Text Mining - Amazon Beauty Reviews.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![amazon](img/amazon-logo.jpg)\n",
    "\n",
    "## Product Category:\n",
    "# $$Beauty \\ Reviews$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Dataset\n",
    "\n",
    "![aws-s3](img/amazon_aws-s3.png)\n",
    "\n",
    "<br>\n",
    "UCSanDiego library/repo Curated by Julian McAuley [Source](http://jmcauley.ucsd.edu/data/amazon/)\n",
    "\n",
    "https://s3-us-west-2.amazonaws.com/msds-projects/data/reviews_Beauty_5.json.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset -fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "import itertools\n",
    "import string\n",
    "import wordcloud\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "\n",
    "from collections import Counter\n",
    "from sklearn import svm\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.decomposition import NMF, LatentDirichletAllocation\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n",
    "\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.corpus import wordnet\n",
    "from sklearn import metrics\n",
    "\n",
    "from keras.preprocessing import sequence\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Lambda\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers.recurrent import LSTM, SimpleRNN, GRU\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from collections import defaultdict\n",
    "from keras.layers.convolutional import Convolution1D\n",
    "from keras import backend as K\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.callbacks import EarlyStopping\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Part I EDA and Preprocessing\n",
    "---\n",
    "### `Download and Unzip Reviews into Pandas DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Succesfully Retrieved!\n"
     ]
    }
   ],
   "source": [
    "!python load_data.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code provided via http://jmcauley.ucsd.edu/data/amazon/\n",
    "\n",
    "def parse_gz(path):\n",
    "    g = gzip.open(path, 'rb')\n",
    "    for l in g:\n",
    "        yield eval(l)\n",
    "\n",
    "def convert_to_DF(path):\n",
    "    i = 0\n",
    "    df = {}\n",
    "    for d in parse_gz(path):\n",
    "        df[i] = d\n",
    "        i += 1\n",
    "    return pd.DataFrame.from_dict(df, orient='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Convert compressed file of data into Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "beauty = convert_to_DF('reviews_Beauty_5.json.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size: 198,502 words\n"
     ]
    }
   ],
   "source": [
    "print('Dataset size: {:,} words'.format(len(beauty)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>helpful</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>overall</th>\n",
       "      <th>summary</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>reviewTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1YJEY40YUW4SE</td>\n",
       "      <td>7806397051</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>[3, 4]</td>\n",
       "      <td>Very oily and creamy. Not at all what I expect...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Don't waste your money</td>\n",
       "      <td>1391040000</td>\n",
       "      <td>01 30, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A60XNB876KYML</td>\n",
       "      <td>7806397051</td>\n",
       "      <td>Jessica H.</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>This palette was a decent price and I was look...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>OK Palette!</td>\n",
       "      <td>1397779200</td>\n",
       "      <td>04 18, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3G6XNM240RMWA</td>\n",
       "      <td>7806397051</td>\n",
       "      <td>Karen</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>The texture of this concealer pallet is fantas...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>great quality</td>\n",
       "      <td>1378425600</td>\n",
       "      <td>09 6, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1PQFP6SAJ6D80</td>\n",
       "      <td>7806397051</td>\n",
       "      <td>Norah</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>I really can't tell what exactly this thing is...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Do not work on my face</td>\n",
       "      <td>1386460800</td>\n",
       "      <td>12 8, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A38FVHZTNQ271F</td>\n",
       "      <td>7806397051</td>\n",
       "      <td>Nova Amor</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>It was a little smaller than I expected, but t...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>It's okay.</td>\n",
       "      <td>1382140800</td>\n",
       "      <td>10 19, 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reviewerID        asin reviewerName helpful  \\\n",
       "0  A1YJEY40YUW4SE  7806397051       Andrea  [3, 4]   \n",
       "1   A60XNB876KYML  7806397051   Jessica H.  [1, 1]   \n",
       "2  A3G6XNM240RMWA  7806397051        Karen  [0, 1]   \n",
       "3  A1PQFP6SAJ6D80  7806397051        Norah  [2, 2]   \n",
       "4  A38FVHZTNQ271F  7806397051    Nova Amor  [0, 0]   \n",
       "\n",
       "                                          reviewText  overall  \\\n",
       "0  Very oily and creamy. Not at all what I expect...      1.0   \n",
       "1  This palette was a decent price and I was look...      3.0   \n",
       "2  The texture of this concealer pallet is fantas...      4.0   \n",
       "3  I really can't tell what exactly this thing is...      2.0   \n",
       "4  It was a little smaller than I expected, but t...      3.0   \n",
       "\n",
       "                  summary  unixReviewTime   reviewTime  \n",
       "0  Don't waste your money      1391040000  01 30, 2014  \n",
       "1             OK Palette!      1397779200  04 18, 2014  \n",
       "2           great quality      1378425600   09 6, 2013  \n",
       "3  Do not work on my face      1386460800   12 8, 2013  \n",
       "4              It's okay.      1382140800  10 19, 2013  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Reformat** $\\underline{datetime}$ from raw form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "beauty[\"reviewTime\"] = pd.to_datetime(beauty[\"reviewTime\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Rearrange** the left-to-right by relevance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "beauty = beauty[['asin', 'summary', 'reviewText', 'overall', 'reviewerID', 'reviewerName', 'helpful', 'reviewTime',\n",
    "      'unixReviewTime']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>asin</th>\n",
       "      <th>summary</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>helpful</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>unixReviewTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>Don't waste your money</td>\n",
       "      <td>Very oily and creamy. Not at all what I expect...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A1YJEY40YUW4SE</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>[3, 4]</td>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>1391040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>OK Palette!</td>\n",
       "      <td>This palette was a decent price and I was look...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A60XNB876KYML</td>\n",
       "      <td>Jessica H.</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>1397779200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>great quality</td>\n",
       "      <td>The texture of this concealer pallet is fantas...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A3G6XNM240RMWA</td>\n",
       "      <td>Karen</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>2013-09-06</td>\n",
       "      <td>1378425600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asin                 summary  \\\n",
       "0  7806397051  Don't waste your money   \n",
       "1  7806397051             OK Palette!   \n",
       "2  7806397051           great quality   \n",
       "\n",
       "                                          reviewText  overall      reviewerID  \\\n",
       "0  Very oily and creamy. Not at all what I expect...      1.0  A1YJEY40YUW4SE   \n",
       "1  This palette was a decent price and I was look...      3.0   A60XNB876KYML   \n",
       "2  The texture of this concealer pallet is fantas...      4.0  A3G6XNM240RMWA   \n",
       "\n",
       "  reviewerName helpful reviewTime  unixReviewTime  \n",
       "0       Andrea  [3, 4] 2014-01-30      1391040000  \n",
       "1   Jessica H.  [1, 1] 2014-04-18      1397779200  \n",
       "2        Karen  [0, 1] 2013-09-06      1378425600  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>asin</th>\n",
       "      <th>summary</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>helpful</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>unixReviewTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198499</th>\n",
       "      <td>B00LLPT4HI</td>\n",
       "      <td>One Pound Organic Grade A Unrefined Shea Butter</td>\n",
       "      <td>The skin is the body's largest organ and it ab...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A35Q0RBM3YNQNF</td>\n",
       "      <td>M. Hill</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>2014-07-16</td>\n",
       "      <td>1405468800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198500</th>\n",
       "      <td>B00LLPT4HI</td>\n",
       "      <td>This stuff is amazing!</td>\n",
       "      <td>I have very dry elbows and knees.  I have a to...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A3LGT6UZL99IW1</td>\n",
       "      <td>Richard C. Drew \"Anaal Nathra/Uthe vas Bethod...</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>2014-07-15</td>\n",
       "      <td>1405382400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198501</th>\n",
       "      <td>B00LLPT4HI</td>\n",
       "      <td>The \"Real Stuff\"!</td>\n",
       "      <td>This is 100% pure Shea Butter. Do not mistake ...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>A3UJRNI8UR4871</td>\n",
       "      <td>Wulfstan \"wulfstan\"</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>2014-07-22</td>\n",
       "      <td>1405987200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              asin                                          summary  \\\n",
       "198499  B00LLPT4HI  One Pound Organic Grade A Unrefined Shea Butter   \n",
       "198500  B00LLPT4HI                           This stuff is amazing!   \n",
       "198501  B00LLPT4HI                                The \"Real Stuff\"!   \n",
       "\n",
       "                                               reviewText  overall  \\\n",
       "198499  The skin is the body's largest organ and it ab...      5.0   \n",
       "198500  I have very dry elbows and knees.  I have a to...      5.0   \n",
       "198501  This is 100% pure Shea Butter. Do not mistake ...      5.0   \n",
       "\n",
       "            reviewerID                                      reviewerName  \\\n",
       "198499  A35Q0RBM3YNQNF                                           M. Hill   \n",
       "198500  A3LGT6UZL99IW1  Richard C. Drew \"Anaal Nathra/Uthe vas Bethod...   \n",
       "198501  A3UJRNI8UR4871                               Wulfstan \"wulfstan\"   \n",
       "\n",
       "       helpful reviewTime  unixReviewTime  \n",
       "198499  [0, 0] 2014-07-16      1405468800  \n",
       "198500  [0, 0] 2014-07-15      1405382400  \n",
       "198501  [0, 1] 2014-07-22      1405987200  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Number of Reviews by Unique Products [`asin`]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Products in the Sports & Outdoors Category = 12101\n"
     ]
    }
   ],
   "source": [
    "products = len(beauty['asin'].unique())\n",
    "print(\"Number of Unique Products in the Sports & Outdoors Category = {}\".format(products))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No of Reviews per Product**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B004OHQR1Q    431\n",
