45\n",
       "in       43\n",
       "ur       33\n",
       "or       28\n",
       "es       21\n",
       "pt       21\n",
       "tl       12\n",
       "et        8\n",
       "pa        5\n",
       "fr        4\n",
       "ar        4\n",
       "nl        3\n",
       "de        3\n",
       "ca        3\n",
       "da        1\n",
       "ja        1\n",
       "cs        1\n",
       "no        1\n",
       "lt        1\n",
       "ro        1\n",
       "ne        1\n",
       "lv        1\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## langauge distributions\n",
    "df[\"language\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5cb0124f",
    "outputId": "15e5ffbc-46ae-40b8-e9a0-4c1cb75e71e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after selecting only english langauge is (5825, 10).\n",
      "The percentage of english tweets present in the entire dataset is 60.331.\n"
     ]
    }
   ],
   "source": [
    "## selecting only english language\n",
    "df_m1 = df[df[\"language\"] == \"en\"]\n",
    "\n",
    "## shape after getting the only language\n",
    "print(\"Shape after selecting only english langauge is {}.\".format(df_m1.shape))\n",
    "\n",
    "## percentage of english sentence present in the entire dataset\n",
    "percent = round(((df_m1.shape[0]/df.shape[0]) * 100),3)\n",
    "print(\"The percentage of english tweets present in the entire dataset is {}.\".format(percent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ebe1d788",
    "outputId": "6669c864-6032-46b6-c917-ed2f9e25858c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset is (5825, 2).\n"
     ]
    }
   ],
   "source": [
    "## though there are 10 columns along with the sentiments, the columns which we are going to analyse for the analysis \n",
    "## will be the tweets and the sentiments\n",
    "## selecting the two columns\n",
    "df_m2 = df_m1[[\"tweet\", \"sentiment\"]]\n",
    "print(\"Shape of the dataset is {}.\".format(df_m2.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LNo8GGO622r4"
   },
   "source": [
    "### 2.2 Hypothesis Testing\n",
    "\n",
    "The tweets present in the dataset is done from various sources such as news channels, organizations, and normal users. After looking into tweets I can say most of the tweets are of negative sentiments. There are no difference in sentiments between any individual user or any news organization.\n",
    "\n",
    "#### Assumption : There is no difference in negative sentiment scores, tweeted by news organization and individual users.\n",
    "\n",
    "- alpha = 0.05 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CY5sGSRZ26WX"
   },
   "outputs": [],
   "source": [
    "## selecting handfull of new organization to get tweets\n",
    "news_agency_org = ['Hindustan Times', 'Business Insider India🇮🇳', 'moneycontrol', 'ET NOW', 'Milaap', 'IndiaToday', 'CARE International',\n",
    "                   'The Pioneer', 'India Ahead News', 'Business Today', 'ETimes', 'FinancialXpress', 'Zee News English', 'Business Standard',\n",
    "                   'CNBC-TV18', 'Oneindia News', 'Forbes India', 'SportsFlashes', 'OTV', 'India TV', 'Change.org Australia', 'Amnesty International',\n",
    "                   'NDTV', 'The New Indian Express', 'GOODTIMES', 'The London Economic', 'TOI Mumbai', 'Advertising and Media Insider India',\n",
    "                   'Times of Oman', 'Bangalore Mirror', 'ETHealthWorld', 'National Herald', 'The Telegraph', 'BritAsia TV', 'Dynamite News']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3X3BteWi5Jii"
   },
   "outputs": [],
   "source": [
    "## generating sentiment scores\n",
    "vader = SentimentIntensityAnalyzer()\n",
    "scores = df_m1['tweet'].apply(vader.polarity_scores).tolist()\n",
    "df_m1['Score'] = scores\n",
    "\n",
    "## getting individual scores\n",
    "neg_list = []; neu_list = []; pos_list = []; compound_list = []\n",
    "for i in df_m1['Score']:\n",
    "    neg_list.append(i['neg'])\n",
    "    neu_list.append(i['neu'])\n",
    "    pos_list.append(i['pos'])\n",
    "    compound_list.append(i['compound'])\n",
    "df_m1['Neg'] = neg_list; df_m1['Neu'] = neu_list\n",
    "df_m1['Pos'] = pos_list; df_m1['Compound'] = compound_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OVs81i_w6DeJ",
    "outputId": "a919e544-037b-410f-eb5d-16bd3567eed9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-8.344472987045567, pvalue=1.1011160917559113e-16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind, norm\n",
    "\n",
    "## to avoid the outliers issue in speed we are taking the speed_df as created above.\n",
    "news_tweets = df_m1[df_m1['name'].isin(news_agency_org)]\n",
    "indi_tweets = df_m1[~df_m1['name'].isin(news_agency_org)]\n",
    "\n",
    "nt = news_tweets[\"Neg\"].values\n",
    "indt = indi_tweets[\"Neg\"].values\n",
    "\n",
    "ttest_ind(nt, indt, equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lf0GDqlN6zis"
   },
   "source": [
    "* As p-value is very very less than alpha, that means we reject the null hypothesis using t-test.\n",
    "* That mean there is a significant difference in negative sentiment scores,tweeted by news organization and individual users.\n",
    "* That means tweets done by news agency has less negativity in their tweets as compared to individual users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bb9f7074"
   },
   "source": [
    "### 2.3 Target Label Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "c7e082a5",
    "outputId": "1ebc322f-8df8-4b76-f475-ab86472ef85a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## using pie chart to analyse the sentiment labels present in the dataset\n",
    "plot = df_m2['sentiment'].value_counts(normalize=True).plot(kind='pie', autopct=\"%.1f\", figsize=(7,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b8592e50"
   },
   "source": [
    "- The dataset is highly imbalance and only 19% of the data belongs to class positive and rest tweets are of negative sentiment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "69d54ce0"
   },
   "source": [
    "### 2.4 null value analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "12d4bb00",
    "outputId": "92935b5c-22a2-4e76-8fa4-087ed339a432"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet        0\n",
       "sentiment    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m2.isna().sum() ## checking for null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "09674433"
   },
   "source": [
    "- There are no null values present in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c914a691"
   },
   "source": [
    "### 2.5 duplicate data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "f4358598",
    "outputId": "f4a9ce63-25e6-4b98-e4b8-83e2422363bb",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>National Conference president @OmarAbdullah ha...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>Three more Haryana districts —Sonepat, Karnal ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Delhi's positivity rate has come down from mor...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>Covid: India sets another infection record as ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>The US has decided to deploy “more supplies an...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>#CovidIndia | Amid an unprecedented surge in C...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>Amid a surge in Covid-19 cases across Jammu an...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>The Union government has scrapped basic custom...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>With ‘We Will Win’ Spirit, RSS Asks Its Swayam...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3316</th>\n",
