E-commerce Product Review - Sentiment Analysis (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "971c70c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2e23e6b5",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>Clothing ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "      <th>Division Name</th>\n",
       "      <th>Department Name</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>767</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>Intimates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1077</td>\n",
       "      <td>60</td>\n",
       "      <td>Some major design flaws</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1049</td>\n",
       "      <td>50</td>\n",
       "      <td>My favorite buy!</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>847</td>\n",
       "      <td>47</td>\n",
       "      <td>Flattering shirt</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1080</td>\n",
       "      <td>49</td>\n",
       "      <td>Not for the very petite</td>\n",
       "      <td>I love tracy reese dresses, but this one is no...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>858</td>\n",
       "      <td>39</td>\n",
       "      <td>Cagrcoal shimmer fun</td>\n",
       "      <td>I aded this in my basket at hte last mintue to...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Knits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>858</td>\n",
       "      <td>39</td>\n",
       "      <td>Shimmer, surprisingly goes with lots</td>\n",
       "      <td>I ordered this in carbon for store pick up, an...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Knits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1077</td>\n",
       "      <td>24</td>\n",
       "      <td>Flattering</td>\n",
       "      <td>I love this dress. i usually get an xs but it ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1077</td>\n",
       "      <td>34</td>\n",
       "      <td>Such a fun dress!</td>\n",
       "      <td>I'm 5\"5' and 125 lbs. i ordered the s petite t...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1077</td>\n",
       "      <td>53</td>\n",
       "      <td>Dress looks like it's made of cheap material</td>\n",
       "      <td>Dress runs small esp where the zipper area run...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1095</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This dress is perfection! so pretty and flatte...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1095</td>\n",
       "      <td>53</td>\n",
       "      <td>Perfect!!!</td>\n",
       "      <td>More and more i find myself reliant on the rev...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>767</td>\n",
       "      <td>44</td>\n",
       "      <td>Runs big</td>\n",
       "      <td>Bought the black xs to go under the larkspur m...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>Intimates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1077</td>\n",
       "      <td>50</td>\n",
       "      <td>Pretty party dress with some issues</td>\n",
       "      <td>This is a nice choice for holiday gatherings. ...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Clothing ID  Age  \\\n",
       "0            0          767   33   \n",
       "1            1         1080   34   \n",
       "2            2         1077   60   \n",
       "3            3         1049   50   \n",
       "4            4          847   47   \n",
       "5            5         1080   49   \n",
       "6            6          858   39   \n",
       "7            7          858   39   \n",
       "8            8         1077   24   \n",
       "9            9         1077   34   \n",
       "10          10         1077   53   \n",
       "11          11         1095   39   \n",
       "12          12         1095   53   \n",
       "13          13          767   44   \n",
       "14          14         1077   50   \n",
       "\n",
       "                                           Title  \\\n",
       "0                                            NaN   \n",
       "1                                            NaN   \n",
       "2                        Some major design flaws   \n",
       "3                               My favorite buy!   \n",
       "4                               Flattering shirt   \n",
       "5                        Not for the very petite   \n",
       "6                           Cagrcoal shimmer fun   \n",
       "7           Shimmer, surprisingly goes with lots   \n",
       "8                                     Flattering   \n",
       "9                              Such a fun dress!   \n",
       "10  Dress looks like it's made of cheap material   \n",
       "11                                           NaN   \n",
       "12                                    Perfect!!!   \n",
       "13                                      Runs big   \n",
       "14           Pretty party dress with some issues   \n",
       "\n",
       "                                          Review Text  Rating  \\\n",
       "0   Absolutely wonderful - silky and sexy and comf...       4   \n",
       "1   Love this dress!  it's sooo pretty.  i happene...       5   \n",
       "2   I had such high hopes for this dress and reall...       3   \n",
       "3   I love, love, love this jumpsuit. it's fun, fl...       5   \n",
       "4   This shirt is very flattering to all due to th...       5   \n",
       "5   I love tracy reese dresses, but this one is no...       2   \n",
       "6   I aded this in my basket at hte last mintue to...       5   \n",
       "7   I ordered this in carbon for store pick up, an...       4   \n",
       "8   I love this dress. i usually get an xs but it ...       5   \n",
       "9   I'm 5\"5' and 125 lbs. i ordered the s petite t...       5   \n",
       "10  Dress runs small esp where the zipper area run...       3   \n",
       "11  This dress is perfection! so pretty and flatte...       5   \n",
       "12  More and more i find myself reliant on the rev...       5   \n",
       "13  Bought the black xs to go under the larkspur m...       5   \n",
       "14  This is a nice choice for holiday gatherings. ...       3   \n",
       "\n",
       "    Recommended IND  Positive Feedback Count   Division Name Department Name  \\\n",
       "0                 1                        0       Initmates        Intimate   \n",
       "1                 1                        4         General         Dresses   \n",
       "2                 0                        0         General         Dresses   \n",
       "3                 1                        0  General Petite         Bottoms   \n",
       "4                 1                        6         General            Tops   \n",
       "5                 0                        4         General         Dresses   \n",
       "6                 1                        1  General Petite            Tops   \n",
       "7                 1                        4  General Petite            Tops   \n",
       "8                 1                        0         General         Dresses   \n",
       "9                 1                        0         General         Dresses   \n",
