Assignment 11. Text Mining - 02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 11. Text Mining - 02"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "TWO:\n",
    "1) Extract reviews of any product from ecommerce website like amazon\n",
    "2) Perform emotion mining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset: Musical_instruments_reviews.csv"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This file has reviewer ID , User ID, Reviewer Name, Reviewer text, helpful, Summary(obtained from Reviewer text),Overall Rating on a scale 5, Review time\n",
    "Description of columns in the file:\n",
    "\n",
    "reviewerID - ID of the reviewer, e.g. A2SUAM1J3GNN3B\n",
    "asin - ID of the product, e.g. 0000013714\n",
    "reviewerName - name of the reviewer\n",
    "helpful - helpfulness rating of the review, e.g. 2/3\n",
    "reviewText - text of the review\n",
    "overall - rating of the product\n",
    "summary - summary of the review\n",
    "unixReviewTime - time of the review (unix time)\n",
    "reviewTime - time of the review (raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import string\n",
    "import spacy\n",
    "from matplotlib.pyplot import imread\n",
    "from wordcloud import WordCloud\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading dataset\n",
    "review = pd.read_csv('Datasets/Musical_instruments_reviews.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing for Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10261, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>cassandra tu \"Yeah, well, that's just like, u...</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Not much to write about here, but it does exac...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>good</td>\n",
       "      <td>1393545600</td>\n",
       "      <td>02 28, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Jake</td>\n",
       "      <td>[13, 14]</td>\n",
       "      <td>The product does exactly as it should and is q...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Jake</td>\n",
       "      <td>1363392000</td>\n",
       "      <td>03 16, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Rick Bennette \"Rick Bennette\"</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>The primary job of this device is to block the...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>It Does The Job Well</td>\n",
       "      <td>1377648000</td>\n",
       "      <td>08 28, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>RustyBill \"Sunday Rocker\"</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Nice windscreen protects my MXL mic and preven...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>GOOD WINDSCREEN FOR THE MONEY</td>\n",
       "      <td>1392336000</td>\n",
       "      <td>02 14, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>SEAN MASLANKA</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>This pop filter is great. It looks and perform...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>No more pops when I record my vocals.</td>\n",
       "      <td>1392940800</td>\n",
       "      <td>02 21, 2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reviewerID        asin  \\\n",
       "0  A2IBPI20UZIR0U  1384719342   \n",
       "1  A14VAT5EAX3D9S  1384719342   \n",
       "2  A195EZSQDW3E21  1384719342   \n",
       "3  A2C00NNG1ZQQG2  1384719342   \n",
       "4   A94QU4C90B1AX  1384719342   \n",
       "\n",
       "                                       reviewerName   helpful  \\\n",
       "0  cassandra tu \"Yeah, well, that's just like, u...    [0, 0]   \n",
       "1                                              Jake  [13, 14]   \n",
       "2                     Rick Bennette \"Rick Bennette\"    [1, 1]   \n",
       "3                         RustyBill \"Sunday Rocker\"    [0, 0]   \n",
       "4                                     SEAN MASLANKA    [0, 0]   \n",
       "\n",
       "                                          reviewText  overall  \\\n",
       "0  Not much to write about here, but it does exac...      5.0   \n",
       "1  The product does exactly as it should and is q...      5.0   \n",
       "2  The primary job of this device is to block the...      5.0   \n",
       "3  Nice windscreen protects my MXL mic and preven...      5.0   \n",
       "4  This pop filter is great. It looks and perform...      5.0   \n",
       "\n",
       "                                 summary  unixReviewTime   reviewTime  \n",
       "0                                   good      1393545600  02 28, 2014  \n",
       "1                                   Jake      1363392000  03 16, 2013  \n",
       "2                   It Does The Job Well      1377648000  08 28, 2013  \n",
       "3          GOOD WINDSCREEN FOR THE MONEY      1392336000  02 14, 2014  \n",
       "4  No more pops when I record my vocals.      1392940800  02 21, 2014  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>review_name</th>\n",
       "      <th>helpful</th>\n",
       "      <th>review_text</th>\n",
       "      <th>overall</th>\n",
       "      <th>summary</th>\n",
       "      <th>unix_review_time</th>\n",
       "      <th>review_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>cassandra tu \"Yeah, well, that's just like, u...</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Not much to write about here, but it does exac...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>good</td>\n",
       "      <td>1393545600</td>\n",
       "      <td>02 28, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Jake</td>\n",
       "      <td>[13, 14]</td>\n",
       "      <td>The product does exactly as it should and is q...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Jake</td>\n",
       "      <td>1363392000</td>\n",
       "      <td>03 16, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Rick Bennette \"Rick Bennette\"</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>The primary job of this device is to block the...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>It Does The Job Well</td>\n",
       "      <td>1377648000</td>\n",
       "      <td>08 28, 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>RustyBill \"Sunday Rocker\"</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>Nice windscreen protects my MXL mic and preven...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>GOOD WINDSCREEN FOR THE MONEY</td>\n",
       "      <td>1392336000</td>\n",
       "      <td>02 14, 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>SEAN MASLANKA</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>This pop filter is great. It looks and perform...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>No more pops when I record my vocals.</td>\n",
       "      <td>1392940800</td>\n",
       "      <td>02 21, 2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        review_id        asin  \\\n",
       "0  A2IBPI20UZIR0U  1384719342   \n",
       "1  A14VAT5EAX3D9S  1384719342   \n",
       "2  A195EZSQDW3E21  1384719342   \n",
       "3  A2C00NNG1ZQQG2  1384719342   \n",
       "4   A94QU4C90B1AX  1384719342   \n",
       "\n",
       "                                        review_name   helpful  \\\n",
       "0  cassandra tu \"Yeah, well, that's just like, u...    [0, 0]   \n",
       "1                                              Jake  [13, 14]   \n",
       "2                     Rick Bennette \"Rick Bennette\"    [1, 1]   \n",
       "3                         RustyBill \"Sunday Rocker\"    [0, 0]   \n",
       "4                                     SEAN MASLANKA    [0, 0]   \n",
       "\n",
       "                                         review_text  overall  \\\n",
       "0  Not much to write about here, but it does exac...      5.0   \n",
       "1  The product does exactly as it should and is q...      5.0   \n",
       "2  The primary job of this device is to block the...      5.0   \n",
       "3  Nice windscreen protects my MXL mic and preven...      5.0   \n",
       "4  This pop filter is great. It looks and perform...      5.0   \n",
       "\n",
       "                                 summary  unix_review_time  review_time  \n",
       "0                                   good        1393545600  02 28, 2014  \n",
       "1                                   Jake        1363392000  03 16, 2013  \n",
       "2                   It Does The Job Well        1377648000  08 28, 2013  \n",
       "3          GOOD WINDSCREEN FOR THE MONEY        1392336000  02 14, 2014  \n",