      "B0043OYFKU    403\n",
      "B0069FDR96    391\n",
      "B000ZMBSPE    389\n",
      "B00150LT40    329\n",
      "             ... \n",
      "B009LJ81DY      5\n",
      "B000BIVY1U      5\n",
      "B000P9BR4Y      5\n",
      "B002UBF2YG      5\n",
      "B00GRTQBTM      5\n",
      "Name: asin, Length: 12101, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(beauty['asin'].value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most Reviewed Product, B001HBHNHE - has 1024 reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\underline{Most\\ Reviewed}$ is Olay Advanced System\n",
    "[Amazon Product listing](https://www.amazon.com/dp/B0043OYFKU)\n",
    "\n",
    "![olay](img/olay.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\underline{Least\\ Reviewed}$ is a Long Curly Hair Ends Costume Cosplay Wig (Coffee)\n",
    "[Amazon Product listing](https://www.amazon.com/dp/B00GRTQBTM)\n",
    "\n",
    "![Web Nlyon Knife Sheath](img/wig.jpg)\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\underline{Most\\ Common\\ Amount\\ of\\ Reviews} -->$ $5$ Reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Preprocessing:\n",
    "### `Remove punctuations, stopwords, and lemmatize`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Very oily and creamy. Not at all what I expect...\n",
       "1    This palette was a decent price and I was look...\n",
       "2    The texture of this concealer pallet is fantas...\n",
       "3    I really can't tell what exactly this thing is...\n",
       "4    It was a little smaller than I expected, but t...\n",
       "Name: reviewText, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty['reviewText'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = beauty['reviewText']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "198502"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "tokenizer = RegexpTokenizer(r'[a-z]+')\n",
    "\n",
    "def preprocess(document):\n",
    "    document = document.lower() # Convert to lowercase\n",
    "    words = tokenizer.tokenize(document) # Tokenize\n",
    "    words = [w for w in words if not w in stop_words] # Removing stopwords\n",
    "    # Lemmatizing\n",
    "    for pos in [wordnet.NOUN, wordnet.VERB, wordnet.ADJ, wordnet.ADV]:\n",
    "        words = [wordnet_lemmatizer.lemmatize(x, pos) for x in words]\n",
    "    return \" \".join(words)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "beauty['processed_review'] = beauty['reviewText'].apply(preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "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>asin</th>\n",
       "      <th>summary</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>helpful</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>pos_neg</th>\n",
       "      <th>processed_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>Don't waste your money</td>\n",
       "      <td>Very oily and creamy. Not at all what I expect...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A1YJEY40YUW4SE</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>[3, 4]</td>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>1391040000</td>\n",
       "      <td>0</td>\n",
       "      <td>oily creamy expect order try highlight contour...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>OK Palette!</td>\n",
       "      <td>This palette was a decent price and I was look...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A60XNB876KYML</td>\n",
       "      <td>Jessica H.</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>1397779200</td>\n",
       "      <td>0</td>\n",
       "      <td>palette decent price look different shade pale...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>great quality</td>\n",
       "      <td>The texture of this concealer pallet is fantas...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A3G6XNM240RMWA</td>\n",
       "      <td>Karen</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>2013-09-06</td>\n",
       "      <td>1378425600</td>\n",
       "      <td>1</td>\n",
       "      <td>texture concealer pallet fantastic great cover...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>Do not work on my face</td>\n",
       "      <td>I really can't tell what exactly this thing is...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>A1PQFP6SAJ6D80</td>\n",
       "      <td>Norah</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>2013-12-08</td>\n",
       "      <td>1386460800</td>\n",
       "      <td>0</td>\n",
       "      <td>really tell exactly thing powder kind oil ish ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>It's okay.</td>\n",
       "      <td>It was a little smaller than I expected, but t...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A38FVHZTNQ271F</td>\n",
       "      <td>Nova Amor</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>2013-10-19</td>\n",
       "      <td>1382140800</td>\n",
       "      <td>0</td>\n",
       "      <td>little small expect okay last long time think ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asin                 summary  \\\n",
       "0  7806397051  Don't waste your money   \n",
       "1  7806397051             OK Palette!   \n",
       "2  7806397051           great quality   \n",
       "3  7806397051  Do not work on my face   \n",
       "4  7806397051              It's okay.   \n",
       "\n",
       "                                          reviewText  overall      reviewerID  \\\n",
       "0  Very oily and creamy. Not at all what I expect...      1.0  A1YJEY40YUW4SE   \n",
       "1  This palette was a decent price and I was look...      3.0   A60XNB876KYML   \n",
       "2  The texture of this concealer pallet is fantas...      4.0  A3G6XNM240RMWA   \n",
       "3  I really can't tell what exactly this thing is...      2.0  A1PQFP6SAJ6D80   \n",
       "4  It was a little smaller than I expected, but t...      3.0  A38FVHZTNQ271F   \n",
       "\n",
       "  reviewerName helpful reviewTime  unixReviewTime  pos_neg  \\\n",
       "0       Andrea  [3, 4] 2014-01-30      1391040000        0   \n",
       "1   Jessica H.  [1, 1] 2014-04-18      1397779200        0   \n",
       "2        Karen  [0, 1] 2013-09-06      1378425600        1   \n",
       "3        Norah  [2, 2] 2013-12-08      1386460800        0   \n",
       "4    Nova Amor  [0, 0] 2013-10-19      1382140800        0   \n",
       "\n",
       "                                    processed_review  \n",
       "0  oily creamy expect order try highlight contour...  \n",
       "1  palette decent price look different shade pale...  \n",
       "2  texture concealer pallet fantastic great cover...  \n",
       "3  really tell exactly thing powder kind oil ish ...  \n",
       "4  little small expect okay last long time think ...  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud = wordcloud.WordCloud(background_color='gray', max_font_size=60, \n",
    "                                relative_scaling=1).generate(' '.join(beauty.processed_review))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "plt.axis('off')\n",
    "plt.imshow(cloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>summary</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>helpful</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>pos_neg</th>\n",
       "      <th>processed_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>Don't waste your money</td>\n",
       "      <td>Very oily and creamy. Not at all what I expect...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A1YJEY40YUW4SE</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>[3, 4]</td>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>1391040000</td>\n",
       "      <td>0</td>\n",
       "      <td>oily creamy expect order try highlight contour...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>OK Palette!</td>\n",
       "      <td>This palette was a decent price and I was look...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A60XNB876KYML</td>\n",
       "      <td>Jessica H.</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>1397779200</td>\n",
       "      <td>0</td>\n",
       "      <td>palette decent price look different shade pale...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>great quality</td>\n",
       "      <td>The texture of this concealer pallet is fantas...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A3G6XNM240RMWA</td>\n",
       "      <td>Karen</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>2013-09-06</td>\n",
       "      <td>1378425600</td>\n",
       "      <td>1</td>\n",
       "      <td>texture concealer pallet fantastic great cover...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>Do not work on my face</td>\n",
       "      <td>I really can't tell what exactly this thing is...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>A1PQFP6SAJ6D80</td>\n",
       "      <td>Norah</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>2013-12-08</td>\n",
       "      <td>1386460800</td>\n",
       "      <td>0</td>\n",
       "      <td>really tell exactly thing powder kind oil ish ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>It's okay.</td>\n",
       "      <td>It was a little smaller than I expected, but t...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A38FVHZTNQ271F</td>\n",
       "      <td>Nova Amor</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>2013-10-19</td>\n",
       "      <td>1382140800</td>\n",
       "      <td>0</td>\n",
       "      <td>little small expect okay last long time think ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asin                 summary  \\\n",
       "0  7806397051  Don't waste your money   \n",
       "1  7806397051             OK Palette!   \n",
       "2  7806397051           great quality   \n",
       "3  7806397051  Do not work on my face   \n",
       "4  7806397051              It's okay.   \n",
       "\n",
       "                                          reviewText  overall      reviewerID  \\\n",
       "0  Very oily and creamy. Not at all what I expect...      1.0  A1YJEY40YUW4SE   \n",
       "1  This palette was a decent price and I was look...      3.0   A60XNB876KYML   \n",
       "2  The texture of this concealer pallet is fantas...      4.0  A3G6XNM240RMWA   \n",
       "3  I really can't tell what exactly this thing is...      2.0  A1PQFP6SAJ6D80   \n",
       "4  It was a little smaller than I expected, but t...      3.0  A38FVHZTNQ271F   \n",
       "\n",
       "  reviewerName helpful reviewTime  unixReviewTime  pos_neg  \\\n",
       "0       Andrea  [3, 4] 2014-01-30      1391040000        0   \n",
       "1   Jessica H.  [1, 1] 2014-04-18      1397779200        0   \n",
       "2        Karen  [0, 1] 2013-09-06      1378425600        1   \n",
       "3        Norah  [2, 2] 2013-12-08      1386460800        0   \n",
       "4    Nova Amor  [0, 0] 2013-10-19      1382140800        0   \n",
       "\n",
       "                                    processed_review  \n",
       "0  oily creamy expect order try highlight contour...  \n",
       "1  palette decent price look different shade pale...  \n",
       "2  texture concealer pallet fantastic great cover...  \n",
       "3  really tell exactly thing powder kind oil ish ...  \n",
       "4  little small expect okay last long time think ...  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insert `pos_neg` column for Sentiment modeling\n",
    "\n",
    "         Negative reviews:      1-3 Stars  = 0\n",
    "         Positive reviews:      4-5 Stars  = 1\n",
    "\n",
    "   ![star](img/amazon-stars.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "beauty['pos_neg'] = [1 if x > 3 else 0 for x in beauty.overall]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>summary</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>helpful</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>pos_neg</th>\n",
       "      <th>processed_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>Don't waste your money</td>\n",