       "10                0                       14         General         Dresses   \n",
       "11                1                        2  General Petite         Dresses   \n",
       "12                1                        2  General Petite         Dresses   \n",
       "13                1                        0       Initmates        Intimate   \n",
       "14                1                        1         General         Dresses   \n",
       "\n",
       "   Class Name  \n",
       "0   Intimates  \n",
       "1     Dresses  \n",
       "2     Dresses  \n",
       "3       Pants  \n",
       "4     Blouses  \n",
       "5     Dresses  \n",
       "6       Knits  \n",
       "7       Knits  \n",
       "8     Dresses  \n",
       "9     Dresses  \n",
       "10    Dresses  \n",
       "11    Dresses  \n",
       "12    Dresses  \n",
       "13  Intimates  \n",
       "14    Dresses  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the csv file downloaded from Kaggle\n",
    "df = pd.read_csv(\"Womens Clothing E-Commerce Reviews.csv\")\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ad12f5ff",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clothing ID</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1049</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Clothing ID                                        Review Text  Rating  \\\n",
       "0          767  Absolutely wonderful - silky and sexy and comf...       4   \n",
       "1         1080  Love this dress!  it's sooo pretty.  i happene...       5   \n",
       "2         1077  I had such high hopes for this dress and reall...       3   \n",
       "3         1049  I love, love, love this jumpsuit. it's fun, fl...       5   \n",
       "4          847  This shirt is very flattering to all due to th...       5   \n",
       "\n",
       "   Recommended IND  Positive Feedback Count  \n",
       "0                1                        0  \n",
       "1                1                        4  \n",
       "2                0                        0  \n",
       "3                1                        0  \n",
       "4                1                        6  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop Columns that has no high impact on the sentiment analysis process\n",
    "df.drop(['Unnamed: 0','Age','Title','Division Name','Department Name','Class Name'],axis =1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2b91829b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23486, 5)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "764b4639",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clothing ID                  0\n",
       "Review Text                845\n",
       "Rating                       0\n",
       "Recommended IND              0\n",
       "Positive Feedback Count      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check how many null rows we have in the entire dataset\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c3b424dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop null values in the specified column\n",
    "df.dropna(subset=['Review Text'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "702fb12a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clothing ID                0\n",
       "Review Text                0\n",
       "Rating                     0\n",
       "Recommended IND            0\n",
       "Positive Feedback Count    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0add71a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply preprocess data cleaning and transformation on sentences in the Review Text column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "833b2487",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Review Text'] = df['Review Text'].str.lower()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1c2eb279",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Review Text'] = df['Review Text'].replace(to_replace= r'\\d+', value = '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9fbaa3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Review Text']= df['Review Text'].replace(to_replace= r'[^\\w\\s]', value='', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "47cc10b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Review Text']= df['Review Text'].replace(to_replace= r'<[^>]+>', value='', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0a4c18c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Review Text']= df['Review Text'].replace(to_replace= r'http\\S+', value='', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "986f72f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\ADMIN\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\ADMIN\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\ADMIN\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "53c477f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sw = set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "84497855",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'after',\n",
       " 'again',\n",
       " 'against',\n",
       " 'ain',\n",
       " 'all',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'are',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'as',\n",
       " 'at',\n",
       " 'be',\n",
       " 'because',\n",
       " 'been',\n",
       " 'before',\n",
       " 'being',\n",
       " 'below',\n",
       " 'between',\n",
       " 'both',\n",
       " 'but',\n",
       " 'by',\n",
       " 'can',\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'd',\n",
       " 'did',\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'do',\n",
       " 'does',\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'doing',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'down',\n",
       " 'during',\n",
       " 'each',\n",
       " 'few',\n",
       " 'for',\n",
       " 'from',\n",
       " 'further',\n",
       " 'had',\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'has',\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'have',\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'having',\n",
       " 'he',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'into',\n",
       " 'is',\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'll',\n",
       " 'm',\n",
       " 'ma',\n",
       " 'me',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'more',\n",
       " 'most',\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'my',\n",
       " 'myself',\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'now',\n",
       " 'o',\n",
       " 'of',\n",
       " 'off',\n",
       " 'on',\n",
       " 'once',\n",
       " 'only',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 're',\n",
       " 's',\n",
       " 'same',\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'so',\n",
       " 'some',\n",
       " 'such',\n",
       " 't',\n",
       " 'than',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'the',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'there',\n",
       " 'these',\n",
       " 'they',\n",
       " 'this',\n",
       " 'those',\n",
       " 'through',\n",
       " 'to',\n",
       " 'too',\n",
       " 'under',\n",