       "4  No more pops when I record my vocals.        1392940800  02 21, 2014  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming columns for ease of reference\n",
    "review = review.rename({'reviewerID':'review_id', 'reviewerName':'review_name', 'reviewText':'review_text', \n",
    "               'unixReviewTime':'unix_review_time', 'reviewTime':'review_time'}, axis = 1)\n",
    "review.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_id            0\n",
       "asin                 0\n",
       "review_name         27\n",
       "helpful              0\n",
       "review_text          7\n",
       "overall              0\n",
       "summary              0\n",
       "unix_review_time     0\n",
       "review_time          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for NA values\n",
    "review.isna().sum()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1. review_name got 27 null values, but it will not add any value in our analysis as we have gor review_id.\n",
    "2. review_text is important for us. We can drop null values directly but first we will impute them as 'missing' and try to get the reason why reviews are not given. Is it because of ratings ?. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "review['review_text'] = review['review_text'].fillna('Missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>review_name</th>\n",
       "      <th>helpful</th>\n",
       "      <th>overall</th>\n",
       "      <th>unix_review_time</th>\n",
       "      <th>review_time</th>\n",
       "      <th>reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>cassandra tu \"Yeah, well, that's just like, u...</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1393545600</td>\n",
       "      <td>02 28, 2014</td>\n",
       "      <td>Not much to write about here, but it does exac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Jake</td>\n",
       "      <td>[13, 14]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1363392000</td>\n",
       "      <td>03 16, 2013</td>\n",
       "      <td>The product does exactly as it should and is q...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Rick Bennette \"Rick Bennette\"</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1377648000</td>\n",
       "      <td>08 28, 2013</td>\n",
       "      <td>The primary job of this device is to block the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>RustyBill \"Sunday Rocker\"</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1392336000</td>\n",
       "      <td>02 14, 2014</td>\n",
       "      <td>Nice windscreen protects my MXL mic and preven...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>SEAN MASLANKA</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1392940800</td>\n",
       "      <td>02 21, 2014</td>\n",
       "      <td>This pop filter is great. It looks and perform...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        review_id        asin  \\\n",
       "0  A2IBPI20UZIR0U  1384719342   \n",
       "1  A14VAT5EAX3D9S  1384719342   \n",
       "2  A195EZSQDW3E21  1384719342   \n",
       "3  A2C00NNG1ZQQG2  1384719342   \n",
       "4   A94QU4C90B1AX  1384719342   \n",
       "\n",
       "                                        review_name   helpful  overall  \\\n",
       "0  cassandra tu \"Yeah, well, that's just like, u...    [0, 0]      5.0   \n",
       "1                                              Jake  [13, 14]      5.0   \n",
       "2                     Rick Bennette \"Rick Bennette\"    [1, 1]      5.0   \n",
       "3                         RustyBill \"Sunday Rocker\"    [0, 0]      5.0   \n",
       "4                                     SEAN MASLANKA    [0, 0]      5.0   \n",
       "\n",
       "   unix_review_time  review_time  \\\n",
       "0        1393545600  02 28, 2014   \n",
       "1        1363392000  03 16, 2013   \n",
       "2        1377648000  08 28, 2013   \n",
       "3        1392336000  02 14, 2014   \n",
       "4        1392940800  02 21, 2014   \n",
       "\n",
       "                                             reviews  \n",
       "0  Not much to write about here, but it does exac...  \n",
       "1  The product does exactly as it should and is q...  \n",
       "2  The primary job of this device is to block the...  \n",
       "3  Nice windscreen protects my MXL mic and preven...  \n",
       "4  This pop filter is great. It looks and perform...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatinating review_text and summary\n",
    "review['reviews'] = review['review_text'] + review['summary']\n",
    "\n",
    "# Dropping review_text and summary\n",
    "\n",
    "review = review.drop(['review_text', 'summary'], axis = 1)\n",
    "\n",
    "review.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>review_name</th>\n",
       "      <th>helpful</th>\n",
       "      <th>overall</th>\n",
       "      <th>unix_review_time</th>\n",
       "      <th>reviews</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>cassandra tu \"Yeah, well, that's just like, u...</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1393545600</td>\n",
       "      <td>Not much to write about here, but it does exac...</td>\n",
       "      <td>02 28</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Jake</td>\n",
       "      <td>[13, 14]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1363392000</td>\n",
       "      <td>The product does exactly as it should and is q...</td>\n",
       "      <td>03 16</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Rick Bennette \"Rick Bennette\"</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1377648000</td>\n",
       "      <td>The primary job of this device is to block the...</td>\n",
       "      <td>08 28</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>RustyBill \"Sunday Rocker\"</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1392336000</td>\n",
       "      <td>Nice windscreen protects my MXL mic and preven...</td>\n",
       "      <td>02 14</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>SEAN MASLANKA</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1392940800</td>\n",
       "      <td>This pop filter is great. It looks and perform...</td>\n",
       "      <td>02 21</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        review_id        asin  \\\n",
       "0  A2IBPI20UZIR0U  1384719342   \n",
       "1  A14VAT5EAX3D9S  1384719342   \n",
       "2  A195EZSQDW3E21  1384719342   \n",
       "3  A2C00NNG1ZQQG2  1384719342   \n",
       "4   A94QU4C90B1AX  1384719342   \n",
       "\n",
       "                                        review_name   helpful  overall  \\\n",
       "0  cassandra tu \"Yeah, well, that's just like, u...    [0, 0]      5.0   \n",
       "1                                              Jake  [13, 14]      5.0   \n",
       "2                     Rick Bennette \"Rick Bennette\"    [1, 1]      5.0   \n",
       "3                         RustyBill \"Sunday Rocker\"    [0, 0]      5.0   \n",
       "4                                     SEAN MASLANKA    [0, 0]      5.0   \n",
       "\n",
       "   unix_review_time                                            reviews   date  \\\n",
       "0        1393545600  Not much to write about here, but it does exac...  02 28   \n",
       "1        1363392000  The product does exactly as it should and is q...  03 16   \n",
       "2        1377648000  The primary job of this device is to block the...  08 28   \n",
       "3        1392336000  Nice windscreen protects my MXL mic and preven...  02 14   \n",
       "4        1392940800  This pop filter is great. It looks and perform...  02 21   \n",
       "\n",
       "    year  \n",
       "0   2014  \n",
       "1   2013  \n",
       "2   2013  \n",
       "3   2014  \n",
       "4   2014  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling review_time column\n",
    "'''Here we have an unusual review time column which has date and year, once we split both we will split the date \n",
    "further into month and date '''\n",
    "\n",
    "# new data frame which has date and year\n",
    "new = review[\"review_time\"].str.split(\",\", n = 1, expand = True) \n",
    "  \n",
    "# making separate date column from new data frame \n",
    "review[\"date\"]= new[0] \n",
    "  \n",
    "# making separate year column from new data frame \n",
    "review[\"year\"]= new[1] \n",
    "\n",
    "review=review.drop(['review_time'], axis=1)\n",
    "review.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>review_name</th>\n",
       "      <th>helpful</th>\n",
       "      <th>overall</th>\n",