       "      <td>Details of #COVID19Vaccination registration fo...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323</th>\n",
       "      <td>Pregnant &amp;amp; Lactating women have not been p...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>As per @MoHFW_INDIA Precautions and Contraindi...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3361</th>\n",
       "      <td>Yes, you can get vaccinated with 2nd #COVIDVac...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3782</th>\n",
       "      <td>India is one of the largest importers of the w...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3799</th>\n",
       "      <td>Even during COVID, India saw a rise in exports...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3938</th>\n",
       "      <td>During COVID, India found permanent solutions ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3939</th>\n",
       "      <td>During COVID, India found permanent solutions ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3940</th>\n",
       "      <td>During COVID, India found permanent solutions ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3943</th>\n",
       "      <td>During COVID, India found permanent solutions ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3946</th>\n",
       "      <td>During COVID, India found permanent solutions ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4040</th>\n",
       "      <td>In testing times of COVID, India has got recor...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4127</th>\n",
       "      <td>#LifeSciences — Can a post-COVID India-Austral...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4134</th>\n",
       "      <td>#LifeSciences — Can a post-COVID India-Austral...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4456</th>\n",
       "      <td>During COVID, India has attracted foreign inve...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4457</th>\n",
       "      <td>During COVID, India has attracted foreign inve...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4737</th>\n",
       "      <td>Thousands of companies mull China exit after C...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4827</th>\n",
       "      <td>.@TCS' work-from-home (WFH) model has set new ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4855</th>\n",
       "      <td>Thousands of companies mull China exit after C...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4930</th>\n",
       "      <td>Despite COVID, India-Nepal Border Row Takes a ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4948</th>\n",
       "      <td>☝️ This is a part of an ongoing series 'The Ec...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5258</th>\n",
       "      <td>Thousands of companies mull China exit after C...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  tweet sentiment\n",
       "262   National Conference president @OmarAbdullah ha...  NEGATIVE\n",
       "368   Three more Haryana districts —Sonepat, Karnal ...  NEGATIVE\n",
       "1268  Delhi's positivity rate has come down from mor...  NEGATIVE\n",
       "1308  Covid: India sets another infection record as ...  NEGATIVE\n",
       "1353  The US has decided to deploy “more supplies an...  NEGATIVE\n",
       "1399  #CovidIndia | Amid an unprecedented surge in C...  NEGATIVE\n",
       "1464  Amid a surge in Covid-19 cases across Jammu an...  NEGATIVE\n",
       "1478  The Union government has scrapped basic custom...  NEGATIVE\n",
       "1799  With ‘We Will Win’ Spirit, RSS Asks Its Swayam...  POSITIVE\n",
       "3316  Details of #COVID19Vaccination registration fo...  NEGATIVE\n",
       "3323  Pregnant &amp; Lactating women have not been p...  NEGATIVE\n",
       "3359  As per @MoHFW_INDIA Precautions and Contraindi...  NEGATIVE\n",
       "3361  Yes, you can get vaccinated with 2nd #COVIDVac...  NEGATIVE\n",
       "3782  India is one of the largest importers of the w...  POSITIVE\n",
       "3799  Even during COVID, India saw a rise in exports...  POSITIVE\n",
       "3938  During COVID, India found permanent solutions ...  NEGATIVE\n",
       "3939  During COVID, India found permanent solutions ...  NEGATIVE\n",
       "3940  During COVID, India found permanent solutions ...  NEGATIVE\n",
       "3943  During COVID, India found permanent solutions ...  NEGATIVE\n",
       "3946  During COVID, India found permanent solutions ...  NEGATIVE\n",
       "4040  In testing times of COVID, India has got recor...  POSITIVE\n",
       "4127  #LifeSciences — Can a post-COVID India-Austral...  POSITIVE\n",
       "4134  #LifeSciences — Can a post-COVID India-Austral...  POSITIVE\n",
       "4456  During COVID, India has attracted foreign inve...  POSITIVE\n",
       "4457  During COVID, India has attracted foreign inve...  POSITIVE\n",
       "4737  Thousands of companies mull China exit after C...  NEGATIVE\n",
       "4827  .@TCS' work-from-home (WFH) model has set new ...  NEGATIVE\n",
       "4855  Thousands of companies mull China exit after C...  NEGATIVE\n",
       "4930  Despite COVID, India-Nepal Border Row Takes a ...  NEGATIVE\n",
       "4948  ☝️ This is a part of an ongoing series 'The Ec...  NEGATIVE\n",
       "5258  Thousands of companies mull China exit after C...  NEGATIVE"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m2[df_m2.duplicated()] ## checking for duplicate rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7f94cf19"
   },
   "source": [
    "- So there are duplicate tweets present in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cbffd0a8",
    "outputId": "e6beee22-f7cc-4684-8ebf-7d9147d6fcfc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape after dropping the duplicate rows from the dataset is (5794, 2).\n"
     ]
    }
   ],
   "source": [
    "## checking the shape and removing them\n",
    "df_m3 = df_m2.drop_duplicates()\n",
    "print(\"Shape after dropping the duplicate rows from the dataset is {}.\".format(df_m3.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b6cf122d"
   },
   "source": [
    "## 3. Text Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5a4827d"
   },
   "source": [
    "* Below are preprocessing techniques going to be used.\n",
    "\n",
    "1. lowercasing the text\n",
    "2. removal of retweets, url links, emoticons etc.\n",
    "3. removal od stopwords\n",
    "4. removal of punctuations\n",
    "5. stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e0bfff26"
   },
   "outputs": [],
   "source": [
    "def retweet_cleaning(text):\n",
    "    \"\"\"\n",
    "    This function cleans all the re tweet tags, website tags along with al emojis, symbols and flags.\n",
    "    \"\"\"\n",
    "    \n",
    "    ## removal of retweets, hypertext, website tags\n",
    "    text = re.sub(r'RT\\s+@(.*?):', \"\", text)\n",
    "    text = re.sub(r'RT\\s+:', \"\", text)\n",
    "    text = re.sub(r'https?://\\S+|www\\.\\S+', '', text)\n",
    "    text = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', text)\n",
    "    text = re.sub(r\"@\\w+\\s+\", \"\", text)\n",
    "    \n",
    "    #emoji removal\n",
    "    emoji = re.compile(\n",
    "        u\"(\\ud83d[\\ude00-\\ude4f])|\"  # emoticons\n",
    "        u\"(\\ud83c[\\udf00-\\uffff])|\"  # symbols & pictographs (1 of 2)\n",
    "        u\"(\\ud83d[\\u0000-\\uddff])|\"  # symbols & pictographs (2 of 2)\n",
    "        u\"(\\ud83d[\\ude80-\\udeff])|\"  # transport & map symbols\n",