       " 'until',\n",
       " 'up',\n",
       " 've',\n",
       " 'very',\n",
       " 'was',\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'we',\n",
       " 'were',\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'what',\n",
       " 'when',\n",
       " 'where',\n",
       " 'which',\n",
       " 'while',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " 'y',\n",
       " 'you',\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves'}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0cfc7966",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Review Text']= df['Review Text'].apply(lambda x: \" \".join([word for word in word_tokenize(x) if word not in sw]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "88663615",
   "metadata": {},
   "outputs": [],
   "source": [
    "L = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7e145c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Review Text']= df['Review Text'].apply(lambda x: \" \".join([L.lemmatize(word) for word in word_tokenize(x)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f70b5af6",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Clothing ID</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>767</td>\n",
       "      <td>absolutely wonderful silky sexy comfortable</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>love dress sooo pretty happened find store im ...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077</td>\n",
       "      <td>high hope dress really wanted work initially o...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1049</td>\n",
       "      <td>love love love jumpsuit fun flirty fabulous ev...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847</td>\n",
       "      <td>shirt flattering due adjustable front tie perf...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23481</th>\n",
       "      <td>1104</td>\n",
       "      <td>happy snag dress great price easy slip flatter...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23482</th>\n",
       "      <td>862</td>\n",
       "      <td>reminds maternity clothes soft stretchy shiny ...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23483</th>\n",
       "      <td>1104</td>\n",
       "      <td>fit well top see never would worked im glad ab...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23484</th>\n",
       "      <td>1084</td>\n",
       "      <td>bought dress wedding summer cute unfortunately...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23485</th>\n",
       "      <td>1104</td>\n",
       "      <td>dress lovely platinum feminine fit perfectly e...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22641 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clothing ID                                        Review Text  Rating  \\\n",
       "0              767        absolutely wonderful silky sexy comfortable       4   \n",
       "1             1080  love dress sooo pretty happened find store im ...       5   \n",
       "2             1077  high hope dress really wanted work initially o...       3   \n",
       "3             1049  love love love jumpsuit fun flirty fabulous ev...       5   \n",
       "4              847  shirt flattering due adjustable front tie perf...       5   \n",
       "...            ...                                                ...     ...   \n",
       "23481         1104  happy snag dress great price easy slip flatter...       5   \n",
       "23482          862  reminds maternity clothes soft stretchy shiny ...       3   \n",
       "23483         1104  fit well top see never would worked im glad ab...       3   \n",
       "23484         1084  bought dress wedding summer cute unfortunately...       3   \n",
       "23485         1104  dress lovely platinum feminine fit perfectly e...       5   \n",
       "\n",
       "       Recommended IND  Positive Feedback Count  \n",
       "0                    1                        0  \n",
       "1                    1                        4  \n",
       "2                    0                        0  \n",
       "3                    1                        0  \n",
       "4                    1                        6  \n",
       "...                ...                      ...  \n",
       "23481                1                        0  \n",
       "23482                1                        0  \n",
       "23483                0                        1  \n",
       "23484                1                        2  \n",
       "23485                1                       22  \n",
       "\n",
       "[22641 rows x 5 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93726286",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data as dependent and independet  with test size = 20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1df99647",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['Review Text']\n",
    "y = df['Recommended IND']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "26989b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0248785",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Count Vectorization on top of \"x\" data which is the column that contain the review text as the machine accept only numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f81f1d61",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = CountVectorizer()\n",
    "x_train = vec.fit_transform(x_train).toarray()\n",
    "x_test = vec.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1879a171",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the model with fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c30b9e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "29223069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "79120dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "712d7aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18112, 14984)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "6eb03318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18112,)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "6be8f1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4529, 14984)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "cb684082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4529,)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d58b0baf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91c77c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating the model mean accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "da499cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8812099801280636"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a5501ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(vec.transform(['This product was faraway from expectations, i will not try it again']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "b2ec40c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.56823352, 0.43176648]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(vec.transform(['This product was faraway from expectations, i will not try it again']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53fabb66",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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