       "      <th>unix_review_time</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>cassandra tu \"Yeah, well, that's just like, u...</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1393545600</td>\n",
       "      <td>Not much to write about here, but it does exac...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Jake</td>\n",
       "      <td>[13, 14]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1363392000</td>\n",
       "      <td>The product does exactly as it should and is q...</td>\n",
       "      <td>2013</td>\n",
       "      <td>03</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>Rick Bennette \"Rick Bennette\"</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1377648000</td>\n",
       "      <td>The primary job of this device is to block the...</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>RustyBill \"Sunday Rocker\"</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1392336000</td>\n",
       "      <td>Nice windscreen protects my MXL mic and preven...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>SEAN MASLANKA</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1392940800</td>\n",
       "      <td>This pop filter is great. It looks and perform...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        review_id        asin  \\\n",
       "0  A2IBPI20UZIR0U  1384719342   \n",
       "1  A14VAT5EAX3D9S  1384719342   \n",
       "2  A195EZSQDW3E21  1384719342   \n",
       "3  A2C00NNG1ZQQG2  1384719342   \n",
       "4   A94QU4C90B1AX  1384719342   \n",
       "\n",
       "                                        review_name   helpful  overall  \\\n",
       "0  cassandra tu \"Yeah, well, that's just like, u...    [0, 0]      5.0   \n",
       "1                                              Jake  [13, 14]      5.0   \n",
       "2                     Rick Bennette \"Rick Bennette\"    [1, 1]      5.0   \n",
       "3                         RustyBill \"Sunday Rocker\"    [0, 0]      5.0   \n",
       "4                                     SEAN MASLANKA    [0, 0]      5.0   \n",
       "\n",
       "   unix_review_time                                            reviews   year  \\\n",
       "0        1393545600  Not much to write about here, but it does exac...   2014   \n",
       "1        1363392000  The product does exactly as it should and is q...   2013   \n",
       "2        1377648000  The primary job of this device is to block the...   2013   \n",
       "3        1392336000  Nice windscreen protects my MXL mic and preven...   2014   \n",
       "4        1392940800  This pop filter is great. It looks and perform...   2014   \n",
       "\n",
       "  month day  \n",
       "0    02  28  \n",
       "1    03  16  \n",
       "2    08  28  \n",
       "3    02  14  \n",
       "4    02  21  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the date \n",
    "new1 = review[\"date\"].str.split(\" \", n = 1, expand = True) \n",
    "  \n",
    "# adding month to the main dataset \n",
    "review[\"month\"]= new1[0] \n",
    "  \n",
    "# adding day to the main dataset \n",
    "review[\"day\"]= new1[1] \n",
    "\n",
    "review=review.drop(['date'], axis=1)\n",
    "review.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Finding the helpfulness of the review\n",
    "From the main dataframe we can see the helpful feature with values in list [a,b] format. It says that a out of b people found that review helpful. But with that format, it could not add value to the machine learning model and it will be difficult to decrypt the meaning for the machine.Create helpful_rate feature which returns a/b value from [a,b]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset based on comma and square bracket \n",
    "new1 = review[\"helpful\"].str.split(\",\", n = 1, expand = True)\n",
    "new2 = new1[0].str.split(\"[\", n = 1, expand = True)\n",
    "new3 = new1[1].str.split(\"]\", n = 1, expand = True)\n",
    "\n",
    "#Resetting the index\n",
    "new2.reset_index(drop=True, inplace=True)\n",
    "new3.reset_index(drop=True, inplace=True)\n",
    "\n",
    "#Dropping empty columns due to splitting \n",
    "new2=new2.drop([0], axis=1)\n",
    "new3=new3.drop([1], axis=1)\n",
    "\n",
    "#Concatenating the splitted columns\n",
    "helpful=pd.concat([new2, new3], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10256</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10257</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10258</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10259</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10260</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10261 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        1    0\n",
       "0       0    0\n",
       "1      13   14\n",
       "2       1    1\n",
       "3       0    0\n",
       "4       0    0\n",
       "...    ..  ...\n",
       "10256   0    0\n",
       "10257   0    0\n",
       "10258   0    0\n",
       "10259   0    0\n",
       "10260   0    0\n",
       "\n",
       "[10261 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helpful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I found few spaces in new3, so it is better to strip all the values to find the rate\n",
    "def trim_all_columns(df):\n",
    "    \"\"\"\n",
    "    Trim whitespace from ends of each value across all series in dataframe\n",
    "    \"\"\"\n",
    "    trim_strings = lambda x: x.strip() if isinstance(x, str) else x\n",
    "    return df.applymap(trim_strings)\n",
    "\n",
    "#Applying the function\n",
    "helpful= trim_all_columns(helpful)\n",
    "\n",
    "#Converting into integer types\n",
    "helpful[0]=helpful[0].astype(str).astype(int)\n",
    "helpful[1]=helpful[1].astype(str).astype(int)\n",
    "\n",
    "'''Dividing the two columns, we have 0 in the second columns when dvided gives error, so I'm ignoring those \n",
    "#errors'''\n",
    "try:\n",
    "  helpful['result'] = helpful[1]/helpful[0]\n",
    "except ZeroDivisionError:\n",
    "  helpful['result']=0\n",
    "\n",
    "#Filling the NaN values(created due to dividing) with 0\n",
    "helpful['result'] = helpful['result'].fillna(0)\n",
    "\n",
    "#Rounding of the results to two decimal places\n",
    "helpful['result']=helpful['result'].round(2) \n",
    "\n",
    "#Attaching the results to a new column of the main dataframe\n",
    "review['helpful_rate']=helpful['result']\n",
    "\n",
    "#dropping the helpful column from main dataframe\n",
    "review=review.drop(['helpful'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00    7215\n",
       "1.00    2040\n",
       "0.50     266\n",
       "0.67     136\n",
       "0.75     111\n",
       "        ... \n",
       "0.30       1\n",
       "0.47       1\n",
       "0.69       1\n",
       "0.10       1\n",
       "0.43       1\n",
       "Name: helpful_rate, Length: 65, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review['helpful_rate'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### removing the punctuations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing unnecessary columns\n",
    "review=review.drop(['review_name','unix_review_time'], axis=1)\n",
    "#Creating a copy \n",
    "clean_reviews=review.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def review_cleaning(text):\n",
    "    '''Make text lowercase, remove text in square brackets,remove links,remove punctuation\n",
    "    and remove words containing numbers.'''\n",
    "    text = str(text).lower()\n",
    "    text = re.sub('\\[.*?\\]', '', text)\n",
    "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
    "    text = re.sub('<.*?>+', '', text)\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
    "    text = re.sub('\\n', '', text)\n",
    "    text = re.sub('\\w*\\d\\w*', '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "review['reviews'] = review['reviews'].apply(lambda x: review_cleaning(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>not much to write about here but it does exact...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>28</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>the product does exactly as it should and is q...</td>\n",
       "      <td>2013</td>\n",
       "      <td>03</td>\n",
       "      <td>16</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>the primary job of this device is to block the...</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "      <td>28</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>nice windscreen protects my mxl mic and preven...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>this pop filter is great it looks and performs...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>21</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        review_id        asin  overall  \\\n",