       "      <td>Very oily and creamy. Not at all what I expect...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A1YJEY40YUW4SE</td>\n",
       "      <td>Andrea</td>\n",
       "      <td>[3, 4]</td>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>1391040000</td>\n",
       "      <td>0</td>\n",
       "      <td>oily creamy expect order try highlight contour...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>OK Palette!</td>\n",
       "      <td>This palette was a decent price and I was look...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>A60XNB876KYML</td>\n",
       "      <td>Jessica H.</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>1397779200</td>\n",
       "      <td>0</td>\n",
       "      <td>palette decent price look different shade pale...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7806397051</td>\n",
       "      <td>great quality</td>\n",
       "      <td>The texture of this concealer pallet is fantas...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>A3G6XNM240RMWA</td>\n",
       "      <td>Karen</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>2013-09-06</td>\n",
       "      <td>1378425600</td>\n",
       "      <td>1</td>\n",
       "      <td>texture concealer pallet fantastic great cover...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asin                 summary  \\\n",
       "0  7806397051  Don't waste your money   \n",
       "1  7806397051             OK Palette!   \n",
       "2  7806397051           great quality   \n",
       "\n",
       "                                          reviewText  overall      reviewerID  \\\n",
       "0  Very oily and creamy. Not at all what I expect...      1.0  A1YJEY40YUW4SE   \n",
       "1  This palette was a decent price and I was look...      3.0   A60XNB876KYML   \n",
       "2  The texture of this concealer pallet is fantas...      4.0  A3G6XNM240RMWA   \n",
       "\n",
       "  reviewerName helpful reviewTime  unixReviewTime  pos_neg  \\\n",
       "0       Andrea  [3, 4] 2014-01-30      1391040000        0   \n",
       "1   Jessica H.  [1, 1] 2014-04-18      1397779200        0   \n",
       "2        Karen  [0, 1] 2013-09-06      1378425600        1   \n",
       "\n",
       "                                    processed_review  \n",
       "0  oily creamy expect order try highlight contour...  \n",
       "1  palette decent price look different shade pale...  \n",
       "2  texture concealer pallet fantastic great cover...  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    154272\n",
       "0     44230\n",
       "Name: pos_neg, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty['pos_neg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">processed_review</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos_neg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44230</td>\n",
       "      <td>44173</td>\n",
       "      <td>work</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>154272</td>\n",
       "      <td>153928</td>\n",
       "      <td>love</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        processed_review                   \n",
       "                   count  unique   top freq\n",
       "pos_neg                                    \n",
       "0                  44230   44173  work   11\n",
       "1                 154272  153928  love   34"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty2 = beauty[[\"pos_neg\",\"processed_review\"]]\n",
    "beauty2.groupby('pos_neg').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Part II Modeling\n",
    "---\n",
    "## Model data\n",
    "\n",
    "> **[ 1 ]** CountVectorizer - Multinomial NB\n",
    "\n",
    "> **[ 2 ]** TfidfVectorizer\n",
    "\n",
    "            1. with Logistic Regression\n",
    "            2. with Linear SVM by using SGD\n",
    "            3. Pipeline and GridSearch\n",
    "            \n",
    "> **[ 3 ]** Word2Vec\n",
    "\n",
    "           1. Random Forest\n",
    "           2. Logistic Regression\n",
    "\n",
    "> **[ 4 ]** LSTM\n",
    "\n",
    "            1. Simple LSTM\n",
    "            2. LSTM with Word2Vec\n",
    "            \n",
    "            \n",
    "> **[ 5 ]** Clustering / Topic Modeling (NMF and LDA)  with TF and TFIDF\n",
    "          \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_text = beauty[\"processed_review\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(beauty.processed_review, beauty.pos_neg, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (148876,)\n",
      "y_train shape: (148876,)\n",
      "\n",
      "x_test shape: (49626,)\n",
      "y_test shape: (49626,)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"x_train shape: {}\".format(x_train.shape), end='\\n')\n",
    "print(\"y_train shape: {}\".format(y_train.shape), end='\\n\\n')\n",
    "print(\"x_test shape: {}\".format(x_test.shape), end='\\n')\n",
    "print(\"y_test shape: {}\".format(y_test.shape), end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41325     question rogaine work need start use lose hair...\n",
       "35450     price guess really complain perfume smell stro...\n",
       "112087    polish great really really smelly perfect cove...\n",
       "97533     eh thank love pale pink hue lip treatment saw ...\n",
       "31285                              good healthy strong nail\n",
       "                                ...                        \n",
       "152315    product difficult use attachements hard put tu...\n",
       "176963    skin problem blackhead pimple area need improv...\n",
       "117952    well love wear eyelash throughout day go try y...\n",
       "173685    something really wrong brush build liquid thic...\n",
       "43567     first time use product burn eye eye bloodshot ...\n",
       "Name: processed_review, Length: 148876, dtype: object"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `CountVectorizer`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CountVectorizer with Multinomial Naive Bayes (Benchmark Model)\n",
    "\n",
    "Now as we have cleaned all reviews the next step is converting the reviews into numerical representations for a machine learning algorithm.\n",
    "\n",
    "We will use CountVectorizer which implements both tokenization and occurrence counting in a single class provided by the Sklearn library. The output is a sparse matrix representation of the document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:\n",
      "<148876x13630 sparse matrix of type '<class 'numpy.int64'>'\n",
      "\twith 5015799 stored elements in Compressed Sparse Row format>\n"
     ]
    }
   ],
   "source": [
    "# Vectorize X_train\n",
    "vectorizer = CountVectorizer(min_df=5).fit(x_train)\n",
    "X_train = vectorizer.transform(x_train)\n",
    "print(\"X_train:\\n{}\".format(repr(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features: 13630\n"
     ]
    }
   ],
   "source": [
    "feature_names = vectorizer.get_feature_names()\n",
    "print(\"Number of features: {}\".format(len(feature_names)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Show some feature names : \n",
      " ['aa', 'bbw', 'choke', 'definately', 'estimation', 'glittery', 'infestation', 'lpmp', 'nix', 'plaque', 'repetitive', 'slippage', 'terra', 'vetiver']\n"
     ]
    }
   ],
   "source": [
    "print(\"Show some feature names : \\n\", vectorizer.get_feature_names()[::1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(MultinomialNB(), X_train, y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation accuracy: 0.830\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean cross-validation accuracy: {:.3f}\".format(np.mean(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train MultinomialNB classifier\n",
    "mnb = MultinomialNB()\n",
    "mnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = vectorizer.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelEvaluation(predictions):\n",
    "    '''\n",
    "    Print model evaluation to predicted result \n",
    "    '''\n",
    "    print (\"\\nAccuracy on validation set: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
    "    #print(\"\\nAUC score : {:.4f}\".format(roc_auc_score(y_test, predictions)))\n",
    "    print(\"\\nClassification report : \\n\", metrics.classification_report(y_test, predictions))\n",
    "    print(\"\\nConfusion Matrix : \\n\", metrics.confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy on validation set: 0.8344\n",
      "\n",
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.59      0.61     11112\n",
      "           1       0.88      0.91      0.89     38514\n",
      "\n",
      "    accuracy                           0.83     49626\n",
      "   macro avg       0.76      0.75      0.75     49626\n",
      "weighted avg       0.83      0.83      0.83     49626\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[ 6513  4599]\n",
      " [ 3617 34897]]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on validaton set\n",
    "predictions = mnb.predict(X_test)\n",
    "modelEvaluation(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IEmCwme1ACAr859Qe6sjZZtYvHq+1tMCO4xSHsJDWNtFRiZRK6R4JjIrno8gQ9NfM/mdmE+P5NEIszJ7FEtBxnNKQWkhLclQipZK6V4zOAyGmZa9cjSUNIMS9/CCtOFMAYsdxqoA6U6KjEmmx+bmkJ4CNM1Sdn35hZiYpa8jiGILtFmCImaVCaCYOQBwjyQ8FaN+2UyNH4ThOsfEdaU3EzA7KVifpM0m9zWx6VKozs7TrBPwLOD/mK0r1nZolL5f0D+AXOeQYTlDMdG6/cfXmdXacKqLevRcKzhgglcJ4CBmC/kpaD7ifkCTungZ12QIQO45T4YSAN20SHZVIqZb/hgF3SToR+Bg4BkBSf+BkMzspln0N6C7p+Hjf8dFT4bYsAYgdx6lwDLHStwEXFjObAxyYoXwCcFI8vxW4Ncv9GQMQO45T+ZhR1ZsjKtPRzXGcKqZyNz4kwZWu4zhlheEzXcdxnKJSqYtkSXCl6zhOWWHIg5g7juMUi5CCvXpVU/WOzHGcCkVVHU+3eg0njuNUJEbYkZbkyIek9pJelvR6DBN7cSzfWtJLkiZJujNuxkJSu3g9KdZvldbXebH8fUmHpJUPjGWTJGWKmLgWrnQdxyk76uJsN9+RgOXAAWa2C9APGChpT+AK4Coz6wPMA06M7U8E5sXyq2I7JG0PDAJSoWavlVQjqQb4G3AosD1wXGybFVe6juOUFWYq2EzXAoviZW08DDgASIUXSA8vmx529h7gwBhu4EhgtJktN7MPgUnAgHhMMrPJZrYCGB3bZsVtuo7jlBVhIS3xNuAekiakXQ+PQa5WE2ejrwB9CLPSD4D5ZrYqNpkCbBrPNwU+BTCzVZIWAN1j+bi0btPv+bRB+R65BHal6zhOmdGoHGmzzax/rgZmVgf0i5ln7ge2bZ58zcOVruM4ZUVYSCu894KZzZf0NLAX0EVS2zjb3QyYGptNBTYHpkhqC3QG5qSVp0i/J1t5Rtym6zhO2VGo0I6SeqZyK0paH/g68C7wNHB0bJYeXjY97OzRwFNmZrF8UPRu2BroC7wMjAf6Rm+I9QiLbWNyyeQzXcdxyooC70jrDYyKdt02wF1m9pCkd4DRki4D/gvcFNvfBNwiaRIwl6BEMbO3Jd0FvAOsAk6NZgsknQY8BtQAI8zs7VwCudJ1HKfsKFTSSTN7A/hyhvLJBM+DhuXLgO9m6ety4PIM5Q8DDyeVyZVumbJBx/accdnRbLVNL8zgql/dzW77bMPAYwawYO5iAEZe+Sjjn32fjl06cMHVP2CbHTdj7P2vcO2laxJxXHbjCXTr2ZGamhreeuVD/nbxA9TXe9ailqJNG3H1A6cze8YCLhr6D8664lh2GvAFFn++DIArz7mTye9OY8NO63PmsGPovUV3VixfyVXn3sXHEz8D4Mgh+zDw2D2Q4NE7X+KBkc+XckhFxwxW1lev5dOVbply8vlH8Mpz73P56bfStraGdu1r2W2fbbh/5PPcO+LZtdquWL6Sm//yOFv27cVWfdfOBfrb029jyeLlAFxw9Q/46sCd+ffDrxdtHK2NI4//Kp9MmkmHDdckqL7piod4/tE312p37E8P4IN3p3HpKaPY7As9OfWib3He4OFs2bcXA4/dgzO+fTUrV9Zx2YiTeOnpd5n+8ZxiD6VkBPNC9Srd6h1ZBdNhw/bstPvWPHrPeABWraxbPVPKxPKlK3n7lY9YuXzVOnUphVvTtg1ta2sIawJOS9Bj484M2G9bHrvrpbxtt+jTi9dfnATAlMmz6LVZN7p035DN+/Ti/dc/YfmyldTX1fPmy5PZ++CdWlr0sqOAO9LKDle6ZcjGm3VlwdzF/Px33+Wv9/+MMy77Du3WrwXgiO/vxXVjzuDM3x7Nhp3WT9Tf5TeeyOgXfs3Sxct5/rE389/gNImfXHAEN13xL+obfLENOWsg1z50FkPP/ya16wWn/8nvTWPvQ3YEYJudN2ejTbrQY+POfPy/GezQf2s6dulAu/a17L7ftvTs3bnoYyklKZexJEclUlKlmy9QRFOCT1QDNW3b0Gf7TXjojnGc9q2rWbZ0BccO3Z+H7hjHj77+e0458i/Mnfk5Pz73G4n6O/+km/jePpdTu15bdtmzTwtL3zoZsP92zJ+ziElvr+2i+Y8/PsyPD/4Dp3/7L3Ts3IHvDt0fgLtveJoNOq7PX8ecyRGD9+aDd6ZRX298+sFM7h7+NJeP/DGXjjiJye9Mo76utf06Kdw24HKkZDbdtEARXydsnRsvaYyZvZPWbHXwCUmDCMEnjm0QfGIT4AlJ26RcOCqd2TMWMHvGAt5/I+wufO7RNzl26H7Mn7NodZtH736Zi68/PnGfK1es4sUn32GvA7fnvy9MLLTIrZ7td9uKPQ/cnt333ZbadrV02LAdZ//pOP7w8zsAWLmijsfvHc93TtwXgCWLlnPVuXetvn/kM+cx49Ngt3387vE8fncwLQ35+UBmz1hQ5NGUnmrOkVbKr4okgSIaG3yiKpg3exGzZixgs617APDlvfrwyQcz6daz4+o2XzloBz6Kq93ZaN9hvdX3tKlpw4D9tuXTyTNbTvBWzMg/PsIP97mc4/f7HcPOuJXXX5zEH35+B13Xes925OOJM4DgndK2NpgaBh47gDfHf8iSRcH+3rnbBgD07N2FvQ/eiWfG/LfIoyktwXuhJtFRiZTSe2F1YIlIpkARTQk+sRaShgJDAdq37VQQwYvBtZc+yC//eBy1tTVM/3QuV553Nz+94Ai+sG1vAD6bOo+rf3Pf6vajnjyHDhuGP+S9DtqB80+4kYXzl3DRdUOoXa8tknj9pQ/41+j8izxO4fjlld+jc7cNkMTkd6dxza/vBWDzPr34+e+PBTM+nvgZfz7v7tX3XPC3wXTqugGrVtZx7UX351xErUaqPV2PSrWaLeloYKCZnRSvfwjsYWanpbV5K7aZEq8/ICjmi4BxZnZrLL8JeMTM7iEHndtvbHttOSRXE6fMUF19qUVwGsELU25hwbIZzdKY3bfraQNHHpWo7e173vhKvoA35UYpZ7q5Akg0bNOY4BOO41QwLRXwplwopU03SaCIxgafcBynCnDvhRYg2mjXCRQh6RJggpmNoQnBJxzHqWzMxKoKVahJKOk24EyBIszsN2nnjQ4+4ThO5VPN5gWPveA4TllR7TZdV7qO45QdrnQdx3GKRLX76brSdRyn7KjmbcCudB3HKSvMYJUHMXccxykerd68IGkjYG9CRK+lwFsEX1rfo+k4TkFp1TZdSfsD5wLdCBkzZwLtgaOAL0q6B/iTmS1sYTkdx2lFWGtVusBhwI/N7JOGFTEWwuGEeLj3toBsjuO0Uqp5IS2ntdrMzs6kcGPdKjN7wMxc4TqOUzDMCpeuR9Lmkp6W9I6ktyWdHssvkjRV0mvxOCztnoxZabJluonxY16K5XfGWDJZyWdeOCtXvZldmW/QjuM4jUPUFc57YRXwczN7VVJH4BVJY2PdVWb2x7WenCUrTazOlunmitjXaEnXEzLeXJdNoHzmhY556h3HcQpOoWy6ZjYdmB7PP5f0LlkSHkRWZ6UBPozBtlJZaSaZ2WQASaOBI2N/BwDfi21GEeJ9N03pmtnF+QblOI5TSBoZe6GHpAlp18PNbHimhjGx7ZeBlwjeWKdJGgxMIMyG55E7K02mTDfdgflmtipD+4zkMy9cnavezH6Wq95xHKfRWLDrJmR2kswRkjYkLPifYWYLJV0HXBqexqXAn4ATmiZw48hnXnilGEI4juOkU0jvBUm1BIV7m5ndB2Bmn6XV/x14KF7mykqTqXwO0EVS2zjbzZvFJp95YVSuesdxnEJjBVxIi9nDbwLeTV/4l9Q72nsBvkXY8AUhK83tkq4kLKSlstKImOmGoFQHAd8zM5P0NCGzzWhCppsHc8mUdEdaT+AcYHvC5ggAzOyAJPc7juM0hgLmy90b+CHwpqTXYtmvgOMk9SOYFz4CfhKemz0rTaZMN7G/c4DRki4jbCK7KZdASWMv3AbcCXwDOJmgzWclvNdxHKdRFNB74XnIaKt4OENZ6p6MWWkyZbqJ5ZNZ4+GQl6Rz+O5mdhOw0sz+bWYnENwkmkU2Z+O0+rOiU/Mbkp6UtGVaXV2aY3PDhJaO41QoZkHpJjkqkaQz3ZXx/+mSvgFMI8RjaDKSasjubJziv0B/M1si6afA74FjY91SM+vXHBkcxylPWm3AmzQuk9QZ+DlwDdAJOLOZzx5ABmdjgi0FADN7Oq39OOAHzXym4zgVQAFtumVHIqVrZil3igXA/gV69qZkdjbOxonAI2nX7aNT9CpgmJk9kOkmSUOBoQDt23ZqjryO4xQBQ9RXcRDzRCOTNEpSl7TrrpJGtJhU6z7/B0B/4A9pxVtGp+jvAX+W9MVM95rZcDPrb2b916tZvwjSOo7TXCzhUYkkNS/sbGbzUxdmNk/Sl5v57FxOyKuRdBBwPrBv3A+dkmFq/H+ypGcI2/s+aKZMjuOUGqvueLpJ5/BtJHVNXUjqRvNT/YwnOhvHUGiDCI7Jq4mK/QbgCDObmVbeVVK7eN6D4IuXvgDnOE4lU8VT3aSK80/Ai5LujtffJYMfW2Mws1WZnI0lXUJIBTSGYE7YELg7bCzhEzM7AtgOuEFSPeGLY1gDrwfHcSqYap7pJl1IuzkuWqV8c79dCCWXydnYzH6Tdn5QlvteAHZq7vMdxyk/DKivr16l25glwm7AYjP7KzAr7kF2HMcpLAaYkh0VSNLYCxcSvAe+BPwDqAVuJdhSHcdxCko1++kmnel+CzgCWAxgZtPwrBKO47QUvpDGihjCzAAkbdCCMjmO06qp3LgKSUg6071L0g2EYL0/Bp4Ebmw5sRzHadW09pmumf1R0teBhQS77q/NbGye2xzHcRqPgVWx90JepRujgXWNSnZs3MhwvKR3zWy7FpfQcZxWSPUq3ZzmBUmDgLnAG5L+LelgYDJwKPD9IsjnOE5rpBWbFy4AdjOzSZJ2BV4Ejjazf7a8aI7jtFoqVKEmIZ/SXWFmkwDM7FVJE13hOo7ToqQ2R1Qp+ZTuRpLOSrvukn6dnl3TcRynUFTz5oh8SvfvrL0JouG14zhO4Wmt3gtmdnGxBHEcx0mhKp7p5vNeuCA9jm6G+gMkHV54sRzHabUk9VxIoJglbS7p6ZhV/G1Jp8fybpLGSpoY/+8ayyXp6pih/I3oQJDqa0hsP1HSkLTy3SS9Ge+5WjEObTbymRfeBB6StAx4FZgFtAf6Av2AJ4Df5h+64zhOUgoaQWwV8PPoCNAReEXSWOB44EkzGybpXOBc4ByCO2zfeOwBXAfsERM3pAJ/WexnjJnNi21+DLxECFU7kLXzOa5FzpmumT1oZnsDJwNvE4KNLyREGBtgZmea2awmvRSO4zjZKNBM18ymm9mr8fxz4F1CUtwjgVGx2SjgqHh+JHCzBcYRnAd6A4cAY81sblS0Y4GBsa6TmY0zMwNuTusrI0m3AU8EJiZp6ziO02zqE7fsERMspBhuZsMzNZS0FSGX4ktALzObHqtmAL3ieaYs5ZvmKZ+SoTwrzc1z5jiOU1ga56c7O2YFz4mkDYF7gTPMbGG62TU9gmIxKGlyeUkDJb0fDdDnZqg/XtIsSa/F46S0uoxGbcdxKh9ZsiNRX1ItQeHeZmb3xeLPommA+H8q8W22LOW5yjfLUJ6VkindGEjnbwTD9fbAcZK2z9D0TjPrF48b470po/YewADgwlxeFo7jVBiF814QcBPwboPNXGOA1GRtCPBgWvng6MWwJ7AgmiEeAw6Omci7AgcDj8W6hZL2jM8anNZXRnKaFyRdk2toZvazXPfnYQAwycwmx2eNJhixkyS8XG3UjveOJawY3tEMeRzHqT72Bn4IvCnptVj2K2AYIU74icDHwDGx7mHgMGASsAT4EYCZzZV0KTA+trskpX+AU4CRwPoEr4WsnguQ36Y7IU99c8hkmN4jQ7vvSPoa8D/gTDP7NMu9GY3XkoYCQwHa04G6iZMLILpTLB6b9lqpRXAawYBD5hekn0JZWM3sebLHiTwwQ3sDTs3S1whgRIbyCcCOSWXKtyNtVPq1pA5mtiRp5wXgn8AdZrZc0k8Irh0H5LlnLeJK5nCATupWxftcHKdKMKp6G3Aim66kvSS9A7wXr3eRdG0zn53NML0aM5tjZsvj5Y3AbknvdRyngqnieLpJF9L+TLCjzgEws9eBrzXz2eOBvpK2jtkoBhGM2KtJrS5GjiA4NkMWo3Yz5XEcp0wopPdCuZHYT9fMPm2wpbiuOQ82s1WSTiMoyxpghJm9LekSYIKZjQF+JukIwla+uYSte/mM2o7jVDoVqlCTkFTpfirpK4BFn7fTWTPrbDJm9jBhtTC97Ddp5+cB52W5N6NR23GcKqCKlW5S88LJhBW9TYFphGA3GVf4HMdxmkNS00JVmxfMbDaeiNJxnGLh3gv6gqR/xi25MyU9KOkLLS2c4zitk2qe6SY1L9wO3AX0BjYB7sZ3fzmO01K4yxgdzOwWM1sVj1sJwcwdx3EKS2u26cbAMgCPxChgownfL8fSwOvAcRynYFSoQk1CvoW0VwjDT1m1f5JWZ2Rx53Icx2kOSh7EvOLIF3th62IJ4jiO0xpIvCNN0o6EuLerbblmdnNLCOU4TiunFZsXAJB0IbAfQek+TAg8/jwhCZvjOE7hqOBFsiQk9V44mhB7coaZ/QjYBejcYlI5jtO6qWKXsaTmhaVmVi9plaROhHxCm+e7yXEcp0lUqEJNQlKlO0FSF+DvBI+GRcCLLSWU4zitF9GKvRdSmNkp8fR6SY8CnczsjZYTy3GcVkuV23TzbY7YNVedmb1aeJEcx2n1tFalC/wpR53RyHxljuM4iWitStfM9i+WII7jOClarXnBcRynJFSx0k3qp9siSBoo6X1Jk2JAnYb1V0l6LR7/kzQ/ra4urW5Mw3sdx6lQLHgvJDnyIWlEjAH+VlrZRZKmpumPw9Lqzov66H1Jh6SVZ9RVMbHuS7H8zphkNyclU7qSaoC/EXa3bQ8cJ2n79DZmdqaZ9TOzfsA1wH1p1UtTdWZ2RLHkdhynCBRuc8RIYGCG8qvS9MfDAFH/DAJ2iPdcK6kmj666IvbVB5gHnJhPoKSZIyTpB5J+E6+3kDQgyb05GABMMrPJZraCEDbyyBztj8MDpztOq6BQ8XTN7FlCJvEkHAmMNrPlZvYhMImgpzLqKoX06AcA98T7RwFH5XtI0pnutcBeBMUH8DlB8zeHTYFP066nxLJ1kLQlsDXwVFpxe0kTJI2TdFS2h0gaGttNWMnyZorsOE5RSD7T7ZH6+47H0IRPOE3SG9H80DWWZdNJ2cq7A/PNbFWD8pwkXUjbw8x2lfRfADObl8R2UUAGAfeYWV1a2ZZmNjXmantK0ptm9kHDG81sODAcoJO6VbF53nGqhMbFVZhtZv0b+YTrgEvjUy4luMae0Mg+mkzSme7KaNcwAEk9geZu1JvK2vEbNotlmRhEA9OCmU2N/08GngG+3Ex5HMcpA0TLpusxs8/MrM7M6gmhDVKm0mw6KVv5HKCLpLYNynOSVOleDdwPbCTpckJYx98mvDcb44G+cfVvPYJiXccLQdK2QFfSYj1I6iqpXTzvAewNvNNMeRzHKRNaUulK6p12+S0g5dkwBhgkqZ2krYG+wMtk0VVmZsDThCiMAEOAB/M9P2nshdskvUII7yjgKDN7N8m9OfpcJek04DGgBhhhZm9LugSYYGYpBTyIYNxOf4m3A26QVE/44hhmZq50HadaKJAhUNIdhFjgPSRNAS4E9pPULz7lI2Iasqh/7iJM4FYBp6ZMmpl0VXzEOcBoSZcB/wVuyivT2rosq+BbZCo3s0/y3lxGdFI320MHlloMpxE8Nu21UovgNIIBh3zKhNeXKX/L7HTotbn1PfasRG3fuOasV5pg0y0pSRfS/sWaBJXtCZ4E7xP82RzHcQpHa44ylsLMdkq/jtHHTsnS3HEcp3m0dqXbEDN7VdIehRbGcRwHPIg5ktINLG2AXYFpLSKR4zitnlZvXgA6pp2vIth47y28OI7jtHoqOOlkEvIq3bgpoqOZ/aII8jiO47RepSupbfSn3btYAjmO07pJ7UirVvLNdF8m2G9TMWvvBhanKs3svmw3Oo7jNBXVV6/WTWrTbU/YZ3wAa/x1jbXj2zqO4zSfVm7T3Sh6LrzFGmWboopfFsdxSklrNi/UABuytrJNUcUvi+M4JaWKtUs+pTvdzC4piiSO4ziR1jzTbVbgCsdxnCbRipWuh+RyHKe4WCveBmxmSRO6OQWktl0tV/77EmrbtaWmbQ3P3TuOmy+6C4AfXXYcXzt6T+rr6vnn9Y/zwDWPsPmXNuEXI06lz65b848L7uCeP/0TgM222YQLRp+5ut+Nv7ARoy68k/v/8nBJxlV9rIe63Q5aD2gLyx/FFl29ulYdfw3rfweb2S8UtOmNOv8e2nQC2mCf/xFW/BuoRZ0uhdodgXrs88tgxcugDVC3tIQpNb1g6Rjs88uLN8QS0Nr9dJ0SsHL5Ss4+8GKWLV5GTdsarnruUsY/8l+22G5Tem7WnRO2OwMzo0vPTgB8PncRfzt9BHsftXaC5in/m8bJu54NQJs2bbhjyg385/6Xiz6e6mUFNm8w2BKgLeo2GmqfhZWvQdsdo3JdgzY8BVv2CCy9HWr6oG5/x2btDx2OAcDmHA5tuqGuN2Fzvg22GJtzxJr7u9+PLXu8iOMrIQnifFcqSdP1OEVm2eJlALStraFtbQ1mxuEnH8Ktl95DKvD8/FkLV///vwkfsGrlqqz9ffnAHZn+wQxmfjK75YVvTdiSeNIW1JZgjGyDOp6Dff77ddu32XDN/3UzAVBNH2xFzEZVPxfqF0LtTmvfV7MVtOkOK8e3wCDKj5ZM11NqfKZbprRp04ZrJ1zBJn02Zsy1j/Ley5PY5Iu92O/Yr7D3UQOYP2sh154+gqmTZiTqb79Be/P06P+0sNStkTao+wNQswUsuQ1Wvg4dhmDLn4T6WWu1tEVXo67/gA4/BK2PzR0Syle9h9ofiC17CGp6BzNDm97AG2tubn84LPtX8YZVSqp8c0RJZ7ox5/xMSW9lqZekqyVNijnqd02rGyJpYjyGFE/q4lBfX8/Ju57NcZv/hC/t3oetdtic2na1rFi2glMHnMsjNz7Bz29KFke+bW1b9vpmf/5994v5GzuNpB6bcwQ266tQuzPU7o7aD4Qlt6zbtP3h2NL7sFlfxeadhLr8ERAsvQfqZqDu96OO58PKV4G6tW7V+t/Alj5UlBGVA6pPdlQipTYvjAQG5qg/lJCRsy8wlJCvHkndCAnm9iCkT75QUtcWlbRELF6whNefeZv+A/sxa8ocnr8v2GSfv/9lvrDzlon62P3Qfkx69UPmz1zQkqK2buxzbMVLsN4eULMl6vkE6vk0aH3U4wkAtP53YVlcxFz5GtAO2nQF6rDPfxuU9/yfgjrBqo/W9N12W6AGVr1Na8GVbgthZs8CuTwkjgRutsA4Qo753sAhwFgzm2tm84Cx5FbeFUXnHp3YoHMHANZrvx67HrQzn743lRceHM8u+4e0dDvvuz1T/pcsjvz+g/bh6dHPt5i8rRZ1A6VCTbdD7b4Cq97GZn0Fm7V/WCSzpdjsg0KT+mnQ7ivhvOaLweuhfi7QHrR+KF9vb6AO6iateUz7w2FZ65nlBvOCJTsqkHK36W4KfJp2PSWWZStfB0lDCbNk2tOhZaQsMN16d+GXI0+jTU0b1EY8e/eLvPSvV3nr+fc479bT+c4Zh7N00TKu/PH1AHTt1YW/jR9Gh07rY/XGt0//BiftcCZLPl9K+w7t2O3rO/Pnk4eXeFRVSE3P4AJGG6BN8ExY/nTW5rZwGOp8GXQ4PlwvODf20x11HQEY1M3A5jcIXd3+MGzeSS0xgrKlUhfJklDuSrfZmNlwYDiEFOwlFicRH775CT/d7ZfrlC9esIQLvvm7dcrnfTaf721xcsa+li1Zznd6nlBwGR1g1fvYnCNzNlntowtQNwmbO2jdRnVTsdmHZO9j9gFNFLCCKdBfqqQRwOHATDPbMZZ1A+4EtgI+Ao4xs3mSBPwFOAxYAhxvZq/Ge4YAF8RuLzOzUbF8N4KZdH3gYeB0s9xT8FLbdPMxFdg87XqzWJat3HGcCie1OaJALmMjWdf0eC7wpJn1BZ6M19C0NaTrgB+n3ZfXzFnuSncMMDh6MewJLDCz6cBjwMGSusbBHxzLHMepdMxQfbIjf1cZ142OBEbF81HAUWnlideQYl0nMxsXZ7c3p/WVlZKaFyTdAewH9JA0hfBtUgtgZtcTpuuHAZMI0/0fxbq5ki4FUp7il/iWZcepIpKbF3pImpB2PTyaFHPRK07eAGYAveJ5Y9eQNo3nDctzUlKla2bH5ak34NQsdSOAES0hl+M4paURC2mzzax/U59jZiYVd9mu3M0LjuO0Ngyot2RH0/gsmgaI/8+M5Y1dQ5oazxuW58SVruM45YclPJrGGCC1i3UI8GBaeeI1pFi3UNKe0fNhcFpfWal6lzHHcSqPQv3gz7JuNAy4S9KJwMfAMbF5U9aQTmGNy9gj8ciJK13HccqOQqVgz7FutE6ChqasIZnZBGDHxsjkStdxnPKiyqOMudJ1HKesCJsjqlfrutJ1HKf8qNAIYklwpes4TtnhM13HcZxi4TZdx3GcYpIsrkKl4krXcZzyw80LjuM4RcIqNxVPElzpOo5TfvhM13Ecp4hUr851pes4Tvmh+uq1L7jSdRynvDB8c4TjOE6xEOabIxzHcYpKFSvdkgYxlzRC0kxJb2Wp/76kNyS9KekFSbuk1X0Uy19rkCPJcZxKxyzZUYGUOnPESHKnLP4Q2NfMdgIuBRomnNvfzPo1J0eS4zhlRsqmm+SoQEqdmPJZSVvlqH8h7XIca+cjchynSqlm74VSz3Qbw4msnQrDgMclvSJpaIlkchyn4CQ0LVSoeaEiFtIk7U9QuvukFe9jZlMlbQSMlfSemT2b4d6hwFCA9nQoiryO4zQDo2IVahLKfqYraWfgRuBIM5uTKjezqfH/mcD9wIBM95vZcDPrb2b9a2lXDJEdx2kuVWzTLWulK2kL4D7gh2b2v7TyDSR1TJ0TUiJn9IBwHKfykFmioxIpqXkhS3rkWgAzux74DdAduDaklWdV9FToBdwfy9oCt5vZo0UfgOM4LUOFKtQklNp7IVt65FT9ScBJGconA7use4fjOBWPGdQVznYg6SPgc6COOHGT1A24E9gK+Ag4xszmKczk/gIcBiwBjjezV2M/Q4ALYreXmdmopshT1uYFx3FaKYX3Xmjo038u8KSZ9QWejNcAhwJ94zEUuA4gKukLgT0I60cXSuralKG50nUcp/xoeZexI4HUTHUUcFRa+c0WGAd0kdQbOAQYa2ZzzWweMJbcG7uy4krXcZzywoB6S3Yk77GhT38vM5sez2cQ1okANgU+Tbt3SizLVt5oKsJP13Gc1oSBJbbp9mgQe2W4mTUMF7COT/9aTzMzSUVbuXOl6zhOeWE0ZiFtdr7YK+k+/ZJSPv2fSeptZtOj+WBmbD4V2Dzt9s1i2VSCp1V6+TNJhUzHzQuO45QfBbLp5vDpHwMMic2GAA/G8zHAYAX2BBZEM8RjwMGSusYFtINjWaPxma7jOOVH4fx0M/r0SxoP3CXpROBj4JjY/mGCu9gkgsvYj4I4NlfSpcD42O4SM5vbFIFc6TqOU2YULphNNp/+GFLgwAzlBpyapa8RwIjmyuRK13Gc8sKAKg7t6ErXcZzyw7cBO47jFIvCbgMuN1zpOo5TXhhYcj/disOVruM45Ufy3WYVhytdx3HKD7fpOo7jFAkz915wHMcpKj7TdRzHKRaG1dWVWogWw5Wu4zjlRSq0Y5XiStdxnPKjil3GShplTNIISTMlZczkK2k/SQskvRaP36TVDZT0vqRJks7NdL/jOJWHAVZviY5KpNQz3ZHAX4Gbc7R5zswOTy+QVAP8Dfg6IYL7eEljzOydlhLUcZwiYY0KYl5xlDob8LOStmrCrQOASTGCEJJGE3IbudJ1nCrAF9JKy16SXgemAb8ws7fJnK9oj0w3x5xIqbxIy5+wezKaMiqcHsDsUgvREtT0rtqxVeu4vtTcDj5n3mNP2D09EjavuNew3JXuq8CWZrZI0mHAA4TUyImJ+ZKGA0iakC+1RyVSreOC6h1bNY+ruX2YWZOy7FYKZZ2ux8wWmtmieP4wUCupB9nzGDmO45Q1Za10JW2smGdD0gCCvHMIKTP6Stpa0nrAIEJuI8dxnLKmpOYFSXcQMmz2kDQFuBCoBTCz64GjgZ9KWgUsBQbFdBqrJJ1GSAxXA4yItt58NEzNXC1U67igesfm42qlyKp4j7PjOE65UdbmBcdxnGrDla7jOE4RqWqlK6mbpLGSJsb/u2ZpV5e21bhsF+TybX2W1E7SnbH+pSZuPCk6CcZ1vKRZae/RSaWQs7Ek2OYuSVfHcb8haddiy9gUmrN936lypQucCzxpZn2BJ+N1JpaaWb94HFE88ZKTtvX5UGB74DhJ2zdodiIwz8z6AFcBVxRXysaTcFwAd6a9RzcWVcimMxLI5XN6KMHvvC9hA891RZCpEIwk97ggbN9PvV+XFEGmiqHale6RwKh4Pgo4qnSiNJvVW5/NbAWQ2vqcTvp47wEOTLnclTFJxlWRmNmzwNwcTY4EbrbAOKCLpN7Fka7pJBiXk4NqV7q9zGx6PJ8B9MrSrr2kCZLGSTqqOKI1mkxbnzfN1sbMVgELgO5Fka7pJBkXwHfiT/B7JG2eob4SSTr2SmQvSa9LekTSDqUWppwo923AeZH0BLBxhqrz0y/MzCRl84/b0symSvoC8JSkN83sg0LL6jSZfwJ3mNlyST8hzOYPKLFMTnaavX2/mql4pWtmB2Wrk/SZpN5mNj3+bJuZpY+p8f/Jkp4BvgyUm9JNsvU51WaKpLZAZ8IOvnIm77jMLH0MNwK/L4JcxaAqt7Ob2cK084clXSuph5lVXHCalqDazQtjgCHxfAjwYMMGkrpKahfPewB7U54hIpNsfU4f79HAU1b+u1/yjquBnfMI4N0iyteSjAEGRy+GPYEFaeawiiXH9n2HKpjp5mEYcJekE4GPgWMAJPUHTjazk4DtgBsk1RM+HMPKMRi6mWXc+izpEmCCmY0BbgJukTSJsNAxqHQSJyPhuH4m6QhgFWFcx5dM4EaQYJv7w8BhwCRgCfCj0kjaOJqxfd/BtwE7juMUlWo3LziO45QVrnQdx3GKiCtdx3GcIuJK13Ecp4i40nUcxykirnTLnLQIaG9JultSh2b0NVLS0fH8xiyBZVJt95P0lSY846Po75yovEGbRY181kWSfpGl7gxJg+P5SEkfxtfxVUl7NeY5WfrvL+nqHPWbSLqnGf0/oSxR8ZzKxpVu+ZOKgLYjsAI4Ob0y7jxrNGZ2Uh5/5P2ARivdciC+JicAt6cVn21m/QiR5m7IcE9NY55hZhPM7Gc56qeZ2dGN6bMBtwCnNON+p0xxpVtZPAf0ibPQ5xRi/74jqUbSHySNj0FhfgKr47X+VSFW7RPARqmOJD0TN4mk4tm+GgOUPKkQh/dk4Mw4O/yqpJ6S7o3PGC9p73hvd0mPS3pb0o1A3qhmkh6Q9Eq8Z2iDuqti+ZOSesayL0p6NN7znKRt8zziAODVGPSnIc8CfWK/H0m6QtKrwHclHSzpxfha3C1pw9hud0kvxNfnZUkd43vwUKzfV2tix/431m+lGG9WUntJ/5D0ZqzfP5YfL+m+OLaJktK3N48Bjsv3WjqVR7XvSKsa4uztUODRWLQrsKOZfRgV1wIz211hS/N/JD1OiCHxJUKc2l6E7c0jGvTbE/g78LXYVzczmyvpemCRmf0xtrsduMrMnpe0BWEH2XaE3UjPm9klkr5BiOmbjxPiM9YHxku6N8ZX2ICwC+1MhcDXFwKnEZIdnmxmEyXtAVxL7oA3ewOvZKn7JvBm2vUcM9s1mj7uAw4ys8WSzgHOkjQMuBM41szGS+pE2GWVzi+AU83sP1FRL2tQfyoh5tJO8QvjcUnbxLp+hPdpOfC+pGvM7FMzm6cQlL57g9gTToXjSrf8WV/Sa/H8OcJW368AL5vZh7H8YGBnRXstIdBNX+BrhOhcdcA0SU9l6H9P4NlUX2aWLU7qQcD2WhOet1NUMF8Dvh3v/ZekeQnG9DNJ34rnm0dZ5wD1BAUHcCtwX3zGV4C7057dLk//vVk3PsMfJF0AzGLtL4bU8/YkfDn9Jz5nPeBFwpfWdDMbH8e4EEBrhyn+D3ClpNuA+8xsSoP6fYBr4v3vSfoYSCndJ81sQezzHWBL1oR7nAlsgsctqCpc6ZY/S6MtcjXxD3pxehHwf2b2WIN2hxVQjjbAnma21ixOjYyRLmk/ggLfy8yWKER1a5+lucXnzm/4GuRhaYY+zzazTAtbqddRwFgzW+snvaSd8j3MzIZJ+hchjsJ/JB3CurPdbCxPO69j7b/J9qw7q3YqHLfpVgePEQKM1AJI2kbSBgT75bEKNt/ewP4Z7h0HfE3S1vHebrH8c6BjWrvHgf9LXUjqF0+fBb4Xyw4F8q24dyakFFoSf2rvmVbXhhAshdjn83Fm+aGk78ZnSNIueZ7xLtFu2wjGAXtLStl7N4gmgPeB3pJ2j+Ud1WDxUtIXzexNM7uCEDWtoc35OeD7se02wBax36wofJttDHzUyHE4ZY4r3ergRoK99tW4eHMDYcZ0PzAx1t1M+Lm8FmY2i5Cf6z5Jr7Pm5/Y/gW/FxaGvAj8D+iss1L3DGi+KiwlK+22CmeGTPLI+CrSV9C4hCty4tLrFwIA4hgOAVG6t7wMnRvneJn86n0cIZo/ExNfheOAOSW8QXqttYwqhY4Fr4vPHsu4s+gwFl743gJXx+elcC7SR9Cbh9T3ezJaTm92AcVkWA50KxqOMOVWJpPuBX5rZxFLL0hQk/QUYY2ZPlloWp7D4TNepVs4lLKhVKm+5wq1OfKbrOI5TRHym6ziOU0Rc6TqO4xQRV7qO4zhFxJWu4zhOEXGl6ziOU0T+H8aBkm59Z5B8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnb_cfm = confusion_matrix(y_test, predictions)\n",
    "plt.imshow(log_cfm, interpolation='nearest')\n",
    "\n",
    "for i, j in itertools.product(range(mnb_cfm.shape[0]), range(mnb_cfm.shape[1])):\n",