    "        u\"(\\ud83c[\\udde0-\\uddff])\"  # flags (iOS)\n",
    "    \"+\", flags=re.UNICODE)\n",
    "    text = emoji.sub(r'', text)\n",
    "    \n",
    "    ## lowercasing the text\n",
    "    text = text.lower()\n",
    "    \n",
    "    ##removal of #tags\n",
    "    text = re.sub(r\"#\\w+\", \"\", text)\n",
    "    \n",
    "    ## removing punctuation\n",
    "    text = re.sub(r'-', ' ', text)\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)\n",
    "    \n",
    "    ## removal of stopwords\n",
    "    text = remove_stopwords(text)\n",
    "    \n",
    "    ## removing numbers\n",
    "    text = re.sub(\" \\d+\", \"\", text)\n",
    "    \n",
    "    ## stemming of words\n",
    "    ps = PorterStemmer()\n",
    "    text = \" \".join(ps.stem(val) for val in text.split())\n",
    "    \n",
    "    ## removing white spaces\n",
    "    text = re.sub(\"\\s\\s\\s+\", \"\", text)\n",
    "    text = text.strip()\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9faf4436"
   },
   "outputs": [],
   "source": [
    "## preprocessing \n",
    "df_m3[\"preprocessed_tweet\"] = df_m3[\"tweet\"].apply(lambda x : retweet_cleaning(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "843d96b4",
    "outputId": "0f4b1881-e259-4e71-be90-29d45e1b676a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>preprocessed_tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>German Army working around the clock to help I...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>german armi work clock help india covid crisi ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TBC, this is an old report. The point is that ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>tbc old report point indian govern known use n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>\"Most government-linked disinformation efforts...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>govern link disinform effort focus domest rese...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>The news out of #India is devastating. #CovidI...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>news devast hurt countri bay area help gilroy ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Second COVID wave has hampered India's economi...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>second covid wave hamper india econom recoveri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9650</th>\n",
       "      <td>#COVID OR no Covid, India is not China or USA....</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>covid india china usa burgeon imagin medic doc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9651</th>\n",
       "      <td>Watch #CoronaVirusUpdate from Gandhi Hospital ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>watch gandhi hospit peopl quarantin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9652</th>\n",
       "      <td>#CoronavirusUpdate || A 15 member team from #T...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>15 member team visit studi dealt posit case</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9653</th>\n",
       "      <td>Ye gads ... Now NDTV’s  @harsha_ndtv reports a...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>ye gad ndtv report italian tourist test posit ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9654</th>\n",
       "      <td>#JUSTIN || #Telangana man who returned from #D...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>man return test posit kept quarantin gandhi me...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5794 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  tweet  ...                                 preprocessed_tweet\n",
       "3     German Army working around the clock to help I...  ...  german armi work clock help india covid crisi ...\n",
       "4     TBC, this is an old report. The point is that ...  ...  tbc old report point indian govern known use n...\n",
       "5     \"Most government-linked disinformation efforts...  ...  govern link disinform effort focus domest rese...\n",
       "6     The news out of #India is devastating. #CovidI...  ...  news devast hurt countri bay area help gilroy ...\n",
       "7     Second COVID wave has hampered India's economi...  ...  second covid wave hamper india econom recoveri...\n",
       "...                                                 ...  ...                                                ...\n",
       "9650  #COVID OR no Covid, India is not China or USA....  ...  covid india china usa burgeon imagin medic doc...\n",
       "9651  Watch #CoronaVirusUpdate from Gandhi Hospital ...  ...                watch gandhi hospit peopl quarantin\n",
       "9652  #CoronavirusUpdate || A 15 member team from #T...  ...        15 member team visit studi dealt posit case\n",
       "9653  Ye gads ... Now NDTV’s  @harsha_ndtv reports a...  ...  ye gad ndtv report italian tourist test posit ...\n",
       "9654  #JUSTIN || #Telangana man who returned from #D...  ...  man return test posit kept quarantin gandhi me...\n",
       "\n",
       "[5794 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2f86b82e"
   },
   "source": [
    "- As we can above, after preprocess the text quality has been imporved.\n",
    "- I have lower cased the data to avoid the case related word uniqueness, also applied potterstemmer to bring all the similar meaning words to it's root/stem words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f16cc220"
   },
   "source": [
    "## 4. after preprocessing text analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a04a5f18",
    "outputId": "b5514c4e-8be9-425b-a25d-24f69de39828",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       7\n",
       "1      53\n",
       "2      87\n",
       "3     136\n",
       "4     233\n",
       "5     217\n",
       "6     276\n",
       "7     380\n",
       "8     409\n",
       "9     428\n",
       "10    445\n",
       "11    441\n",
       "12    408\n",
       "13    328\n",
       "14    276\n",
       "15    248\n",
       "16    243\n",
       "17    216\n",
       "18    184\n",
       "19    153\n",
       "20    162\n",
       "21    127\n",
       "22     83\n",
       "23     68\n",
       "24     70\n",
       "25     40\n",
       "26     26\n",
       "27     15\n",
       "28     18\n",
       "29     11\n",
       "30      2\n",
       "31      1\n",
       "32      3\n",
       "Name: total_words, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## getting total words present in each sentence so that we can remove where there are no more words left\n",
    "df_m3[\"total_words\"] = df_m3[\"preprocessed_tweet\"].apply(lambda x : len(x.split()))\n",
    "df_m3[\"total_words\"].value_counts().sort_index() ## number of words present in the sentence in how many sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "df680394",
    "outputId": "343105d1-5d76-4246-bf67-d6232ceb0129"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>preprocessed_tweet</th>\n",
       "      <th>total_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>I’ll be anchoring on @BBCWorld from 0600GMT. #...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>ill anchoringgmt</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Very scaled-down wedding, from the looks of it...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>scale wed look</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>BADLY needs Oxygen #CovidIndia</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>badli need oxygen</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>And this goes on and on and on.... #CovidIndia...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>goe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Everything you wanted to know about #vaccinepr...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>want know</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9592</th>\n",