       "0  A2IBPI20UZIR0U  1384719342      5.0   \n",
       "1  A14VAT5EAX3D9S  1384719342      5.0   \n",
       "2  A195EZSQDW3E21  1384719342      5.0   \n",
       "3  A2C00NNG1ZQQG2  1384719342      5.0   \n",
       "4   A94QU4C90B1AX  1384719342      5.0   \n",
       "\n",
       "                                             reviews   year month day  \\\n",
       "0  not much to write about here but it does exact...   2014    02  28   \n",
       "1  the product does exactly as it should and is q...   2013    03  16   \n",
       "2  the primary job of this device is to block the...   2013    08  28   \n",
       "3  nice windscreen protects my mxl mic and preven...   2014    02  14   \n",
       "4  this pop filter is great it looks and performs...   2014    02  21   \n",
       "\n",
       "   helpful_rate  \n",
       "0          0.00  \n",
       "1          0.93  \n",
       "2          1.00  \n",
       "3          0.00  \n",
       "4          0.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Review text-Stop words\n",
    "Coming to stop words, general nltk stop words contains words like not,hasn't,would'nt which actually conveys a negative sentiment. If we remove that it will end up contradicting the target variable(sentiment). So I have curated the stop words which doesn't have any negative sentiment or any negative alternatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words= ['yourselves', 'between', 'whom', 'itself', 'is', \"she's\", 'up', 'herself', 'here', 'your', 'each', \n",
    "             'we', 'he', 'my', \"you've\", 'having', 'in', 'both', 'for', 'themselves', 'are', 'them', 'other',\n",
    "             'and', 'an', 'during', 'their', 'can', 'yourself', 'she', 'until', 'so', 'these', 'ours', 'above', \n",
    "             'what', 'while', 'have', 're', 'more', 'only', \"needn't\", 'when', 'just', 'that', 'were', \"don't\", \n",
    "             'very', 'should', 'any', 'y', 'isn', 'who',  'a', 'they', 'to', 'too', \"should've\", 'has', 'before',\n",
    "             'into', 'yours', \"it's\", 'do', 'against', 'on',  'now', 'her', 've', 'd', 'by', 'am', 'from', \n",
    "             'about', 'further', \"that'll\", \"you'd\", 'you', 'as', 'how', 'been', 'the', 'or', 'doing', 'such',\n",
    "             'his', 'himself', 'ourselves',  'was', 'through', 'out', 'below', 'own', 'myself', 'theirs', \n",
    "             'me', 'why', 'once',  'him', 'than', 'be', 'most', \"you'll\", 'same', 'some', 'with', 'few', 'it',\n",
    "             'at', 'after', 'its', 'which', 'there','our', 'this', 'hers', 'being', 'did', 'of', 'had', 'under',\n",
    "             'over','again', 'where', 'those', 'then', \"you're\", 'i', 'because', 'does', 'all']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>not much write but exactly supposed filters po...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>28</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>product exactly quite affordablei not realized...</td>\n",
       "      <td>2013</td>\n",
       "      <td>03</td>\n",
       "      <td>16</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>primary job device block breath would otherwis...</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "      <td>28</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>nice windscreen protects mxl mic prevents pops...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>14</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pop filter great looks performs like studio fi...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>21</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        review_id        asin  overall  \\\n",
       "0  A2IBPI20UZIR0U  1384719342      5.0   \n",
       "1  A14VAT5EAX3D9S  1384719342      5.0   \n",
       "2  A195EZSQDW3E21  1384719342      5.0   \n",
       "3  A2C00NNG1ZQQG2  1384719342      5.0   \n",
       "4   A94QU4C90B1AX  1384719342      5.0   \n",
       "\n",
       "                                             reviews   year month day  \\\n",
       "0  not much write but exactly supposed filters po...   2014    02  28   \n",
       "1  product exactly quite affordablei not realized...   2013    03  16   \n",
       "2  primary job device block breath would otherwis...   2013    08  28   \n",
       "3  nice windscreen protects mxl mic prevents pops...   2014    02  14   \n",
       "4  pop filter great looks performs like studio fi...   2014    02  21   \n",
       "\n",
       "   helpful_rate  \n",
       "0          0.00  \n",
       "1          0.93  \n",
       "2          1.00  \n",
       "3          0.00  \n",
       "4          0.00  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review['reviews'] = review['reviews'].apply(lambda x: ' '.join(\n",
    "    [word for word in x.split() if word not in (stop_words)]))\n",
    "review.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating sentiment value for reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2477, 2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afinn = pd.read_csv('Datasets/Afinn.csv', sep=',', encoding='latin-1')\n",
    "afinn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>word</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abandon</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abandoned</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abandons</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abducted</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abduction</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        word  value\n",
       "0    abandon     -2\n",
       "1  abandoned     -2\n",
       "2   abandons     -2\n",
       "3   abducted     -2\n",
       "4  abduction     -2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "afinn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "affinity_scores = afinn.set_index('word')['value'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Custom function :score each word in a sentence in lemmatised form, \n",
    "#but calculate the score for the whole original sentence.\n",
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "sentiment_lexicon = affinity_scores\n",
    "\n",
    "def calculate_sentiment(text: str = None):\n",
    "    sent_score = 0\n",
    "    if text:\n",
    "        sentence = nlp(text)\n",
    "        for word in sentence:\n",
    "            sent_score += sentiment_lexicon.get(word.lemma_, 0)\n",
    "    return sent_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test that it works\n",
    "calculate_sentiment(text = 'nice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "review['sentiment_value'] = review['reviews'].apply(calculate_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>sentiment_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>not much write but exactly supposed filters po...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>product exactly quite affordablei not realized...</td>\n",
       "      <td>2013</td>\n",
       "      <td>03</td>\n",
       "      <td>16</td>\n",
       "      <td>0.93</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>primary job device block breath would otherwis...</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "      <td>28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>nice windscreen protects mxl mic prevents pops...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pop filter great looks performs like studio fi...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10256</th>\n",
       "      <td>A14B2YH83ZXMPP</td>\n",
       "      <td>B00JBIVXGC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>great expected thank allfive stars</td>\n",
       "      <td>2014</td>\n",
       "      <td>07</td>\n",
       "      <td>20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10257</th>\n",
       "      <td>A1RPTVW5VEOSI</td>\n",
       "      <td>B00JBIVXGC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ive thinking trying nanoweb strings but bit pu...</td>\n",
       "      <td>2014</td>\n",
       "      <td>07</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10258</th>\n",
       "      <td>AWCJ12KBO5VII</td>\n",
       "      <td>B00JBIVXGC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>tried coated strings past including elixirs ne...</td>\n",