    "    plt.text(j, i, mnb_cfm[i, j],\n",
    "             horizontalalignment=\"center\",\n",
    "             color=\"white\")\n",
    "\n",
    "plt.ylabel('True label (Recall)')\n",
    "plt.xlabel('Predicted label (Precision)')\n",
    "plt.title('Multinomial Naive Bayes | Confusion Matrix')\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TfidfVectorizer with Logistic Regression\n",
    "\n",
    "Some words might appear quite frequently but have a very less or negligible meaningful information about the sentiment for a particular review. Instead of using occurance counting we will use tf-idf transform to scale down the impact of frequently appearing words in a given corpus.\n",
    "\n",
    "In sklearn library we will use TfidfVectorizer which implements both tokenization and tf-idf weighted counting in a single class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features : 13630 \n",
      "\n",
      "Show some feature names : \n",
      " ['aa', 'bbw', 'choke', 'definately', 'estimation', 'glittery', 'infestation', 'lpmp', 'nix', 'plaque', 'repetitive', 'slippage', 'terra', 'vetiver']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Software\\envs\\myenv\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting and transforming the training data to a document-term matrix using TfidfVectorizer \n",
    "tfidf = TfidfVectorizer(min_df=5) #minimum document frequency of 5\n",
    "X_train_tfidf = tfidf.fit_transform(x_train)\n",
    "print(\"Number of features : %d \\n\" %len(tfidf.get_feature_names())) #1722\n",
    "print(\"Show some feature names : \\n\", tfidf.get_feature_names()[::1000])\n",
    "\n",
    "# Logistic Regression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 10 features with the smallest coefficients :\n",
      "['unfortunately' 'disappoint' 'waste' 'ok' 'return' 'maybe' 'okay'\n",
      " 'disappointment' 'hop' 'useless']\n",
      "\n",
      "Top 10 features with the largest coefficients : \n",
      "['love' 'great' 'highly' 'amaze' 'perfect' 'excellent' 'awesome'\n",
      " 'skeptical' 'compliment' 'happy']\n"
     ]
    }
   ],
   "source": [
    "# Have a look at the top 10 features with the smallest and largest coefficients\n",
    "feature_names = np.array(tfidf.get_feature_names())\n",
    "sorted_coef_index = lr.coef_[0].argsort()\n",
    "print('\\nTop 10 features with the smallest coefficients :\\n{}\\n'.format(feature_names[sorted_coef_index[:10]]))\n",
    "print('Top 10 features with the largest coefficients : \\n{}'.format(feature_names[sorted_coef_index[:-11:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy on validation set: 0.8572\n",
      "\n",
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.53      0.63     11112\n",
      "           1       0.88      0.95      0.91     38514\n",
      "\n",
      "    accuracy                           0.86     49626\n",
      "   macro avg       0.82      0.74      0.77     49626\n",
      "weighted avg       0.85      0.86      0.85     49626\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[ 5935  5177]\n",
      " [ 1912 36602]]\n"
     ]
    }
   ],
   "source": [
    "# Evaluating on the validaton set\n",
    "predictions = lr.predict(tfidf.transform(x_test))\n",
    "modelEvaluation(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfm = confusion_matrix(y_test, predictions)\n",
    "plt.imshow(log_cfm, interpolation='nearest')\n",
    "\n",
    "for i, j in itertools.product(range(cfm.shape[0]), range(cfm.shape[1])):\n",
    "    plt.text(j, i, cfm[i, j],\n",
    "             horizontalalignment=\"center\",\n",
    "             color=\"white\")\n",
    "\n",
    "plt.ylabel('True label (Recall)')\n",
    "plt.xlabel('Predicted label (Precision)')\n",
    "plt.title('Logistic Regression with TFIDF | Confusion Matrix')\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TfidfVectorizer using Linear SVM by using SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier()"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting and transforming the training data to a document-term matrix using TfidfVectorizer \n",
    "tfidf = TfidfVectorizer(min_df=5) #minimum document frequency of 5\n",
    "X_train_tfidf = tfidf.fit_transform(x_train)\n",
    "\n",
    "# SVM\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "clf = SGDClassifier(loss=\"hinge\", penalty=\"l2\")\n",
    "clf.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 10 features with the smallest coefficients :\n",
      "['ok' 'unfortunately' 'okay' 'waste' 'disappoint' 'maybe' 'return' 'hop'\n",
      " 'bad' 'nothing']\n",
      "\n",
      "Top 10 features with the largest coefficients : \n",
      "['love' 'great' 'amaze' 'perfect' 'happy' 'without' 'best' 'year'\n",
      " 'definitely' 'highly']\n"
     ]
    }
   ],
   "source": [
    "# Have a look at the top 10 features with the smallest and largest coefficients\n",
    "feature_names = np.array(tfidf.get_feature_names())\n",
    "sorted_coef_index = clf.coef_[0].argsort()\n",
    "print('\\nTop 10 features with the smallest coefficients :\\n{}\\n'.format(feature_names[sorted_coef_index[:10]]))\n",
    "print('Top 10 features with the largest coefficients : \\n{}'.format(feature_names[sorted_coef_index[:-11:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy on validation set: 0.8393\n",
      "\n",
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.35      0.50     11112\n",
      "           1       0.84      0.98      0.90     38514\n",
      "\n",
      "    accuracy                           0.84     49626\n",
      "   macro avg       0.84      0.67      0.70     49626\n",
      "weighted avg       0.84      0.84      0.81     49626\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[ 3917  7195]\n",
      " [  781 37733]]\n"
     ]
    }
   ],
   "source": [
    "# Evaluating on the validaton set\n",
    "predictions = clf.predict(tfidf.transform(x_test))\n",
    "modelEvaluation(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline and GridSearch\n",
    "\n",
    "We will build a pipeline in the Sklearn library to streamline the workflow and use GridSearch on the pipeline model to implement hyperparameter tuning for both the vectorizer and classifier at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Software\\envs\\myenv\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best paramenter set is : \n",
      " {'lr__C': 10, 'tfidf__max_features': None, 'tfidf__min_df': 1, 'tfidf__ngram_range': (1, 2), 'tfidf__stop_words': None}\n",
      "\n",
      "Accuracy on validation set: 0.8734\n",
      "\n",
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.62      0.69     11112\n",
      "           1       0.90      0.95      0.92     38514\n",
      "\n",
      "    accuracy                           0.87     49626\n",
      "   macro avg       0.83      0.78      0.80     49626\n",
      "weighted avg       0.87      0.87      0.87     49626\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[ 6861  4251]\n",
      " [ 2034 36480]]\n"
     ]
    }
   ],
   "source": [
    "# Building a pipeline\n",
    "estimators = [(\"tfidf\", TfidfVectorizer()), (\"lr\", LogisticRegression())]\n",
    "model = Pipeline(estimators)\n",
    "\n",
    "\n",
    "# Grid search\n",
    "params = {\"lr__C\":[0.1, 1, 10], #regularization param of logistic regression\n",
    "          \"tfidf__min_df\": [1, 3], #min count of words \n",
    "          \"tfidf__max_features\": [1000, None], #max features\n",
    "          \"tfidf__ngram_range\": [(1,1), (1,2)], #1-grams or 2-grams\n",
    "          \"tfidf__stop_words\": [None, \"english\"]} #use stopwords or don't\n",
    "\n",
    "grid = GridSearchCV(estimator=model, param_grid=params, scoring=\"accuracy\", n_jobs=-1)\n",
    "grid.fit(x_train, y_train)\n",
    "print(\"The best paramenter set is : \\n\", grid.best_params_)\n",
    "\n",
    "\n",
    "# Evaluate on the validaton set\n",
    "predictions = grid.predict(x_test)\n",
    "modelEvaluation(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfm = confusion_matrix(y_test, predictions)\n",
    "plt.imshow(log_cfm, interpolation='nearest')\n",
    "\n",
    "for i, j in itertools.product(range(cfm.shape[0]), range(cfm.shape[1])):\n",
    "    plt.text(j, i, cfm[i, j],\n",
    "             horizontalalignment=\"center\",\n",
    "             color=\"white\")\n",
    "\n",
    "plt.ylabel('True label (Recall)')\n",
    "plt.xlabel('Predicted label (Precision)')\n",
    "plt.title('Logistic Regression with TFIDF | Confusion Matrix')\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word2Vec\n",
    "\n",
    "Another common approach of word embedding is the prediction based embedding like Word2Vec model. Briefly, Word2Vec is a combination of two techniques: Continuous Bag of Words (CBoW) and Skip-Gram model. Both are Shallow Neural Networks which learn weights for the word vector representations.\n",
    "\n",
    "Here, we will train Word2Vec model to create our own word vector representation using gensim library. Then we will fit the feature vectors of the reviews to the Random Forest Classifier. Here's the workflow of this part: -\n",
    "\n",
    "Parse review text to sentences (Word2Vec model takes a list of sentences as inputs).\n",
    "Create vocabulary list using Word2Vec model.\n",
    "Transform each review into numerical representation by computing average feature vectors of words therein\n",
    "Fit the average feature vectors to Random Forest Classifier\n",
    "Parsing Review into Sentences\n",
    "\n",
    "Word2Vec model takes a list of sentences as inputs and outputs word vector representations for words in the vocabulary list created. Before we train the Word2Vec model, we have to parse reviews in the training set into sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\aashi\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup \n",
    "import re\n",
    "def cleanText(raw_text, remove_stopwords=False, stemming=False, split_text=False, \\\n",
    "             ):\n",
    "    '''\n",
    "    Convert a raw review to a cleaned review\n",
    "    '''\n",
    "    text = BeautifulSoup(raw_text, 'lxml').get_text()  #remove html\n",
    "    letters_only = re.sub(\"[^a-zA-Z]\", \" \", text)  # remove non-character\n",
    "    words = letters_only.lower().split() # convert to lower case \n",
    "    \n",
    "    if remove_stopwords: # remove stopword\n",
    "        stops = set(stopwords.words(\"english\"))\n",
    "        words = [w for w in words if not w in stops]\n",
    "        \n",
    "    if stemming==True: # stemming\n",
    "#         stemmer = PorterStemmer()\n",
    "        stemmer = SnowballStemmer('english') \n",
    "        words = [stemmer.stem(w) for w in words]\n",
    "        \n",
    "    if split_text==True:  # split text\n",
    "        return (words)\n",
    "    \n",
    "    return( \" \".join(words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148851 parsed sentence in the training set\n",
      "\n",
      "Show a parsed sentence in the training set : \n",
      " ['make', 'makeup', 'go', 'smooth', 'seem', 'tame', 'line', 'bite', 'crazy', 'smell', 'though', 'smell', 'kind', 'toxic']\n"
     ]
    }
   ],
   "source": [
    "# Splitting review text into parsed sentences using NLTK's punkt tokenizer\n",
    "\n",
    "tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
    "\n",
    "def parseSent(review, tokenizer, remove_stopwords=False):\n",
    "    '''\n",
    "    Parse text into sentences\n",
    "    '''\n",
    "    raw_sentences = tokenizer.tokenize(review.strip())\n",
    "    sentences = []\n",
    "    for raw_sentence in raw_sentences:\n",
    "        if len(raw_sentence) > 0:\n",
    "            sentences.append(cleanText(raw_sentence, remove_stopwords, split_text=True))\n",
    "    return sentences\n",
    "\n",
    "\n",
    "# Parsing each review in the training set into sentences\n",
    "sentences = []\n",
    "for review in x_train:\n",
    "    sentences += parseSent(review, tokenizer)\n",
    "    \n",
    "print('%d parsed sentence in the training set\\n'  %len(sentences))\n",