       "      <td>Are you working from home? #covidindia #Corona...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>work home</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9628</th>\n",
       "      <td>#CoronoVirus #India Current Count is 125 #COVI...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>current count</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9633</th>\n",
       "      <td>Please understand what this 👇🏼 can mean. #covi...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>understand mean</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9642</th>\n",
       "      <td>#Kerala setting a great example #COVID19 #covi...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>set great exampl</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9644</th>\n",
       "      <td>#CancelEverything Tweets Flood Twitter! #Cance...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>tweet flood twitter</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>283 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  tweet  ... total_words\n",
       "25    I’ll be anchoring on @BBCWorld from 0600GMT. #...  ...           2\n",
       "57    Very scaled-down wedding, from the looks of it...  ...           3\n",
       "63                       BADLY needs Oxygen #CovidIndia  ...           3\n",
       "71    And this goes on and on and on.... #CovidIndia...  ...           1\n",
       "131   Everything you wanted to know about #vaccinepr...  ...           2\n",
       "...                                                 ...  ...         ...\n",
       "9592  Are you working from home? #covidindia #Corona...  ...           2\n",
       "9628  #CoronoVirus #India Current Count is 125 #COVI...  ...           2\n",
       "9633  Please understand what this 👇🏼 can mean. #covi...  ...           2\n",
       "9642  #Kerala setting a great example #COVID19 #covi...  ...           3\n",
       "9644  #CancelEverything Tweets Flood Twitter! #Cance...  ...           3\n",
       "\n",
       "[283 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m3[df_m3[\"total_words\"] < 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "79901551"
   },
   "source": [
    "- As we can after text preprocessing we found out there are 283 sentences present where the number of words are less than 4.\n",
    "- I am keeping 4 as thereshold for the analysis, and removing all the sentences which are less than 4, because it makes no sense as a sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "81dcd447",
    "outputId": "9a224409-0f1d-4348-904b-ae2cdb77e192"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of the dataset after selecting more than 3 words for a preprocessed tweet is (5511, 4).\n"
     ]
    }
   ],
   "source": [
    "## selecting sentences more than 3 words\n",
    "df_m4 = df_m3[df_m3[\"total_words\"] > 3]\n",
    "print(\"shape of the dataset after selecting more than 3 words for a preprocessed tweet is {}.\".format(df_m4.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4a6_1SKyGbCT"
   },
   "outputs": [],
   "source": [
    "## encoding of output data\n",
    "df_m4['label'] = df_m4['sentiment'].map({\"NEGATIVE\":0, \"POSITIVE\":1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d57c9c02"
   },
   "source": [
    "## 5. word clouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7667cc67"
   },
   "outputs": [],
   "source": [
    "def wordClouds(text):\n",
    "    \"\"\"\n",
    "    This function creates the word cloud by taking all words into consideration.\n",
    "    \"\"\"\n",
    "    #Creating a list of words present in the text\n",
    "    corpus = [line.split() for line in text.values]\n",
    "    ## getting all sentences in one text\n",
    "    all_words = \" \".join(i for line in corpus for i in line)\n",
    "            \n",
    "    #plotting wordcloud\n",
    "    wordcloud = WordCloud(width = 1200, height = 1200, stopwords = set(STOPWORDS), min_font_size = 30,\n",
    "                          background_color = \"black\").generate(all_words)\n",
    "    #display image\n",
    "    plt.figure(figsize=(15,10))\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 575
    },
    "id": "f2e1b9c0",
    "outputId": "0d30630c-7969-42dc-9989-878b2c952bf4"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordClouds(df_m4['preprocessed_tweet']) ## plotting word clouds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "30479640"
   },
   "source": [
    "- As the tweets are related to covid-19, we can see the words like help, case, test, death, report etc. along with live, update which shows the diversity of the tweets belong to various aspects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0j60h2wtV0Jg"
   },
   "source": [
    "## 6. Uni, Bi, and Tri gram analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OlQsL3pnV5r4"
   },
   "outputs": [],
   "source": [
    "#Source: - https://www.kaggle.com/sudalairajkumar/simple-exploration-notebook-qiqc\n",
    "\"\"\"function to generate ngrams and plot the data\"\"\"\n",
    "def generate_ngrams(text, n_gram):\n",
    "    #generating token\n",
    "    word = [token for token in str(text).lower().split(\" \") if token != \"\"]\n",
    "    #generating ngrams\n",
    "    ngrams = zip(*[word[i:] for i in range(n_gram)])\n",
    "    return [\" \".join(ngram) for ngram in ngrams]\n",
    "\n",
    "def freq_ngrams(text, n_gram, range):\n",
    "    \"\"\"parameters : text, number of ngrams(1,2,3), range(top ngrams)\n",
    "       return : plot of word vs no of occurance\n",
    "    \"\"\"\n",
    "\n",
    "    freq_dict = defaultdict(int)\n",
    "    for sent in tqdm(text):\n",
    "        for w in generate_ngrams(sent,n_gram):\n",
    "            freq_dict[w] += 1\n",
    "    freq_dict = dict(freq_dict)\n",
    "    #sorting dict to descending order\n",
    "    qstn_txt = pd.DataFrame(sorted(freq_dict.items(), key=lambda x: x[1], reverse=True))\n",
    "    qstn_txt.columns = [\"word\", \"wordcount\"]\n",
    "    #Plotting the data\n",
    "    data = [go.Bar(x=qstn_txt.word[0:range], y=qstn_txt.wordcount[0:range])]\n",
    "    iplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 574,
     "referenced_widgets": [
      "ac5c6a0425e14f5897edb8628127a150",
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    "id": "ByXb_nmOWTxt",
    "outputId": "69758b1c-c32d-4732-acb3-a23321dc1786"
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   "outputs": [
    {
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      "text/plain": [
       "  0%|          | 0/5511 [00:00<?, ?it/s]"
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     "output_type": "display_data"
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       "  0%|          | 0/5511 [00:00<?, ?it/s]"
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       "</body>\n",
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   ],
   "source": [
    "freq_ngrams(df_m4['preprocessed_tweet'], n_gram=3, range=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6e359087"
   },
   "source": [
    "## 7. Splitting of Data"
   ]
  },
  {
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    "colab": {
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    },
    "id": "a8c99831",