       "      <td>2014</td>\n",
       "      <td>07</td>\n",
       "      <td>22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10259</th>\n",
       "      <td>A2Z7S8B5U4PAKJ</td>\n",
       "      <td>B00JBIVXGC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>well made elixir developed taylor guitars stri...</td>\n",
       "      <td>2014</td>\n",
       "      <td>07</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10260</th>\n",
       "      <td>A2WA8TDCTGUADI</td>\n",
       "      <td>B00JBIVXGC</td>\n",
       "      <td>4.0</td>\n",
       "      <td>strings really quite good but wouldnt call per...</td>\n",
       "      <td>2014</td>\n",
       "      <td>07</td>\n",
       "      <td>16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10261 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            review_id        asin  overall  \\\n",
       "0      A2IBPI20UZIR0U  1384719342      5.0   \n",
       "1      A14VAT5EAX3D9S  1384719342      5.0   \n",
       "2      A195EZSQDW3E21  1384719342      5.0   \n",
       "3      A2C00NNG1ZQQG2  1384719342      5.0   \n",
       "4       A94QU4C90B1AX  1384719342      5.0   \n",
       "...               ...         ...      ...   \n",
       "10256  A14B2YH83ZXMPP  B00JBIVXGC      5.0   \n",
       "10257   A1RPTVW5VEOSI  B00JBIVXGC      5.0   \n",
       "10258   AWCJ12KBO5VII  B00JBIVXGC      4.0   \n",
       "10259  A2Z7S8B5U4PAKJ  B00JBIVXGC      4.0   \n",
       "10260  A2WA8TDCTGUADI  B00JBIVXGC      4.0   \n",
       "\n",
       "                                                 reviews   year month day  \\\n",
       "0      not much write but exactly supposed filters po...   2014    02  28   \n",
       "1      product exactly quite affordablei not realized...   2013    03  16   \n",
       "2      primary job device block breath would otherwis...   2013    08  28   \n",
       "3      nice windscreen protects mxl mic prevents pops...   2014    02  14   \n",
       "4      pop filter great looks performs like studio fi...   2014    02  21   \n",
       "...                                                  ...    ...   ...  ..   \n",
       "10256                 great expected thank allfive stars   2014    07  20   \n",
       "10257  ive thinking trying nanoweb strings but bit pu...   2014    07   2   \n",
       "10258  tried coated strings past including elixirs ne...   2014    07  22   \n",
       "10259  well made elixir developed taylor guitars stri...   2014    07   1   \n",
       "10260  strings really quite good but wouldnt call per...   2014    07  16   \n",
       "\n",
       "       helpful_rate  sentiment_value  \n",
       "0              0.00                0  \n",
       "1              0.93                2  \n",
       "2              1.00               -1  \n",
       "3              0.00                4  \n",
       "4              0.00                5  \n",
       "...             ...              ...  \n",
       "10256          0.00                5  \n",
       "10257          0.00               26  \n",
       "10258          0.00               11  \n",
       "10259          0.00               16  \n",
       "10260          0.00               21  \n",
       "\n",
       "[10261 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         25\n",
       "1         55\n",
       "2         48\n",
       "3         22\n",
       "4         21\n",
       "        ... \n",
       "10256      5\n",
       "10257    291\n",
       "10258     58\n",
       "10259    153\n",
       "10260     61\n",
       "Name: word_count, Length: 10261, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many words are in the sentence?\n",
    "review['word_count'] = review['reviews'].str.split().apply(len)\n",
    "review['word_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>sentiment_value</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6758</th>\n",
       "      <td>A3FQ9WYUHG9CD</td>\n",
       "      <td>B001OLZYUU</td>\n",
       "      <td>4.0</td>\n",
       "      <td>fender treated polish cloth soft ecru colored ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-35</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5678</th>\n",
       "      <td>A34WEXT7SIRFE4</td>\n",
       "      <td>B000ULAP4U</td>\n",
       "      <td>2.0</td>\n",
       "      <td>cans decent back bucks theyve inflated thats r...</td>\n",
       "      <td>2012</td>\n",
       "      <td>02</td>\n",
       "      <td>3</td>\n",
       "      <td>0.64</td>\n",
       "      <td>-30</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4112</th>\n",
       "      <td>A3M9FH2OZKYF4D</td>\n",
       "      <td>B000A2HOB6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>crikey begin firstly hated fact package two st...</td>\n",
       "      <td>2013</td>\n",
       "      <td>06</td>\n",
       "      <td>23</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-27</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8812</th>\n",
       "      <td>A165EMA3YTIM6A</td>\n",
       "      <td>B004UJO41S</td>\n",
       "      <td>1.0</td>\n",
       "      <td>downgrade star returning back brookmays seller...</td>\n",
       "      <td>2012</td>\n",
       "      <td>01</td>\n",
       "      <td>3</td>\n",
       "      <td>0.09</td>\n",
       "      <td>-20</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9364</th>\n",
       "      <td>A3D1A0YHTT0E9G</td>\n",
       "      <td>B005OZE9SA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>updated owning year encountering many difficul...</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-16</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8500</th>\n",
       "      <td>ADH0O8UVJOT10</td>\n",
       "      <td>B0043RZ9QQ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>unfamiliar voxs amplug products things amazing...</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>1.00</td>\n",
       "      <td>78</td>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6576</th>\n",
       "      <td>A20JJ8634DG3FS</td>\n",
       "      <td>B001I8ITQA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>comparisons compared side side following pedal...</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "      <td>21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5973</th>\n",
       "      <td>A1LCG14GZJI5LO</td>\n",
       "      <td>B000Y7Q2C4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>love capo let tell capo journey ive used every...</td>\n",
       "      <td>2014</td>\n",
       "      <td>05</td>\n",
       "      <td>30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>105</td>\n",
       "      <td>637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8636</th>\n",
       "      <td>A35YS5ND3K5FN</td>\n",
       "      <td>B004ISK48S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>constructionthis amp certainly looks sharp not...</td>\n",
       "      <td>2011</td>\n",
       "      <td>02</td>\n",
       "      <td>10</td>\n",
       "      <td>0.99</td>\n",
       "      <td>130</td>\n",
       "      <td>719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>A27L5L6I7OSV5B</td>\n",
       "      <td>B0002CZV82</td>\n",
       "      <td>2.0</td>\n",
       "      <td>greg abrams review entitled but tone snobs rev...</td>\n",
       "      <td>2011</td>\n",
       "      <td>06</td>\n",
       "      <td>30</td>\n",
       "      <td>0.95</td>\n",
       "      <td>139</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10261 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           review_id        asin  overall  \\\n",
       "6758   A3FQ9WYUHG9CD  B001OLZYUU      4.0   \n",
       "5678  A34WEXT7SIRFE4  B000ULAP4U      2.0   \n",
       "4112  A3M9FH2OZKYF4D  B000A2HOB6      1.0   \n",
       "8812  A165EMA3YTIM6A  B004UJO41S      1.0   \n",
       "9364  A3D1A0YHTT0E9G  B005OZE9SA      1.0   \n",
       "...              ...         ...      ...   \n",
       "8500   ADH0O8UVJOT10  B0043RZ9QQ      5.0   \n",
       "6576  A20JJ8634DG3FS  B001I8ITQA      5.0   \n",
       "5973  A1LCG14GZJI5LO  B000Y7Q2C4      5.0   \n",
       "8636   A35YS5ND3K5FN  B004ISK48S      5.0   \n",
       "412   A27L5L6I7OSV5B  B0002CZV82      2.0   \n",
       "\n",
       "                                                reviews   year month day  \\\n",
       "6758  fender treated polish cloth soft ecru colored ...   2013    12   4   \n",
       "5678  cans decent back bucks theyve inflated thats r...   2012    02   3   \n",