    "print('Show a parsed sentence in the training set : \\n',  sentences[10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "w2v = Word2Vec()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating Vocabulary List using Word2Vec Model\n",
    "\n",
    "As we have a set of cleaned and parsed sentences from the training data, we will train our own word vector representations by specifying the embedding dimension (= length of feature vector)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41325     question rogaine work need start use lose hair...\n",
       "35450     price guess really complain perfume smell stro...\n",
       "112087    polish great really really smelly perfect cove...\n",
       "97533     eh thank love pale pink hue lip treatment saw ...\n",
       "31285                              good healthy strong nail\n",
       "                                ...                        \n",
       "152315    product difficult use attachements hard put tu...\n",
       "176963    skin problem blackhead pimple area need improv...\n",
       "117952    well love wear eyelash throughout day go try y...\n",
       "173685    something really wrong brush build liquid thic...\n",
       "43567     first time use product burn eye eye bloodshot ...\n",
       "Name: processed_review, Length: 148876, dtype: object"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Word2Vec model ...\n",
      "\n",
      "Number of words in the vocabulary list : 14323 \n",
      "\n",
      "Show first 10 words in the vocabulary list  vocabulary list: \n",
      " ['use', 'hair', 'product', 'like', 'skin', 'get', 'good', 'look', 'one', 'make']\n"
     ]
    }
   ],
   "source": [
    "# Fitting parsed sentences to Word2Vec model \n",
    "\n",
    "num_features = 100  #embedding dimension                     \n",
    "min_word_count = 5                \n",
    "num_workers = 4       \n",
    "context = 5                                                                                          \n",
    "downsampling = 1e-3 \n",
    "\n",
    "print(\"Training Word2Vec model ...\\n\")\n",
    "w2v = Word2Vec(sentences, workers=num_workers, size=num_features, min_count = min_word_count,\n",
    "                window = context)\n",
    "w2v.init_sims(replace=True)\n",
    "w2v.save(\"w2v_300features_10minwordcounts_10context\") #save trained word2vec model\n",
    "\n",
    "print(\"Number of words in the vocabulary list : %d \\n\" %len(w2v.wv.index2word)) #4016 \n",
    "print(\"Show first 10 words in the vocabulary list  vocabulary list: \\n\", w2v.wv.index2word[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Averaging Feature Vectors\n",
    "\n",
    "Now we have created a vocabulary list of words with each word having a word representation(ie. feature vector of dim 300).\n",
    "\n",
    "To find a numerical representation for a review, we run through each word in a review text. For words appear in the vocabulary list, we compute the average feature vectors of all those words. The average feature vector is the numerical represenation of the review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transfroming the training data into feature vectors\n",
    "\n",
    "def makeFeatureVec(review, model, num_features):\n",
    "    '''\n",
    "    Transform a review to a feature vector by averaging feature vectors of words \n",
    "    appeared in that review and in the vocabulary list created\n",
    "    '''\n",
    "    featureVec = np.zeros((num_features,),dtype=\"float32\")\n",
    "    nwords = 0.\n",
    "    index2word_set = set(model.wv.index2word) #index2word is the vocabulary list of the Word2Vec model\n",
    "    isZeroVec = True\n",
    "    for word in review:\n",
    "        if word in index2word_set: \n",
    "            nwords = nwords + 1.\n",
    "            featureVec = np.add(featureVec, model[word])\n",
    "            isZeroVec = False\n",
    "    if isZeroVec == False:\n",
    "        featureVec = np.divide(featureVec, nwords)\n",
    "    return featureVec\n",
    "\n",
    "\n",
    "def getAvgFeatureVecs(reviews, model, num_features):\n",
    "    '''\n",
    "    Transform all reviews to feature vectors using makeFeatureVec()\n",
    "    '''\n",
    "    counter = 0\n",
    "    reviewFeatureVecs = np.zeros((len(reviews),num_features),dtype=\"float32\")\n",
    "    for review in reviews:\n",
    "        reviewFeatureVecs[counter] = makeFeatureVec(review, model,num_features)\n",
    "        counter = counter + 1\n",
    "    return reviewFeatureVecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-187-46b6f43eb48b>:15: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  featureVec = np.add(featureVec, model[word])\n"
     ]
    }
   ],
   "source": [
    "trainVector = getAvgFeatureVecs(x_train, w2v, num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set : 148876 feature vectors with 100 dimensions\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set : %d feature vectors with %d dimensions\" %trainVector.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-187-46b6f43eb48b>:15: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  featureVec = np.add(featureVec, model[word])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set : 49626 feature vectors with 100 dimensions\n"
     ]
    }
   ],
   "source": [
    "testVector = getAvgFeatureVecs(x_test, w2v, num_features)\n",
    "print(\"Validation set : %d feature vectors with %d dimensions\" %testVector.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy on validation set: 0.7757\n",
      "\n",
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.48      0.02      0.04     11112\n",
      "           1       0.78      0.99      0.87     38514\n",
      "\n",
      "    accuracy                           0.78     49626\n",
      "   macro avg       0.63      0.51      0.46     49626\n",
      "weighted avg       0.71      0.78      0.69     49626\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[  235 10877]\n",
      " [  252 38262]]\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "rf.fit(trainVector, y_train)\n",
    "predictions = rf.predict(testVector)\n",
    "modelEvaluation(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy on validation set: 0.7763\n",
      "\n",
      "Classification report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.00      0.01     11112\n",
      "           1       0.78      1.00      0.87     38514\n",
      "\n",
      "    accuracy                           0.78     49626\n",
      "   macro avg       0.70      0.50      0.44     49626\n",
      "weighted avg       0.74      0.78      0.68     49626\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[   31 11081]\n",
      " [   19 38495]]\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(trainVector, y_train)\n",
    "predictions = lr.predict(testVector)\n",
    "modelEvaluation(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Applying LSTM\n",
    "\n",
    "Long Short Term Memory(LSTM) Networks are a special kind of the Recurrent Neural Networks(RNN) capable of learning long-term dependencies. LSTM can be very useful in text mining problems as it involves dependencies in the sentences which can be caught in the \"memory\" of the LSTM. Here, we will train a simple LSTM and LSTM with Word2Vec embedding for classifying the reviews into positive and negative sentiments using Keras library.\n",
    "\n",
    "## Simple LSTM\n",
    "\n",
    "We need to preprocess the text data to 2D tensor before we begin fitting it into a simple LSTM. Firstly we will tokenize the corpus by considering only top words (top_words = 20000) and transforming reviews to numerical sequences using the trained tokenizer. Lastly we will make it sure that all the numerical sequences have the same length (maxlen=100) for modelling by truncating the long reviews and padding shorter reviews having zero values.\n",
    "\n",
    "For constructing a simple LSTM, we will use embedding class in Keras to building up the first layer. This embedding layer converts numerical sequence of words into a word embedding. We should also note that the embedding class provides a convenient way to map discrete words into a continuous vector space but it doesn't take the semantic similarity of the words into account. The next layer is the LSTM layer with 128 memory units. Finally, we will use a dense output layer with a single neuron and a sigmoid activation function to make 0 or 1 prediction for the two classes (positive sentiment and negative sentiment). As it is a binary classification problem log loss is used as the loss function(binary_crossentropy in Keras). ADAM optimization algorithm will be used.\n",
    "\n",
    "Here's the workflow in this part: -\n",
    "\n",
    "Prepare X_train and X_test to 2D tensor.\n",
    "Train a simple LSTM (embedding layer => LSTM layer => dense layer).\n",
    "Compile and fit the model using log loss function and ADAM optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_words = 20000 \n",
    "maxlen = 100 \n",
    "batch_size = 32\n",
    "nb_classes = 3\n",
    "nb_epoch = 3\n",
    "\n",
    "\n",
    "# Vectorize X_train and X_test to 2D tensor\n",
    "tokenizer = Tokenizer(nb_words=top_words) #Considering only top 20000 words in the corpus\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.fit_on_texts(x_train)\n",
    "# tokenizer.word_index #access word-to-index dictionary of trained tokenizer\n",
    "\n",
    "\n",
    "sequences_train = tokenizer.texts_to_sequences(x_train)\n",
    "sequences_test = tokenizer.texts_to_sequences(x_test)\n",
    "\n",
    "# One-Hot Encoding of y_train and y_test\n",
    "y_train_seq = np_utils.to_categorical(y_train, nb_classes)\n",
    "y_test_seq = np_utils.to_categorical(y_test, nb_classes)\n",
    "\n",
    "X_train_seq = sequence.pad_sequences(sequences_train, maxlen=maxlen)\n",
    "X_test_seq = sequence.pad_sequences(sequences_test, maxlen=maxlen)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (148876, 100)\n",
      "X_test shape: (49626, 100)\n",
      "y_train shape: (148876,)\n",
      "y_test shape: (49626,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train shape:', X_train_seq.shape)\n",
    "print('X_test shape:', X_test_seq.shape) \n",
    "print('y_train shape:', y_train_seq.shape) \n",
    "print('y_test shape:', y_test_seq.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    0,    0, ...,  794,   43, 3437],\n",
       "       [   0,    0,    0, ...,  258,   28, 3742],\n",
       "       [   0,    0,    0, ...,  449,   73,    3],\n",
       "       ...,\n",
       "       [   0,    0,    0, ...,  319,  117,   23],\n",
       "       [   0,    0,    0, ...,  567,  235,  442],\n",
       "       [   0,    0,    0, ...,  174, 1301,  314]])"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_8 (Embedding)      (None, None, 128)         1833344   \n",
      "_________________________________________________________________\n",
      "lstm_7 (LSTM)                (None, 128)               131584    \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 3)                 387       \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 1,965,315\n",
      "Trainable params: 1,965,315\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/3\n",
      "4653/4653 [==============================] - 664s 143ms/step - loss: 0.2359 - accuracy: 0.8462\n",
      "Epoch 2/3\n",
      "4653/4653 [==============================] - 684s 147ms/step - loss: 0.1959 - accuracy: 0.8749\n",
      "Epoch 3/3\n",
      "4653/4653 [==============================] - 651s 140ms/step - loss: 0.1743 - accuracy: 0.8891\n",
      "1551/1551 [==============================] - 60s 39ms/step - loss: 0.2206 - accuracy: 0.8623\n",
      "Test loss : 0.2206\n",
      "Test accuracy : 0.8623\n"
     ]
    }
   ],
   "source": [
    "# Constructing a Simple LSTM\n",
    "model1 = Sequential()\n",
    "model1.add(Embedding(top_words, 128))\n",
    "model1.add(LSTM(128)) \n",
    "model1.add(Dropout(0.2))\n",
    "model1.add(Dense(nb_classes))\n",
    "model1.add(Activation('softmax'))\n",
    "model1.summary()\n",
    "\n",
    "# Compiling LSTM\n",