    "outputId": "030a62de-5f97-43e4-fd74-eff6b00aae56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEGATIVE    4450\n",
       "POSITIVE    1061\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m4[\"sentiment\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d56793a4",
    "outputId": "69e12fac-102f-4835-8639-d9482df2f77f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4408,), (1103,), (4408,), (1103,))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## getting input and output data\n",
    "X = df_m4['preprocessed_tweet'].values\n",
    "y = df_m4[\"label\"]\n",
    "\n",
    "### splitting of data \n",
    "xtrain, xtest, ytrain, ytest  = train_test_split(X, y, stratify = y, ## keeping output %age same in both train and test data\n",
    "                                                 test_size=0.20, ## splitting into 80:20 percent\n",
    "                                                 random_state=42)\n",
    "\n",
    "xtrain.shape, xtest.shape, ytrain.shape, ytest.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ac3a0d35"
   },
   "source": [
    "## 8. data encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fb172cf2",
    "outputId": "dd36d0d7-260d-4438-fe2d-81205bd4488d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4408, 20000), (1103, 20000))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "## setting up encoder\n",
    "encoder = TfidfVectorizer(ngram_range=(1,3),max_features=20000, strip_accents='unicode')\n",
    "encoder.fit(xtrain) ## fitting and transformation of training and test data\n",
    "\n",
    "x_train = encoder.transform(xtrain.astype('U'))\n",
    "x_test = encoder.transform(xtest.astype('U'))\n",
    "\n",
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8b610eee"
   },
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d9f73d79"
   },
   "outputs": [],
   "source": [
    "def output_scores(ytrain_original, ytrain_predicted, ytest_original, ytest_predicted):\n",
    "    \n",
    "    \"\"\" This function produces fscore output along with precision, and recall.\"\"\"\n",
    "    ## output scores and overfitting\n",
    "    train_acc = accuracy_score(ytrain_original, ytrain_predicted)\n",
    "    test_acc = accuracy_score(ytest_original, ytest_predicted)\n",
    "    test_fscore = f1_score(ytest_original, ytest_predicted)\n",
    "    test_precision = precision_score(ytest_original, ytest_predicted)\n",
    "    test_recall = recall_score(ytest_original, ytest_predicted)\n",
    "    \n",
    "    return train_acc, test_acc, test_fscore, test_precision, test_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ae6f57bb"
   },
   "outputs": [],
   "source": [
    "def conf_matrix(test_original, test_predicted, title):\n",
    "\n",
    "    \"\"\"This function produces confusion matrix of given predicted output.\"\"\"\n",
    "    fig, ax = plot_confusion_matrix(conf_mat = confusion_matrix(test_original, test_predicted), figsize=(5, 5))\n",
    "    plt.title(title, fontsize=20)\n",
    "    plt.ylabel(\"True Output\", fontsize=15)\n",
    "    plt.xlabel(\"Predicted Output\", fontsize=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9c2ccf1e"
   },
   "source": [
    "## 9. Naive Bayes\n",
    "\n",
    "Naive Bayes classifiers are a collection of classification algorithms based on Bayes' Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1f8469d2",
    "outputId": "ec8bff92-f2a0-4bdc-abcc-04e458c56d93"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "Best cross-validation score: 0.83\n",
      "Best parameters:  {'alpha': 1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "clf_nb = MultinomialNB()  ## creating object\n",
    "param_ = {'alpha': [10 ** x for x in range(-3, 3)]}  ## getting parameters\n",
    "\n",
    "## grid search cv to get the optimal output\n",
    "clf = GridSearchCV(clf_nb, param_, cv=5, scoring='f1_micro', return_train_score=True, \n",
    "                   verbose=True, n_jobs=-1).fit(x_train, ytrain)\n",
    "\n",
    "\n",
    "print(\"Best cross-validation score: {:.2f}\".format(clf.best_score_))\n",
    "print(\"Best parameters: \", clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2d537b4d",
    "outputId": "3f9a94f2-1fb0-416d-8d97-92a91f860b67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train and test accuracy are 0.8478 and 0.8341 respectively.\n",
      "test fscore, precision and recall are 0.2531, 0.9394, and 0.1462.\n"
     ]
    }
   ],
   "source": [
    "clf_nb = MultinomialNB(alpha = clf.best_params_['alpha']).fit(x_train, ytrain)  ## predicting with best parameters\n",
    "ypred_nb_tr = clf_nb.predict(x_train); ypred_nb_te = clf_nb.predict(x_test) ## output prediction\n",
    "\n",
    "train_acc_nb, test_acc_nb, test_fscore_nb, test_precision_nb, test_recall_nb = output_scores(ytrain, ypred_nb_tr, \n",
    "                                                                                             ytest, ypred_nb_te)\n",
    "\n",
    "print(\"train and test accuracy are {:.4f} and {:.4f} respectively.\".format(train_acc_nb, test_acc_nb))\n",
    "\n",
    "print(\"test fscore, precision and recall are {:.4f}, {:.4f}, and {:.4f}.\".format(test_fscore_nb, test_precision_nb, \n",
    "                                                                                 test_recall_nb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "f3df08a9",
    "outputId": "3112c080-960f-43dd-8a8b-8fbe3ae91e0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report Test Data for Naive Bayes:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      1.00      0.91       891\n",
      "           1       0.94      0.15      0.25       212\n",
      "\n",
      "    accuracy                           0.83      1103\n",
      "   macro avg       0.89      0.57      0.58      1103\n",
      "weighted avg       0.85      0.83      0.78      1103\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Classification Report Test Data for Naive Bayes:\\n\\n\",\n",
    "      classification_report(ytest, ypred_nb_te)) ## classification report\n",
    "\n",
    "conf_matrix(ytest, ypred_nb_te, \"confusion_matrix_naive_bayes\") ## confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hXzS3TgH_dnZ"
   },
   "source": [
    "## 10. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ByXpJm76_hzi",
    "outputId": "830eb9ac-a17d-4ab5-9ed5-6c7f101839bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 7 folds for each of 6 candidates, totalling 42 fits\n",
      "Best cross-validation score: 0.85\n",
      "Best parameters:  {'C': 10}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "## creating object\n",
    "clf_lr = LogisticRegression()\n",
    "## getting parameters\n",
    "param_ = {'C': [10 ** x for x in range(-3, 3)]}\n",
    "\n",
    "## grid search cv to get the optimal output\n",
    "clf = GridSearchCV(clf_lr, \n",
    "                   param_, \n",
    "                   cv=7, \n",
    "                   scoring='f1_micro',\n",
    "                   return_train_score=True, \n",
    "                   verbose=True, \n",
    "                   n_jobs=-1). fit(x_train, ytrain)\n",
    "\n",
    "\n",
    "print(\"Best cross-validation score: {:.2f}\".format(clf.best_score_))\n",
    "print(\"Best parameters: \", clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3WRSzY3RBBFB",
    "outputId": "d743976b-1e70-4033-ec14-51d70446a4cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train and test accuracy are 0.9948 and 0.8649 respectively.\n",
      "test fscore, precision and recall are 0.5706, 0.7333, and 0.4670.\n"
     ]
    }
   ],
   "source": [