       "4112  crikey begin firstly hated fact package two st...   2013    06  23   \n",
       "8812  downgrade star returning back brookmays seller...   2012    01   3   \n",
       "9364  updated owning year encountering many difficul...   2012    10  29   \n",
       "...                                                 ...    ...   ...  ..   \n",
       "8500  unfamiliar voxs amplug products things amazing...   2012    10  24   \n",
       "6576  comparisons compared side side following pedal...   2013    08  21   \n",
       "5973  love capo let tell capo journey ive used every...   2014    05  30   \n",
       "8636  constructionthis amp certainly looks sharp not...   2011    02  10   \n",
       "412   greg abrams review entitled but tone snobs rev...   2011    06  30   \n",
       "\n",
       "      helpful_rate  sentiment_value  word_count  \n",
       "6758          0.00              -35         349  \n",
       "5678          0.64              -30         105  \n",
       "4112          0.50              -27          67  \n",
       "8812          0.09              -20         410  \n",
       "9364          0.90              -16         268  \n",
       "...            ...              ...         ...  \n",
       "8500          1.00               78         501  \n",
       "6576          1.00               98        1129  \n",
       "5973          1.00              105         637  \n",
       "8636          0.99              130         719  \n",
       "412           0.95              139         713  \n",
       "\n",
       "[10261 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review.sort_values(by='sentiment_value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximum sentiment value\n",
    "review['sentiment_value'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>sentiment_value</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>A27L5L6I7OSV5B</td>\n",
       "      <td>B0002CZV82</td>\n",
       "      <td>2.0</td>\n",
       "      <td>greg abrams review entitled but tone snobs rev...</td>\n",
       "      <td>2011</td>\n",
       "      <td>06</td>\n",
       "      <td>30</td>\n",
       "      <td>0.95</td>\n",
       "      <td>139</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          review_id        asin  overall  \\\n",
       "412  A27L5L6I7OSV5B  B0002CZV82      2.0   \n",
       "\n",
       "                                               reviews   year month day  \\\n",
       "412  greg abrams review entitled but tone snobs rev...   2011    06  30   \n",
       "\n",
       "     helpful_rate  sentiment_value  word_count  \n",
       "412          0.95              139         713  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review which is having max sentiment value\n",
    "review[review['sentiment_value']==139]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "For above review, we can see that it's helpful_rate is also high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'greg abrams review entitled but tone snobs review down products pagealright want get good if somewhat basic distortion sound less deali strongly disagree find much better distortions less even near mark currently priced ative played guitar years like many people one first pedal purchases afterall everyone talks theyre available given music store nation hear famous musicians using plus actually pretty cheap snapped one like everyone else come find something popular doesnt always mean good see nickelback transformers movie franchise kardashian family etcso plugged didnt really care distortion harsh thin artificial sounding always noisy tried amps practic amp solid state fender frontman big amp tube halfstack not diverse basic sound not likeable ive read japanese made better sound newer made taiwan models but either way not great pedal mindbut like many newer players didnt know better time fast forward years ive bought sold literally dozens pedals whole world great tones found if keep eyes openso pedals currently like much better great general purpose distortions easy enough find new used guitar shops time writing bought brand new less good ol amazoncom rugged enough last yearsboss turbo distortion lest catch flames people thinking im boss hater im certainly not loved among others ill start big brother pedal much nicer tone not mention distinct distortion modes setting pretty much higher quality ii setting brighter richer tone completely overshadows popularity find ebay cheap prices worthy pedal unfortunately overlooked manypro co great dirty distortion pedal probably personal favorite youve heard box used literally hundreds if not thousands songs last years used ridiculous amount guitarists rich full distortion higher gains gets slightly fuzzy ive never heard bad tone mine great getting big amp kind sound small practice amp also great pushing big amp much headroom overdriven sweet spot without crank amp volume truly excellent pedalehx little big muff big muff another pedal youve heard hundreds songs kind fuzz kind distortion but definitely owns sonic territory big saturated warm used heavily classic rock tones also beyond tons grunge garage used everyone carlos santana david gilmour smashing pumpkins dinosaur jr jack white personally reccomend little big muff not touch creamier tone recent nyc reissue smaller uses regular adapter cheaper great pedalmxr distortion distortion great first distortion usually found pretty cheap whats nice distortion sounds great anything lack tone knob helps keep somewhat transparent ie easily hear natural tone guitar amp doesnt cover equipments sound used quite famous folks including greatful deads jerry garcia randy rhoads ozzys famous guitarist steve jones sex pistolsdanelectro cool cat metal make sure get cool cat metal not metal ii thats fine pedal well but definitely traditional metal pedal cool cat metal actually different pedal metal pedals basically preamp sits floor fully adjustable eq tons volume gain roof anything clean boost gain minimum virtually no coloration tone high gain metal stuff plenty great overdrive rock tones works well amp ive tried find tons great sounds unbelievably versatile pedal even true bypass best part danelectro seems made way many easily find one used even new true hidden gem thats actually cheaper crunch distortion little guy surprised supposedly clone mi audio crunch box if must one awesome pedal essentially marshall stack box boy ever succeed great crunch tons volume gain really make practice amp sound like big ragin stack even sounded good going modeling amp pedals work well modeling amp one even sounded better marshall simualtion built get brand new one shipped fantasticspeaking joyo ive pedals well one two moen another chinese pedal maker im honestly floored nice sound given shockingly low price tag especially themoen mofm fuzz moowhich honestly big muffs every big muff ive ever played joyo moen make variety great sounding fuzz od dirt pedals check anyone gets arms supporting chinese company please keep mind none pedals listed review included made america let alone western hemispherehopefully suggestions give something look also if cant actually try pedal store youtube great place hear demos different pedals doi also mention if try love suits needs power tone subjective every pedal sounds different running different guitars amps may share different opinions something love thats okay main reason posting lengthy review give newer players information plenty options distortion pedals not thing thats affordable find something truly love buy if one pedals ive mentioned anything else important part sounds right youwish knew know'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Full review at index 412\n",
    "review['reviews'][412]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-35"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# minimum sentiment value\n",
    "review['sentiment_value'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>sentiment_value</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6758</th>\n",
       "      <td>A3FQ9WYUHG9CD</td>\n",
       "      <td>B001OLZYUU</td>\n",
       "      <td>4.0</td>\n",
       "      <td>fender treated polish cloth soft ecru colored ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-35</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          review_id        asin  overall  \\\n",
       "6758  A3FQ9WYUHG9CD  B001OLZYUU      4.0   \n",
       "\n",
       "                                                reviews   year month day  \\\n",
       "6758  fender treated polish cloth soft ecru colored ...   2013    12   4   \n",