    "model1.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model1.fit(X_train_seq, y_train_seq, batch_size=batch_size, epochs=nb_epoch, verbose=1)\n",
    "\n",
    "# Model Evaluation\n",
    "score = model1.evaluate(X_test_seq, y_test_seq, batch_size=batch_size)\n",
    "print('Test loss : {:.4f}'.format(score[0]))\n",
    "print('Test accuracy : {:.4f}'.format(score[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM with Word2Vec Embedding\n",
    "\n",
    "In the simple LSTM model constructed above, the embedding class in Keras comes in handy for converting the numerical sequence of words into a word embedding but it doesn't take the semantic similarity of the words into account. The model assigns random weights to the embedding layer and learn the embeddings by minimizing the global error of the network.\n",
    "\n",
    "Instead of using random weights we will use pretrained word embeddings for initializing the weight of an embedding layer. Here, we will use the Word2Vec embedding trained in Part 4 for intializing the weights of embedding layer in LSTM.\n",
    "\n",
    "Load pretrained word embedding model.\n",
    "Construct embedding layer using embedding matrix as weights.\n",
    "Train a LSTM with Word2Vec embedding (embedding layer => LSTM layer => dense layer).\n",
    "Compile and fit the model using log loss function and ADAM optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of embedding matrix :  (14323, 100)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-221-24182a96004a>:5: DeprecationWarning: Call to deprecated `syn0` (Attribute will be removed in 4.0.0, use self.vectors instead).\n",
      "  embedding_matrix = w2v.wv.syn0  # embedding matrix, type = numpy.ndarray\n"
     ]
    }
   ],
   "source": [
    "w2v = Word2Vec.load(\"w2v_300features_10minwordcounts_10context\")\n",
    "\n",
    "\n",
    "# Getting Word2Vec embedding matrix\n",
    "embedding_matrix = w2v.wv.syn0  # embedding matrix, type = numpy.ndarray \n",
    "print(\"Shape of embedding matrix : \", embedding_matrix.shape) #(4016, 300) = (vocabulary size, embedding dimension)\n",
    "# w2v.wv.syn0[0] #feature vector of the first word in the vocabulary list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Software\\envs\\myenv\\lib\\site-packages\\keras_preprocessing\\text.py:178: UserWarning: The `nb_words` argument in `Tokenizer` has been renamed `num_words`.\n",
      "  warnings.warn('The `nb_words` argument in `Tokenizer` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (148876, 100)\n",
      "X_test shape: (49626, 100)\n",
      "y_train shape: (148876, 3)\n",
      "y_test shape: (49626, 3)\n"
     ]
    }
   ],
   "source": [
    "top_words = embedding_matrix.shape[0]\n",
    "maxlen = 100 \n",
    "batch_size = 32\n",
    "nb_classes = 3\n",
    "nb_epoch = 3\n",
    "\n",
    "\n",
    "# Vectorizing X_train and X_test to 2D tensor\n",
    "tokenizer = Tokenizer(nb_words=top_words) #Considering only top 20000 words in the corpus\n",
    "tokenizer.fit_on_texts(x_train)\n",
    "# tokenizer.word_index #access word-to-index dictionary of trained tokenizer\n",
    "\n",
    "sequences_train = tokenizer.texts_to_sequences(x_train)\n",
    "sequences_test = tokenizer.texts_to_sequences(x_test)\n",
    "\n",
    "X_train_seq = sequence.pad_sequences(sequences_train, maxlen=maxlen)\n",
    "X_test_seq = sequence.pad_sequences(sequences_test, maxlen=maxlen)\n",
    "\n",
    "\n",
    "# One-Hot Encoding of y_train and y_test\n",
    "y_train_seq = np_utils.to_categorical(y_train, nb_classes)\n",
    "y_test_seq = np_utils.to_categorical(y_test, nb_classes)\n",
    "\n",
    "print('X_train shape:', X_train_seq.shape) \n",
    "print('X_test shape:', X_test_seq.shape) \n",
    "print('y_train shape:', y_train_seq.shape)\n",
    "print('y_test shape:', y_test_seq.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_5 (Embedding)      (None, None, 100)         1432300   \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 128)               117248    \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 3)                 387       \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 1,549,935\n",
      "Trainable params: 1,549,935\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/3\n",
      "4653/4653 [==============================] - 556s 120ms/step - loss: 0.2353 - accuracy: 0.8455\n",
      "Epoch 2/3\n",
      "4653/4653 [==============================] - 560s 120ms/step - loss: 0.1933 - accuracy: 0.8766\n",
      "Epoch 3/3\n",
      "4653/4653 [==============================] - 563s 121ms/step - loss: 0.1690 - accuracy: 0.8930\n",
      "1551/1551 [==============================] - 59s 38ms/step - loss: 0.2203 - accuracy: 0.8613\n",
      "Test loss : 0.2203\n",
      "Test accuracy : 0.8613\n"
     ]
    }
   ],
   "source": [
    "# Constructing Word2Vec embedding layer\n",
    "embedding_layer = Embedding(embedding_matrix.shape[0], #4016\n",
    "                            embedding_matrix.shape[1], #300\n",
    "                            weights=[embedding_matrix])\n",
    "\n",
    "\n",
    "# Constructing LSTM with Word2Vec embedding\n",
    "model2 = Sequential()\n",
    "model2.add(embedding_layer)\n",
    "model2.add(LSTM(128)) \n",
    "model2.add(Dense(nb_classes))\n",
    "model2.add(Activation('softmax'))\n",
    "model2.summary()\n",
    "\n",
    "# Compiling model\n",
    "model2.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model2.fit(X_train_seq, y_train_seq, batch_size=batch_size, epochs=nb_epoch, verbose=1)\n",
    "\n",
    "\n",
    "# Model evaluation\n",
    "score = model2.evaluate(X_test_seq, y_test_seq, batch_size=batch_size)\n",
    "print('Test loss : {:.4f}'.format(score[0]))\n",
    "print('Test accuracy : {:.4f}'.format(score[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary:\n",
    "\n",
    "* Pipleline and GridSearch CV with Logistic Regression has the highest accuarcy of 87% and works best for classifying product   sentiments\n",
    "\n",
    "* LSTM with Word2Vec is the second best with 86% Accuracy\n",
    "\n",
    "* Take aways - Word embedding with TFIDF work best for our use case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Clustering / Topic Modeling (NMF and LDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Clustering Vectorize:\n",
    "### `CountVectorizer` & `Tf-idf`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* With NLTK stopwords "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use tf-idf features\n",
    "tfidf_vectorizer = TfidfVectorizer(stop_words=stops)\n",
    "tfidf = tfidf_vectorizer.fit_transform(review_text)\n",
    "\n",
    "# Use tf features\n",
    "tf_vectorizer = CountVectorizer(stop_words=stops)\n",
    "tf = tf_vectorizer.fit_transform(review_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of total features: 77874\n"
     ]
    }
   ],
   "source": [
    "tfidf_feature_names = tfidf_vectorizer.get_feature_names()\n",
    "print(\"Number of total features: {}\".format(len(tfidf_feature_names)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Build Clustering Models:\n",
    "## `Nonnegative Matrix Factorization (NMF)` \n",
    "### & \n",
    "## `Latent Dirichlet Allocation (Lda)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize NMF\n",
    "nmf = NMF(n_components=10, random_state=1,\n",
    "          alpha=.1, l1_ratio=.5)\n",
    "\n",
    "# Initialize Ida\n",
    "lda = LatentDirichletAllocation(n_components=10, max_iter=5,\n",
    "                                learning_method='online',\n",
    "                                learning_offset=50.,\n",
    "                                random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_top_words = 15\n",
    "\n",
    "def retrieve_top_words(model, feature_names, num_top_words):\n",
    "    for idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic #{}:\".format(idx), end='\\n')\n",
    "        print(\" \".join([feature_names[i]\n",
    "                        for i in topic.argsort()[:-num_top_words - 1:-1]]), end='\\n\\n')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Term frequency` [ tf ]\n",
    "\n",
    "## NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Software\\envs\\myenv\\lib\\site-packages\\sklearn\\decomposition\\_nmf.py:1076: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n",
      "  warnings.warn(\"Maximum number of iterations %d reached. Increase it to\"\n"
     ]
    }
   ],
   "source": [
    "nmf_tf = nmf.fit(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({8: 39131,\n",
       "         2: 18734,\n",
       "         6: 4898,\n",
       "         1: 32365,\n",
       "         3: 21487,\n",
       "         4: 23146,\n",
       "         7: 7988,\n",
       "         0: 25972,\n",
       "         5: 15660,\n",
       "         9: 9121})"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nmf_ = nmf_tf.transform(tf)\n",
    "Counter([np.argmax(i) for i in nmf_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "hair dry iron long thick used dryer conditioner color curly flat heat time blow ends\n",
      "\n",
      "Topic #1:\n",
      "skin face dry using cream sensitive moisturizer acne products oily feel also lotion used serum\n",
      "\n",
      "Topic #2:\n",
      "use face using every day wash easy also need get week time night make works\n",
      "\n",
      "Topic #3:\n",
      "product products using would used great recommend results good review bottle however say try price\n",
      "\n",
      "Topic #4:\n",
      "like really smell feel scent smells nice look good color lotion feels would little body\n",
      "\n",
      "Topic #5:\n",
      "nail color polish nails gel coat top light apply gelish colors time led uv dry\n",
      "\n",
      "Topic #6:\n",
      "oil argan bottle oils also natural ingredients used organic coconut pure water vitamin dry drops\n",
      "\n",
      "Topic #7:\n",
      "shampoo conditioner products scalp scent using hair good well clean smell shampoos used bottle great\n",
      "\n",
      "Topic #8:\n",
      "one get would really good well time used much great little even love long also\n",
      "\n",
      "Topic #9:\n",
      "brush brushes great bristles foundation love makeup soft set powder well apply quality price good\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "retrieve_top_words(nmf_tf, tfidf_feature_names, num_top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NMF / tf Topics\n",
    "\n",
    "\n",
    "\n",
    "Topic #0: **[ Hair Styling ]**\n",
    "\n",
    "hair dry iron long thick used dryer conditioner color curly flat heat time blow ends\n",
    "\n",
    "Topic #1: **[ Skin Care ]**\n",
    "\n",
    "skin face dry using cream sensitive moisturizer acne products oily feel also lotion used serum\n",
    "\n",
    "Topic #2: **[ Face wash ]**\n",
    "\n",
    "use face using every day wash easy also need get week time night make works\n",
    "\n",
    "Topic #3: **[ Positive Review ]**\n",
    "\n",
    "product products using would used great recommend results good review bottle however say try price\n",
    "\n",
    "Topic #4: **[ Scented Lotion ]**\n",
    "\n",
    "like really smell feel scent smells nice look good color lotion feels would little body\n",
    "\n",
    "Topic #5: **[ Nail Polish ]**\n",
    "\n",
    "nail color polish nails gel coat top light apply gelish colors time led uv dry\n",
    "\n",
    "Topic #6: **[Oil ]**\n",
    "\n",
    "oil argan bottle oils also natural ingredients used organic coconut pure water vitamin dry drops\n",
    "\n",
    "Topic #7: **[Shampoo ]**\n",
    "\n",
    "shampoo conditioner products scalp scent using hair good well clean smell shampoos used bottle great\n",
    "\n",
    "Topic #8: **[ Positive Review ]**\n",
    "\n",
    "one get would really good well time used much great little even love long also\n",
    "\n",
    "Topic #9: **[Brush ]**\n",
    "\n",
    "brush brushes great bristles foundation love makeup soft set powder well apply quality price good\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_tf = lda.fit(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({4: 129242,\n",
       "         0: 25639,\n",