    "clf_lr = LogisticRegression(C = clf.best_params_['C']).fit(x_train, ytrain)  ## predicting with best parameters\n",
    "ypred_lr_tr = clf_lr.predict(x_train); ypred_lr_te = clf_lr.predict(x_test) ## output prediction\n",
    "\n",
    "train_acc_lr, test_acc_lr, test_fscore_lr, test_precision_lr, test_recall_lr = output_scores(ytrain, ypred_lr_tr, \n",
    "                                                                                             ytest, ypred_lr_te)\n",
    "\n",
    "print(\"train and test accuracy are {:.4f} and {:.4f} respectively.\".format(train_acc_lr, test_acc_lr))\n",
    "\n",
    "print(\"test fscore, precision and recall are {:.4f}, {:.4f}, and {:.4f}.\".format(test_fscore_lr, test_precision_lr, \n",
    "                                                                                 test_recall_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "0ZoiZ0KSBY2j",
    "outputId": "9fb3aec9-e0ac-4a88-bc2e-d091721108d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report Test Data for Logistic Regression :\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.96      0.92       891\n",
      "           1       0.73      0.47      0.57       212\n",
      "\n",
      "    accuracy                           0.86      1103\n",
      "   macro avg       0.81      0.71      0.75      1103\n",
      "weighted avg       0.85      0.86      0.85      1103\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Classification Report Test Data for Logistic Regression :\\n\\n\",\n",
    "      classification_report(ytest, ypred_lr_te)) ## classification report\n",
    "\n",
    "conf_matrix(ytest, ypred_lr_te, \"confusion_matrix_logistic_regression\") ## confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FqXN54erBgnS"
   },
   "source": [
    "## 11. LSTM "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yZE8YBwiBieo"
   },
   "source": [
    "### 11.1 creating an embedding from given data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4VyMqXzHBhzB"
   },
   "outputs": [],
   "source": [
    "#creating text corpus\n",
    "sentence = \"\"\n",
    "for i in df_m4[\"preprocessed_tweet\"].values:\n",
    "    sentence += str(i)\n",
    "    sentence += \" . \"\n",
    "\n",
    "f = open(\"text_corpus.txt\", \"w\")\n",
    "f.write(sentence) \n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9duCyzHzGn1J",
    "outputId": "ad380eeb-53c2-4829-a971-31ac6b989355"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.5 s, sys: 992 ms, total: 6.49 s\n",
      "Wall time: 8.22 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "embedding_model = fasttext.train_unsupervised(\"text_corpus.txt\", minn=3, maxn=6, wordNgrams=3, lr=0.1, dim=100)\n",
    "embedding_model.save_model(\"embedding_model.bin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JXfwUCRDHalC"
   },
   "source": [
    "### 11.2 tokenization and padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "h87Pn9a4Hd8k"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "\n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts(xtrain)\n",
    "encoded_docs_train = tokenizer.texts_to_sequences(xtrain)\n",
    "encoded_docs_test = tokenizer.texts_to_sequences(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "omTz7ZXGH0Jl"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "maxlength = 25 #maximum length of a sentence to be padded\n",
    "Xtrain = pad_sequences(encoded_docs_train, maxlen = maxlength, padding='post')\n",
    "Xtest = pad_sequences(encoded_docs_test, maxlen = maxlength, padding='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AkkloiVQJrkl",
    "outputId": "c81a38a4-45e6-46e8-9054-e5f7c06bade2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 533, 1329, 1330, ...,    0,    0,    0],\n",
       "       [  75,    1,  743, ...,    0,    0,    0],\n",
       "       [ 169, 2117,  461, ...,    0,    0,    0],\n",
       "       ...,\n",
       "       [7139, 7140, 1745, ...,    0,    0,    0],\n",
       "       [7141, 7142,  530, ...,  533,  713,  713],\n",
       "       [7147,  147,   38, ...,    0,    0,    0]], dtype=int32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kAX1WcmQISHU"
   },
   "source": [
    "### 11. 3 creating embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KlAcxH1yIUWy",
    "outputId": "b26635b3-ead0-4f19-d552-3a2204e8a47e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of emdedding matrix is:  (7149, 100)\n"
     ]
    }
   ],
   "source": [
    "word_index = tokenizer.word_index #getting word index\n",
    "vocab_size = len(word_index) + 1 #getting vocab size\n",
    "embedding_matrix = np.zeros((vocab_size, 100))\n",
    "\n",
    "for word, i in word_index.items(): #creating embedding matrix\n",
    "    dat = embedding_model.get_word_vector(word)\n",
    "    if dat is not None:\n",
    "        embedding_matrix[i] = dat\n",
    "print('The shape of emdedding matrix is: ',embedding_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "soJX9UsxIkm1"
   },
   "source": [
    "### 11.4 one hot encoding of output data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PYi3f-8dIgUe"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "y_train = to_categorical(ytrain, 2)\n",
    "y_test = to_categorical(ytest, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0DeLv6TXIohY"
   },
   "source": [
    "### 11.5 LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hvu4-TH5Je_Y"
   },
   "outputs": [],
   "source": [
    "earlyStopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=0, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lFpGuecgIqWf",
    "outputId": "19fbe2c1-a874-44f4-b5ca-adbeb5ac759b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    }
   ],
   "source": [
    "inputs = Input(shape=(maxlength,), dtype='int32', name='Input_Text')\n",
    "Embedding_Layer = Embedding(vocab_size, 100, weights = [embedding_matrix], input_length = maxlength, trainable=False)(inputs)\n",
    "sp_drop = SpatialDropout1D(0.2)(Embedding_Layer)\n",
    "lstmLayer =  tf.keras.layers.LSTM(128, kernel_initializer='glorot_normal', return_sequences=True,\n",
    "                                   dropout=0.4, recurrent_dropout=0.4)(sp_drop)\n",
    "flat = Flatten()(lstmLayer)\n",
    "dropout = Dropout(0.2)(flat)\n",
    "dense = Dense(64, activation='relu')(dropout)\n",
    "output = Dense(2, activation='sigmoid')(dense)\n",
    "lstm_model = Model(inputs,output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "flV-a0J_KMmW",
    "outputId": "323d6407-5632-4979-94be-85251cd13490"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " Input_Text (InputLayer)     [(None, 25)]              0         \n",
      "                                                                 \n",
      " embedding_1 (Embedding)     (None, 25, 100)           714900    \n",
      "                                                                 \n",
      " spatial_dropout1d_1 (Spatia  (None, 25, 100)          0         \n",
      " lDropout1D)                                                     \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 25, 128)           117248    \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 3200)              0         \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 3200)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                204864    \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 2)                 130       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,037,142\n",
      "Trainable params: 322,242\n",
      "Non-trainable params: 714,900\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "lstm_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2tcirZDKI8rz",