       "\n",
       "      helpful_rate  sentiment_value  word_count  \n",
       "6758           0.0              -35         349  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# review which is having min sentiment value\n",
    "review[review['sentiment_value']==-35]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fender treated polish cloth soft ecru colored cloth cut scalloped edge around asimple product classic fender logo left upper quadrant cloth resealableplastic bag cloth came label read states size inchesby incheswell size measured actually inches inches wanted mention itbecause seen people get mighty upset things like no problem inch minor detail go lumber yard time look wontactually find actual theyve shrinking yearsi not sure cloth treated but not feel different untreatedgibson polishing cloths purchased also gave nose test thought might hintof whiff polish something nope no scent no whiffwhat find disconcerting warning product details section amazon web page isthe fourth bullet down california residents click proposition warning thewarning verbatim amazon kudos amazon posting warning californias proposition entitles california consumers special warnings products contain chemicals known state california cause cancer birth defects reproductive harm if products expose consumers chemicals certain threshold levels care customers safety hope information helps buying decisions providing following warning products linked page warning product contains chemicals known state california cause cancer birth defects reproductive harm thought let know one guess missing inch really no big deal atthis point need worry concerns keeping beautiful guitar sparkling cleanyou could get cancer wow back reviewthe fender cloth soft little bit bigger gibson cloths but not much differencebetween two well gibson cloth whipstitch edging around notmuch differencewhen use fender cloth fender guitar no marks left no smudges left like tokeep neck especially clean polished regular basis seemed good job idont use body much not exposed dirty oily hands much theneckso based fact fender made cloth not scratch smudge guitar will give thisamazingly boring uninteresting ecru colored cloth stars appears supposed doand polish guitar without harming itit gets one less star california prop warning suppose need take somewhatseriously docked one star will definitely washing hand right using itso far no harm done guitars writing review future not sureabout thank say goodnight gracienote vendor godps responded admirably cloth not show time dayspast delivery date emailed responded quickly put another cloth mail tome came appropriate amount time inexpensive item went distance incustomer service will use x warning fender treated polish cloth photos lt'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Full review at index 6758\n",
    "review['reviews'][6758]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10261.000000\n",
       "mean         6.931293\n",
       "std          8.031435\n",
       "min        -35.000000\n",
       "25%          2.000000\n",
       "50%          6.000000\n",
       "75%         10.000000\n",
       "max        139.000000\n",
       "Name: sentiment_value, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentiment score of the whole review\n",
    "review['sentiment_value'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>sentiment_value</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>not much write but exactly supposed filters po...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>primary job device block breath would otherwis...</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AJNFQI3YR6XJ5</td>\n",
       "      <td>B00004Y2UT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>use cable run output pedal chain input fender ...</td>\n",
       "      <td>2012</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A2NIT6BKW11XJQ</td>\n",
       "      <td>B00005ML71</td>\n",
       "      <td>3.0</td>\n",
       "      <td>if not use using large sustaining pedal playin...</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>A2CCGGDGZ694CT</td>\n",
       "      <td>B000068NVI</td>\n",
       "      <td>4.0</td>\n",
       "      <td>hosa xlr cables affordable heavily madei large...</td>\n",
       "      <td>2012</td>\n",
       "      <td>07</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10178</th>\n",
       "      <td>A768OB1LNE356</td>\n",
       "      <td>B00FXKIG5I</td>\n",
       "      <td>2.0</td>\n",
       "      <td>larger ones sometimes neck snap thought might ...</td>\n",
       "      <td>2014</td>\n",
       "      <td>03</td>\n",
       "      <td>22</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-5</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10181</th>\n",
       "      <td>AYTKUTAP0VA53</td>\n",
       "      <td>B00FXKIG5I</td>\n",
       "      <td>5.0</td>\n",
       "      <td>thing tiny not breakable swivel head original ...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10188</th>\n",
       "      <td>A3M1PLEYNDEYO8</td>\n",
       "      <td>B00GTSM8FW</td>\n",
       "      <td>5.0</td>\n",
       "      <td>several capos but first adjustable tension sev...</td>\n",
       "      <td>2014</td>\n",
       "      <td>04</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10222</th>\n",
       "      <td>A2FY8306QHI5LE</td>\n",
       "      <td>B00IZCSW3M</td>\n",
       "      <td>4.0</td>\n",
       "      <td>twice price standard strings daddario nyxl str...</td>\n",
       "      <td>2014</td>\n",
       "      <td>06</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239</th>\n",
       "      <td>A2PD27UKAD3Q00</td>\n",
       "      <td>B00IZCSW3M</td>\n",
       "      <td>5.0</td>\n",
       "      <td>daddarios nickel plated electric guitar string...</td>\n",
       "      <td>2014</td>\n",
       "      <td>06</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1505 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            review_id        asin  overall  \\\n",
       "0      A2IBPI20UZIR0U  1384719342      5.0   \n",
       "2      A195EZSQDW3E21  1384719342      5.0   \n",
       "7       AJNFQI3YR6XJ5  B00004Y2UT      3.0   \n",
       "12     A2NIT6BKW11XJQ  B00005ML71      3.0   \n",
       "28     A2CCGGDGZ694CT  B000068NVI      4.0   \n",
       "...               ...         ...      ...   \n",
       "10178   A768OB1LNE356  B00FXKIG5I      2.0   \n",
       "10181   AYTKUTAP0VA53  B00FXKIG5I      5.0   \n",
       "10188  A3M1PLEYNDEYO8  B00GTSM8FW      5.0   \n",
       "10222  A2FY8306QHI5LE  B00IZCSW3M      4.0   \n",
       "10239  A2PD27UKAD3Q00  B00IZCSW3M      5.0   \n",
       "\n",
       "                                                 reviews   year month day  \\\n",
       "0      not much write but exactly supposed filters po...   2014    02  28   \n",
       "2      primary job device block breath would otherwis...   2013    08  28   \n",
       "7      use cable run output pedal chain input fender ...   2012    11  16   \n",
       "12     if not use using large sustaining pedal playin...   2013    11  17   \n",
       "28     hosa xlr cables affordable heavily madei large...   2012    07  11   \n",
       "...                                                  ...    ...   ...  ..   \n",
       "10178  larger ones sometimes neck snap thought might ...   2014    03  22   \n",
       "10181  thing tiny not breakable swivel head original ...   2014    02   9   \n",
       "10188  several capos but first adjustable tension sev...   2014    04  29   \n",
       "10222  twice price standard strings daddario nyxl str...   2014    06  21   \n",
       "10239  daddarios nickel plated electric guitar string...   2014    06  17   \n",
       "\n",
       "       helpful_rate  sentiment_value  word_count  \n",
       "0               0.0                0          25  \n",
       "2               1.0               -1          48  \n",
       "7               0.0                0          87  \n",
       "12              0.0                0          16  \n",
       "28              1.0               -2          29  \n",
       "...             ...              ...         ...  \n",
       "10178           0.5               -5          44  \n",
       "10181           0.0                0          12  \n",
       "10188           0.0               -5          72  \n",
       "10222           0.0                0          40  \n",
       "10239           1.0               -3         108  \n",
       "\n",