       "         6: 19085,\n",
       "         9: 16499,\n",
       "         1: 521,\n",
       "         3: 2630,\n",
       "         5: 1265,\n",
       "         2: 3120,\n",
       "         8: 208,\n",
       "         7: 293})"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_ = lda_tf.transform(tf)\n",
    "Counter([np.argmax(i) for i in lda_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "color like colors love look really great looks dark one light would pretty nice pink\n",
      "\n",
      "Topic #1:\n",
      "nail nails polish coat polishes gelish wen manicure split balm cuticles cellulite acrylic opi purse\n",
      "\n",
      "Topic #2:\n",
      "brush mascara lashes use get mirror easy one bag razor apply remove make top eyeliner\n",
      "\n",
      "Topic #3:\n",
      "makeup eyes eye foundation love powder set bb wear look make like primer coverage circles\n",
      "\n",
      "Topic #4:\n",
      "product skin use like using face really used good one products would well dry feel\n",
      "\n",
      "Topic #5:\n",
      "oil scent vitamin argan natural ingredients smell oils bottle organic butter water body fragrance shea\n",
      "\n",
      "Topic #6:\n",
      "hair use dry conditioner product like iron long thick flat fine soft used heat get\n",
      "\n",
      "Topic #7:\n",
      "skin sensitive sunscreen 8217 sun acid spf moisturizer dead sea free ingredients peel hyaluronic aging\n",
      "\n",
      "Topic #8:\n",
      "shampoo conditioner soap wash clean shower scalp body suave shampoos axe husband men dandruff conditioners\n",
      "\n",
      "Topic #9:\n",
      "great price good brushes one quality well love would buy size really nice get recommend\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "retrieve_top_words(lda_tf, tfidf_feature_names, num_top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lda / tf Topics\n",
    " \n",
    "Topic #0: **[ Color ]**\n",
    "\n",
    "color like colors love look really great looks dark one light would pretty nice pink\n",
    "\n",
    "Topic #1: **[ Nail Polish ]**\n",
    "\n",
    "nail nails polish coat polishes gelish wen manicure split balm cuticles cellulite acrylic opi purse\n",
    "\n",
    "Topic #2: **[ Eye makeup ]**\n",
    "\n",
    "brush mascara lashes use get mirror easy one bag razor apply remove make top eyeliner\n",
    "\n",
    "Topic #3: **[ Eye makeup ]**\n",
    "\n",
    "makeup eyes eye foundation love powder set bb wear look make like primer coverage circles\n",
    "\n",
    "Topic #4: **[ Skin care ]**\n",
    "\n",
    "product skin use like using face really used good one products would well dry feel\n",
    "\n",
    "Topic #5: **[ Oil ]**\n",
    "\n",
    "oil scent vitamin argan natural ingredients smell oils bottle organic butter water body fragrance shea\n",
    "\n",
    "Topic #6: **[ Hair Styling/ Care]**\n",
    "\n",
    "hair use dry conditioner product like iron long thick flat fine soft used heat get\n",
    "\n",
    "Topic #7: **[ Skin/Sunscreen ]**\n",
    "\n",
    "skin sensitive sunscreen 8217 sun acid spf moisturizer dead sea free ingredients peel hyaluronic aging\n",
    "\n",
    "Topic #8: **[ Hair Shampoo ]**\n",
    "\n",
    "shampoo conditioner soap wash clean shower scalp body suave shampoos axe husband men dandruff conditioners\n",
    "\n",
    "Topic #9: **[ Nice Brush ]**\n",
    "\n",
    "great price good brushes one quality well love would buy size really nice get recommend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Term frequency-inverse document frequency` [ tfidf ]\n",
    "\n",
    "## NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmf_tfidf = nmf.fit(tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({5: 51017,\n",
       "         4: 11514,\n",
       "         6: 12770,\n",
       "         2: 14179,\n",
       "         0: 33256,\n",
       "         8: 12309,\n",
       "         9: 10721,\n",
       "         1: 28564,\n",
       "         7: 13721,\n",
       "         3: 10451})"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nmf_W = nmf_tfidf.transform(tfidf)\n",
    "Counter([np.argmax(i) for i in nmf_W])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "skin face dry use sensitive using cream oily moisturizer oil acne feel lotion feels feeling\n",
      "\n",
      "Topic #1:\n",
      "hair dry use oil curly thick iron fine soft dryer flat conditioner blow long leave\n",
      "\n",
      "Topic #2:\n",
      "color colors pink dark looks red nice light brown pretty beautiful lipstick lips lip shade\n",
      "\n",
      "Topic #3:\n",
      "brush brushes bristles foundation set soft makeup quality powder use blush sigma perfect good shed\n",
      "\n",
      "Topic #4:\n",
      "great price works smells buy well quality colors good easy really recommend deal looks value\n",
      "\n",
      "Topic #5:\n",
      "like one really good get use time would little well much long used smell 34\n",
      "\n",
      "Topic #6:\n",
      "product recommend would using good products used results highly see use definitely purchase anyone difference\n",
      "\n",
      "Topic #7:\n",
      "nail polish nails coat top art gel polishes base designs colors coats use opi manicure\n",
      "\n",
      "Topic #8:\n",
      "love colors makes smell absolutely soft amazing stuff perfect buy recommend smells use way one\n",
      "\n",
      "Topic #9:\n",
      "shampoo conditioner scent clean smell scalp nice smells shampoos dandruff hair like leaves soft feeling\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "retrieve_top_words(nmf_tfidf, tfidf_feature_names, num_top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NMF / tfidf Topics\n",
    "\n",
    "Topic #0: **[ Skin care ]**\n",
    "\n",
    "skin face dry use sensitive using cream oily moisturizer oil acne feel lotion feels feeling\n",
    "\n",
    "Topic #1: **[ Hair Shampoo/ Styling ]**\n",
    "\n",
    "hair dry use oil curly thick iron fine soft dryer flat conditioner blow long leave\n",
    "\n",
    "Topic #2: **[ Lipstick ]**\n",
    "\n",
    "color colors pink dark looks red nice light brown pretty beautiful lipstick lips lip shade\n",
    "\n",
    "Topic #3: **[ Brushes ]**\n",
    "\n",
    "brush brushes bristles foundation set soft makeup quality powder use blush sigma perfect good shed\n",
    "\n",
    "Topic #4: **[ Positive review ]**\n",
    "\n",
    "great price works smells buy well quality colors good easy really recommend deal looks value\n",
    "\n",
    "Topic #5: **[ ?? ]**\n",
    "\n",
    "like one really good get use time would little well much long used smell 34\n",
    "\n",
    "Topic #6: **[ Brushes ]**\n",
    "\n",
    "product recommend would using good products used results highly see use definitely purchase anyone difference\n",
    "\n",
    "Topic #7: **[ Nail ]**\n",
    "\n",
    "nail polish nails coat top art gel polishes base designs colors coats use opi manicure\n",
    "\n",
    "Topic #8: **[ Positive review ]**\n",
    "\n",
    "love colors makes smell absolutely soft amazing stuff perfect buy recommend smells use way one\n",
    "\n",
    "Topic #9: **[ Shampoo ]**\n",
    "\n",
    "shampoo conditioner scent clean smell scalp nice smells shampoos dandruff hair like leaves soft feeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_tfidf = lda.fit(tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({6: 197916,\n",
       "         0: 58,\n",
       "         9: 45,\n",
       "         7: 114,\n",
       "         8: 70,\n",
       "         3: 56,\n",
       "         2: 13,\n",
       "         5: 160,\n",
       "         4: 43,\n",
       "         1: 27})"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_W = lda_tfidf.transform(tfidf)\n",
    "Counter([np.argmax(i) for i in lda_W])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "wigs granddaughter wall temp expressed motion spiked costume 8226 com http www mechanism outfit install\n",
      "\n",
      "Topic #1:\n",
      "palette highlighter wheel clippers clipper dupe coastal evaluation decently friday realy pallette distributes bones bh\n",
      "\n",
      "Topic #2:\n",
      "bows contour receiving smokey fog adhesive adjustable pairs husbands decorate arent turquoise disposables guards josie\n",
      "\n",
      "Topic #3:\n",
      "polish nail nails polishes gelish opi stamp shellac stamper konad essie acetone top cnd chipping\n",
      "\n",
      "Topic #4:\n",
      "tape rhinestones disappoint combing palettes peace ur cheeky crystals holo weighted orchid pockets defiantly leather\n",
      "\n",
      "Topic #5:\n",
      "slides smoky austin se collections musk spouse resealable physicians fantasy texturizing intoxicating sensational de camouflage\n",
      "\n",
      "Topic #6:\n",
      "hair product skin like use great love really good one well would using color used\n",
      "\n",
      "Topic #7:\n",
      "stones flowers ingredients sodium flower caffeine extract toni oxygen ingredient blossom zinc titanium oxide infusing\n",
      "\n",
      "Topic #8:\n",
      "neat organized bling functional lotus organix accessory magnet discs cools managable wire english hrs decorations\n",
      "\n",
      "Topic #9:\n",
      "iron curling flat heat clips straightener heats curl razors curls curler rollers remington temperature hot\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "retrieve_top_words(lda_tfidf, tfidf_feature_names, num_top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lda / tfidf Topics\n",
    "\n",
    "Topic #0: **[ ?? ]**\n",
    "\n",
    "wigs granddaughter wall temp expressed motion spiked costume 8226 com http www mechanism outfit install\n",
    "\n",
    "Topic #1: **[ palette ?? ]**\n",
    "\n",
    "palette highlighter wheel clippers clipper dupe coastal evaluation decently friday realy pallette distributes bones bh\n",
    "\n",
    "Topic #2: **[ ?? ]**\n",
    "\n",
    "bows contour receiving smokey fog adhesive adjustable pairs husbands decorate arent turquoise disposables guards josie\n",
    "\n",
    "Topic #3: **[ Nail ]**\n",
    "\n",
    "polish nail nails polishes gelish opi stamp shellac stamper konad essie acetone top cnd chipping\n",
    "\n",
    "Topic #4: **[ ?? ]**\n",
    "\n",
    "tape rhinestones disappoint combing palettes peace ur cheeky crystals holo weighted orchid pockets defiantly leather\n",
    "\n",
    "Topic #5: **[ Positiove Review ]**\n",
    "\n",
    "slides smoky austin se collections musk spouse resealable physicians fantasy texturizing intoxicating sensational de camouflage\n",
    "\n",
    "Topic #6: **[ Positive review ]**\n",
    "\n",
    "hair product skin like use great love really good one well would using color used\n",
    "\n",
    "Topic #7: **[ Powder?? ]**\n",
    "\n",
    "stones flowers ingredients sodium flower caffeine extract toni oxygen ingredient blossom zinc titanium oxide infusing\n",
    "\n",
    "Topic #8: **[ Decor ]**\n",
    "\n",
    "neat organized bling functional lotus organix accessory magnet discs cools managable wire english hrs decorations\n",
    "\n",
    "Topic #9: **[ Hair Styling ]**\n",
    "\n",
    "iron curling flat heat clips straightener heats curl razors curls curler rollers remington temperature hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary:\n",
    "\n",
    "* Both **NMF with tf and tfidf** were about the same and performed quite well.\n",
    "* **Lda with tf** was the best. It had a lot of meaningful and unquie associations \n",
    "\n",
    "* **Lda with tfidf** primarily consisted of unassociated words. However - it did accuractely asscoiate a brand with product --> remington for hair styling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Application/Productization:\n",
    "* `Recommendation Engine: Collaborative filtering & Factorization Machine`\n",
    "* Scraping `Nykaa` and/or other beauty ecommerce sites in order to build a better, more robust `Recommendation Engine`  -- to reach the goal of a **Recommendation Engine Chatbot for Beauty Products** (for say Facebook or Slack)"
   ]
  }
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