    "outputId": "5ff3cba1-09fd-4598-a32e-7eb9405c92d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "69/69 [==============================] - 43s 100ms/step - loss: 0.4572 - accuracy: 0.8169 - val_loss: 0.4355 - val_accuracy: 0.8232\n",
      "Epoch 2/30\n",
      "69/69 [==============================] - 7s 98ms/step - loss: 0.4303 - accuracy: 0.8246 - val_loss: 0.4139 - val_accuracy: 0.8277\n",
      "Epoch 3/30\n",
      "69/69 [==============================] - 7s 95ms/step - loss: 0.4278 - accuracy: 0.8215 - val_loss: 0.4144 - val_accuracy: 0.8305\n",
      "Epoch 4/30\n",
      "69/69 [==============================] - 7s 97ms/step - loss: 0.4198 - accuracy: 0.8289 - val_loss: 0.4057 - val_accuracy: 0.8286\n",
      "Epoch 5/30\n",
      "69/69 [==============================] - 7s 97ms/step - loss: 0.4174 - accuracy: 0.8278 - val_loss: 0.4119 - val_accuracy: 0.8296\n",
      "Epoch 6/30\n",
      "69/69 [==============================] - 7s 96ms/step - loss: 0.4179 - accuracy: 0.8271 - val_loss: 0.4087 - val_accuracy: 0.8241\n",
      "Epoch 7/30\n",
      "69/69 [==============================] - 7s 99ms/step - loss: 0.4136 - accuracy: 0.8276 - val_loss: 0.4068 - val_accuracy: 0.8286\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f73a45af290>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm_model.compile(loss = \"binary_crossentropy\", optimizer = \"adam\", metrics = [\"accuracy\"]) #compile the model\n",
    "lstm_model.fit(Xtrain, y_train, batch_size=64, verbose=1, epochs=30, validation_data=(Xtest,y_test), shuffle=True,\n",
    "          callbacks = [earlyStopping]) #fitting the model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l2C8uhd4JPyi"
   },
   "source": [
    "### 11.6 Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9o-9sjoHJS0D",
    "outputId": "058e451d-f53b-4684-b32d-1c674b84823e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "138/138 [==============================] - 1s 7ms/step\n",
      "35/35 [==============================] - 0s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "## using predict attribute to get the likelihood/probabilities\n",
    "ytrain_pred_lstm = lstm_model.predict(Xtrain, verbose=1)\n",
    "ytest_pred_lstm = lstm_model.predict(Xtest, verbose=1)\n",
    "\n",
    "## getting the predicted class from the likelihoods\n",
    "ytrain_predicted_lstm = [max(enumerate(val), key=operator.itemgetter(1))[0] for val in ytrain_pred_lstm]\n",
    "ytest_predicted_lstm = [max(enumerate(val), key=operator.itemgetter(1))[0] for val in ytest_pred_lstm]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fOeG4VqSseUL",
    "outputId": "48f49ac1-1fad-49bc-daf8-a8de9776bc3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train and test accuracy are 0.8308 and 0.8286 respectively.\n",
      "test fscore, precision and recall are 0.2158, 0.8966, and 0.1226.\n"
     ]
    }
   ],
   "source": [
    "train_acc_lstm, test_acc_lstm, test_fscore_lstm, test_precision_lstm, test_recall_lstm = output_scores(ytrain, ytrain_predicted_lstm, \n",
    "                                                                                                       ytest, ytest_predicted_lstm)\n",
    "\n",
    "print(\"train and test accuracy are {:.4f} and {:.4f} respectively.\".format(train_acc_lstm, test_acc_lstm))\n",
    "\n",
    "print(\"test fscore, precision and recall are {:.4f}, {:.4f}, and {:.4f}.\".format(test_fscore_lstm, test_precision_lstm, test_recall_lstm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "kHXIYhG3P0n_",
    "outputId": "a861cb73-e3f8-4c2f-83d9-b058417a618c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report Test Data for lstm model:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      1.00      0.90       891\n",
      "           1       0.90      0.12      0.22       212\n",
      "\n",
      "    accuracy                           0.83      1103\n",
      "   macro avg       0.86      0.56      0.56      1103\n",
      "weighted avg       0.84      0.83      0.77      1103\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Classification Report Test Data for lstm model:\\n\\n\",\n",
    "      classification_report(ytest, ytest_predicted_lstm)) ## classification report\n",
    "\n",
    "conf_matrix(ytest, ytest_predicted_lstm, \"confusion_matrix_lstm_model\") ## confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5wRtsuxQQO4R"
   },
   "source": [
    "## 12. Attention Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-7yAp5DlG_Ie"
   },
   "outputs": [],
   "source": [
    "class Attention(tf.keras.layers.Layer):\n",
    "    '''\n",
    "    Class the calculates score based on the scoring_function using Bahdanu attention mechanism.\n",
    "    '''\n",
    "    def __init__(self, att_units, name=None, **kwargs):\n",
    "        super(Attention, self).__init__(name=name)\n",
    "        #number of attention units to be provided\n",
    "        self.att_units = att_units\n",
    "        super(Attention, self).__init__(**kwargs)\n",
    "        \n",
    "        # Intialize variables needed for Concat score function here\n",
    "        self.W1=tf.keras.layers.Dense(att_units)\n",
    "        self.W2=tf.keras.layers.Dense(att_units)\n",
    "        self.V=tf.keras.layers.Dense(1)\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super(Attention, self).get_config()\n",
    "        config.update({\"att_units\": self.att_units})\n",
    "        return config\n",
    "\n",
    "    def call(self,lstm_output, hidden_state):\n",
    "        \n",
    "        \"\"\"\n",
    "        hidden state shape == (batch_size, hidden size)\n",
    "        lstm output shape == (batch_size, max_length, hidden size)\n",
    "        \"\"\"\n",
    "        \n",
    "        #state_with_time_axis shape == (batch_size, 1, hidden size)\n",
    "        #we are doing this to broadcast addition along the time axis to calculate the score\n",
    "        state_with_time_axis = tf.expand_dims(hidden_state, 1)\n",
    "        \n",
    "        #score shape == (batch_size, max_length, 1)\n",
    "        #we get 1 at the last axis because we are applying score to self.V\n",
    "        #the shape of the tensor before applying self.V is (batch_size, max_length, units)\n",
    "        score = self.V(tf.nn.tanh(self.W1(state_with_time_axis) + self.W2(lstm_output)))\n",
    "\n",
    "        # attention_weights shape == (batch_size, max_length, 1)\n",
    "        attention_weights=tf.nn.softmax(score, axis=1)\n",
    "\n",
    "        # context_vector shape after sum == (batch_size, hidden_size)\n",
    "        context_vector = attention_weights*lstm_output\n",
    "        context_vector = tf.reduce_sum(context_vector, axis=1)  \n",
    "\n",
    "        return context_vector, attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GBQLTsObJLZH"
   },
   "outputs": [],
   "source": [
    "inputs = Input(shape=(maxlength,), dtype='int32', name='Input_Text')\n",
    "Embedding_Layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlength, trainable=False)(inputs)\n",
    "lstm_output, fw_state_h, fw_state_c, bw_state_h, bw_state_c = Bidirectional(LSTM(64, return_sequences=True, return_state=True))(Embedding_Layer)\n",
    "state_h = Concatenate()([fw_state_h, bw_state_h])\n",
    "context_vector, attention_weights = Attention(10)(lstm_output, state_h)\n",
    "dense = Dense(128, activation='relu', name= \"Dense_Layer\")(context_vector)\n",
    "output = Dense(2, activation='sigmoid', name= \"Output_Layer\")(dense)\n",