       "[1505 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentiment score of the whole review\n",
    "review[review['sentiment_value']<=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>sentiment_value</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>A27L5L6I7OSV5B</td>\n",
       "      <td>B0002CZV82</td>\n",
       "      <td>2.0</td>\n",
       "      <td>greg abrams review entitled but tone snobs rev...</td>\n",
       "      <td>2011</td>\n",
       "      <td>06</td>\n",
       "      <td>30</td>\n",
       "      <td>0.95</td>\n",
       "      <td>139</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5973</th>\n",
       "      <td>A1LCG14GZJI5LO</td>\n",
       "      <td>B000Y7Q2C4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>love capo let tell capo journey ive used every...</td>\n",
       "      <td>2014</td>\n",
       "      <td>05</td>\n",
       "      <td>30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>105</td>\n",
       "      <td>637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8636</th>\n",
       "      <td>A35YS5ND3K5FN</td>\n",
       "      <td>B004ISK48S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>constructionthis amp certainly looks sharp not...</td>\n",
       "      <td>2011</td>\n",
       "      <td>02</td>\n",
       "      <td>10</td>\n",
       "      <td>0.99</td>\n",
       "      <td>130</td>\n",
       "      <td>719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           review_id        asin  overall  \\\n",
       "412   A27L5L6I7OSV5B  B0002CZV82      2.0   \n",
       "5973  A1LCG14GZJI5LO  B000Y7Q2C4      5.0   \n",
       "8636   A35YS5ND3K5FN  B004ISK48S      5.0   \n",
       "\n",
       "                                                reviews   year month day  \\\n",
       "412   greg abrams review entitled but tone snobs rev...   2011    06  30   \n",
       "5973  love capo let tell capo journey ive used every...   2014    05  30   \n",
       "8636  constructionthis amp certainly looks sharp not...   2011    02  10   \n",
       "\n",
       "      helpful_rate  sentiment_value  word_count  \n",
       "412           0.95              139         713  \n",
       "5973          1.00              105         637  \n",
       "8636          0.99              130         719  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review[review['sentiment_value']>=100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sentiment_value', ylabel='Density'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the distribution of reviews \n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(10,6)})\n",
    "sns.distplot(review['sentiment_value'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From above distplot, we can state that reviews of this musical instrument are having somewhat normal distribution with positive and negative seniment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "review['index']=range(0,len(review))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='index', ylabel='sentiment_value'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "sns.lineplot(y='sentiment_value',x='index',data=review)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From above graph, it can be stated that most of the reviews are positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sentence sentiment value to sentence word count'}, xlabel='word_count', ylabel='sentiment_value'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "review.plot.scatter(x='word_count', y='sentiment_value', figsize=(15,10), title='Sentence sentiment value to sentence word count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function will return sentiment category based on values in sentiment_value column\n",
    "\n",
    "def sentiment_cat(row):\n",
    "    \n",
    "    '''This function returns sentiment category based on values in sentiment_value column'''\n",
    "    \n",
    "    if row['sentiment_value'] < 0:\n",
    "        val = 'Negative'\n",
    "    elif row['sentiment_value'] == 0: \n",
    "        val = 'Neutral'\n",
    "    elif row['sentiment_value'] > 0:\n",
    "        val = 'Positive'\n",
    "    else:\n",
    "        val = -1\n",
    "    return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>review_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>overall</th>\n",
       "      <th>reviews</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>helpful_rate</th>\n",
       "      <th>sentiment_value</th>\n",
       "      <th>word_count</th>\n",
       "      <th>index</th>\n",
       "      <th>sentiment_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2IBPI20UZIR0U</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>not much write but exactly supposed filters po...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A14VAT5EAX3D9S</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>product exactly quite affordablei not realized...</td>\n",
       "      <td>2013</td>\n",
       "      <td>03</td>\n",
       "      <td>16</td>\n",
       "      <td>0.93</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A195EZSQDW3E21</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>primary job device block breath would otherwis...</td>\n",
       "      <td>2013</td>\n",
       "      <td>08</td>\n",
       "      <td>28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2C00NNG1ZQQG2</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>nice windscreen protects mxl mic prevents pops...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A94QU4C90B1AX</td>\n",
       "      <td>1384719342</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pop filter great looks performs like studio fi...</td>\n",
       "      <td>2014</td>\n",
       "      <td>02</td>\n",
       "      <td>21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        review_id        asin  overall  \\\n",
       "0  A2IBPI20UZIR0U  1384719342      5.0   \n",
       "1  A14VAT5EAX3D9S  1384719342      5.0   \n",
       "2  A195EZSQDW3E21  1384719342      5.0   \n",
       "3  A2C00NNG1ZQQG2  1384719342      5.0   \n",
       "4   A94QU4C90B1AX  1384719342      5.0   \n",
       "\n",
       "                                             reviews   year month day  \\\n",
       "0  not much write but exactly supposed filters po...   2014    02  28   \n",
       "1  product exactly quite affordablei not realized...   2013    03  16   \n",
       "2  primary job device block breath would otherwis...   2013    08  28   \n",
       "3  nice windscreen protects mxl mic prevents pops...   2014    02  14   \n",
       "4  pop filter great looks performs like studio fi...   2014    02  21   \n",
       "\n",
       "   helpful_rate  sentiment_value  word_count  index sentiment_category  \n",
       "0          0.00                0          25      0            Neutral  \n",
       "1          0.93                2          55      1           Positive  \n",
       "2          1.00               -1          48      2           Negative  \n",
       "3          0.00                4          22      3           Positive  \n",
       "4          0.00                5          21      4           Positive  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying the function in our new column\n",
    "review['sentiment_category'] = review.apply(sentiment_cat, axis=1)\n",
    "review.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    8756\n",
       "Negative     860\n",
       "Neutral      645\n",
       "Name: sentiment_category, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review['sentiment_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:1192: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Year vs Sentiment count\n",
    "review.groupby(['year','sentiment_category'])['sentiment_category'].count().unstack().plot(figsize = (15,10),legend=True)\n",
    "plt.title('Year and Sentiment count')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sentiment count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Day of month vs Reviews count\n",
    "\n",
    "#Creating a dataframe\n",
    "day=pd.DataFrame(review.groupby('day')['reviews'].count()).reset_index()\n",
    "day['day']=day['day'].astype('int64')\n",
    "day.sort_values(by=['day'])\n",
    "\n",
    "#Plotting the graph\n",
    "sns.set(rc={'figure.figsize':(15, 10)})\n",
    "sns.barplot(x=\"day\", y=\"reviews\", data=day)\n",
    "plt.title('Day vs Reviews count')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Reviews count')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}