    "model_attention = Model(inputs,output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-0nZiSYOJZ26",
    "outputId": "c1ff75ba-66d4-4848-fa01-276ff0ac2cef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "69/69 [==============================] - 6s 23ms/step - loss: 0.4810 - accuracy: 0.8158 - val_loss: 0.4269 - val_accuracy: 0.8141\n",
      "Epoch 2/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.4281 - accuracy: 0.8221 - val_loss: 0.4216 - val_accuracy: 0.8178\n",
      "Epoch 3/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.4213 - accuracy: 0.8258 - val_loss: 0.4138 - val_accuracy: 0.8241\n",
      "Epoch 4/30\n",
      "69/69 [==============================] - 1s 10ms/step - loss: 0.4182 - accuracy: 0.8287 - val_loss: 0.4132 - val_accuracy: 0.8250\n",
      "Epoch 5/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.4178 - accuracy: 0.8296 - val_loss: 0.4136 - val_accuracy: 0.8314\n",
      "Epoch 6/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.4112 - accuracy: 0.8303 - val_loss: 0.4101 - val_accuracy: 0.8259\n",
      "Epoch 7/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.4093 - accuracy: 0.8296 - val_loss: 0.4144 - val_accuracy: 0.8259\n",
      "Epoch 8/30\n",
      "69/69 [==============================] - 1s 9ms/step - loss: 0.4155 - accuracy: 0.8280 - val_loss: 0.4049 - val_accuracy: 0.8296\n",
      "Epoch 9/30\n",
      "69/69 [==============================] - 1s 9ms/step - loss: 0.4056 - accuracy: 0.8339 - val_loss: 0.4015 - val_accuracy: 0.8323\n",
      "Epoch 10/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.4031 - accuracy: 0.8308 - val_loss: 0.4010 - val_accuracy: 0.8323\n",
      "Epoch 11/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.4025 - accuracy: 0.8344 - val_loss: 0.4127 - val_accuracy: 0.8286\n",
      "Epoch 12/30\n",
      "69/69 [==============================] - 1s 9ms/step - loss: 0.3991 - accuracy: 0.8321 - val_loss: 0.3934 - val_accuracy: 0.8296\n",
      "Epoch 13/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.3962 - accuracy: 0.8317 - val_loss: 0.3984 - val_accuracy: 0.8305\n",
      "Epoch 14/30\n",
      "69/69 [==============================] - 1s 8ms/step - loss: 0.3948 - accuracy: 0.8326 - val_loss: 0.4079 - val_accuracy: 0.8241\n",
      "Epoch 15/30\n",
      "69/69 [==============================] - 1s 9ms/step - loss: 0.3946 - accuracy: 0.8314 - val_loss: 0.4020 - val_accuracy: 0.8305\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f7426df3cd0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_attention.compile(loss = \"binary_crossentropy\", optimizer = \"adam\", metrics = [\"accuracy\"]) #compile the model\n",
    "model_attention.fit(Xtrain, y_train, batch_size=64, verbose=1, epochs=30, validation_data=(Xtest,y_test), shuffle=True,\n",
    "          callbacks = [earlyStopping]) #fitting the model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M7aZ-2dTLlS0"
   },
   "source": [
    "### 12.1 Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QliUStZrterN",
    "outputId": "0d5a8cc6-5758-48f6-9298-0f44e4d59b95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "138/138 [==============================] - 1s 3ms/step\n",
      "35/35 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "## using predict attribute to get the likelihood/probabilities\n",
    "ytrain_pred_attn = model_attention.predict(Xtrain, verbose=1)\n",
    "ytest_pred_attn = model_attention.predict(Xtest, verbose=1)\n",
    "\n",
    "## getting the predicted class from the likelihoods\n",
    "ytrain_predicted_attn = [max(enumerate(val), key=operator.itemgetter(1))[0] for val in ytrain_pred_attn]\n",
    "ytest_predicted_attn = [max(enumerate(val), key=operator.itemgetter(1))[0] for val in ytest_pred_attn]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_aVrqoBxtvlO",
    "outputId": "e193c88d-c5d7-4b59-ae54-01a4abc0aa4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train and test accuracy are 0.8362 and 0.8305 respectively.\n",
      "test fscore, precision and recall are 0.2724, 0.7778, and 0.1651.\n"
     ]
    }
   ],
   "source": [
    "train_acc_attn, test_acc_attn, test_fscore_attn, test_precision_attn, test_recall_attn = output_scores(ytrain, ytrain_predicted_attn, \n",
    "                                                                                                       ytest, ytest_predicted_attn)\n",
    "\n",
    "print(\"train and test accuracy are {:.4f} and {:.4f} respectively.\".format(train_acc_attn, test_acc_attn))\n",
    "\n",
    "print(\"test fscore, precision and recall are {:.4f}, {:.4f}, and {:.4f}.\".format(test_fscore_attn, test_precision_attn, test_recall_attn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "i-Ibc1uJLlsF",
    "outputId": "b413a4c9-89b6-4e95-901a-c218fd2bcafa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report Test Data for attention model:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.99      0.90       891\n",
      "           1       0.78      0.17      0.27       212\n",
      "\n",
      "    accuracy                           0.83      1103\n",
      "   macro avg       0.81      0.58      0.59      1103\n",
      "weighted avg       0.82      0.83      0.78      1103\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Classification Report Test Data for attention model:\\n\\n\",\n",
    "      classification_report(ytest, ytest_predicted_attn)) ## classification report\n",
    "\n",
    "conf_matrix(ytest, ytest_predicted_attn, \"confusion_matrix_attention_model\") ## confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HG7Soh8kuHPY"
   },
   "source": [
    "## 13. Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HG0A6D4PwwNS"
   },
   "outputs": [],
   "source": [
    "## getting list of scores\n",
    "model_name = ['Naive Bayes', 'Logistic Regression', 'LSTM', 'Attention']\n",
    "train_accuracy_scores = [train_acc_nb, train_acc_lr, train_acc_lstm, train_acc_attn]\n",
    "test_accuracy_scores = [test_acc_nb, test_acc_lr, test_acc_lstm, test_acc_attn]\n",
    "f1_scores = [test_fscore_nb, test_fscore_lr, test_fscore_lstm, test_fscore_attn]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PC4HdxU4x8RY"
   },
   "source": [
    "### 13.1 accuracy scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "1qibRHvGwwPy",
    "outputId": "9b912e7d-6d10-4adb-a5bd-81de9c17e019"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis = sns.barplot(x = model_name, y = test_accuracy_scores)\n",
    "axis.set(xlabel='Classifier', ylabel='Accuracy')\n",
    "for p in axis.patches:\n",
    "    height = p.get_height()\n",
    "    axis.text(p.get_x() + p.get_width()/2, height + 0.005, '{:1.4f}'.format(height), ha=\"center\") \n",
    "plt.title('Accuracy score of different supervised learning models')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PNFx5jn7x_uM"
   },
   "source": [
    "### 13.2 f1score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "4JSITM9EyCWP",
    "outputId": "4ed98692-81fe-42a8-8184-7fa4d5eb73f8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis = sns.barplot(x = model_name, y = f1_scores)\n",
    "axis.set(xlabel='Classifier', ylabel='F1 Score')\n",
    "for p in axis.patches:\n",
    "    height = p.get_height()\n",
    "    axis.text(p.get_x() + p.get_width()/2, height + 0.005, '{:1.4f}'.format(height), ha=\"center\") \n",
    "plt.title('F1 Score score of different supervised learning models')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "sentiment_analysis_sonia_v1.ipynb",
   "provenance": []
  },
  "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",
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