Machine Learning & Data Science Masterclass - JP/18-Naive-Bayes-and-NLP/02-Text-Classification-Movie_Reviews_Sentiment_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Classification - Movie Reviews Sentiment Analysis\n",
    "\n",
    "### Goal: Given a set of text movie reviews that have been labeled negative or positive\n",
    "\n",
    "For more information on this dataset visit http://ai.stanford.edu/~amaas/data/sentiment/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../DATA/moviereviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neg</td>\n",
       "      <td>how do films like mouse hunt get into theatres...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neg</td>\n",
       "      <td>some talented actresses are blessed with a dem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pos</td>\n",
       "      <td>this has been an extraordinary year for austra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pos</td>\n",
       "      <td>according to hollywood movies made in last few...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neg</td>\n",
       "      <td>my first press screening of 1998 and already i...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                             review\n",
       "0   neg  how do films like mouse hunt get into theatres...\n",
       "1   neg  some talented actresses are blessed with a dem...\n",
       "2   pos  this has been an extraordinary year for austra...\n",
       "3   pos  according to hollywood movies made in last few...\n",
       "4   neg  my first press screening of 1998 and already i..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check to see if there are any missing values in the dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label      0\n",
       "review    35\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remove any reviews that are NaN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label     0\n",
       "review    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important point for dealing Text data\n",
    "\n",
    "+ **Check to see if any reviews are blank strings and not just NaN.** \n",
    "\n",
    "+ **Note: This means a review text could just be: \"\" or \"  \" or some other larger blank string. How can we check it?**\n",
    "+ [Resources](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.isspace.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['review'].str.isspace().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>pos</td>\n",
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       "      <th>147</th>\n",
       "      <td>pos</td>\n",
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       "      <td>pos</td>\n",
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       "      <th>283</th>\n",
       "      <td>pos</td>\n",
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       "      <th>307</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851</th>\n",
       "      <td>neg</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>pos</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     label review\n",
       "57     neg       \n",
       "71     pos       \n",
       "147    pos       \n",
       "151    pos       \n",
       "283    pos       \n",
       "307    pos       \n",
       "313    neg       \n",
       "323    pos       \n",
       "343    pos       \n",
       "351    neg       \n",
       "427    pos       \n",
       "501    neg       \n",
       "633    pos       \n",
       "675    neg       \n",
       "815    neg       \n",
       "851    neg       \n",
       "977    neg       \n",
       "1079   neg       \n",
       "1299   pos       \n",
       "1455   neg       \n",
       "1493   pos       \n",
       "1525   neg       \n",
       "1531   neg       \n",
       "1763   neg       \n",
       "1851   neg       \n",
       "1905   pos       \n",
       "1993   pos       "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['review'].str.isspace()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## using `~` to get the opposite of the conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neg</td>\n",
       "      <td>how do films like mouse hunt get into theatres...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neg</td>\n",
       "      <td>some talented actresses are blessed with a dem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pos</td>\n",
       "      <td>this has been an extraordinary year for austra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pos</td>\n",
       "      <td>according to hollywood movies made in last few...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neg</td>\n",
       "      <td>my first press screening of 1998 and already i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>pos</td>\n",
       "      <td>i like movies with albert brooks , and i reall...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>pos</td>\n",
       "      <td>it might surprise some to know that joel and e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>pos</td>\n",
       "      <td>the verdict : spine-chilling drama from horror...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>pos</td>\n",
       "      <td>i want to correct what i wrote in a former ret...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>pos</td>\n",
       "      <td>a couple of months ago , when i first download...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1938 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     label                                             review\n",
       "0      neg  how do films like mouse hunt get into theatres...\n",
       "1      neg  some talented actresses are blessed with a dem...\n",
       "2      pos  this has been an extraordinary year for austra...\n",
       "3      pos  according to hollywood movies made in last few...\n",
       "4      neg  my first press screening of 1998 and already i...\n",
       "...    ...                                                ...\n",
       "1995   pos  i like movies with albert brooks , and i reall...\n",
       "1996   pos  it might surprise some to know that joel and e...\n",
       "1997   pos  the verdict : spine-chilling drama from horror...\n",
       "1998   pos  i want to correct what i wrote in a former ret...\n",
       "1999   pos  a couple of months ago , when i first download...\n",
       "\n",
       "[1938 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~df['review'].str.isspace()] # now get get the items which are not spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~df['review'].str.isspace()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check if there is any empty string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [label, review]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "\n",
    "df[df['review'].apply(lambda review: review == '')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label     0\n",
       "review    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check value counts per label:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pos    969\n",
       "neg    969\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA on Bag of Words\n",
    "\n",
    "+ **using CountVectorizer model to get the top 20 words (that are not english stop words) per label type**\n",
    "+ [Resources](https://stackoverflow.com/questions/16288497/find-the-most-common-term-in-scikit-learn-classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vectorizer = CountVectorizer(stop_words='english')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Positive Ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_sparse_matrix = count_vectorizer.fit_transform(df[df['label'] == 'pos']['review'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '12th',\n",
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       " '16mm',\n",
       " '16th',\n",
       " '16x9',\n",
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       " 'afford',\n",
       " 'affordable',\n",
       " 'afforded',\n",
       " 'affords',\n",
       " 'affront',\n",
       " 'afi',\n",
       " 'aficionado',\n",
       " 'aficionados',\n",
       " 'afield',\n",
       " 'afire',\n",
       " 'afloat',\n",
       " 'afo',\n",
       " 'afoot',\n",
       " 'afore',\n",
       " 'aforementioned',\n",
       " 'aformentioned',\n",
       " 'afoul',\n",
       " 'afraid',\n",
       " 'afri',\n",
       " 'africa',\n",
       " 'african',\n",
       " 'africans',\n",
       " 'afro',\n",
       " 'aft',\n",
       " 'aftereffects',\n",
       " 'afterglow',\n",
       " 'afterlife',\n",
       " 'aftermath',\n",
       " 'aftermaths',\n",
       " 'afternoon',\n",
       " 'afternoons',\n",
       " 'afterschool',\n",
       " 'aftertaste',\n",
       " 'afterthought',\n",
       " 'afterward',\n",
       " 'agamemnon',\n",
       " 'agape',\n",
       " 'agatha',\n",
       " 'age',\n",
       " 'aged',\n",
       " 'agee',\n",
       " 'ageing',\n",
       " 'agencies',\n",
       " 'agency',\n",
       " 'agenda',\n",
       " 'agendas',\n",
       " 'agent',\n",
       " 'agents',\n",
       " 'ager',\n",
       " 'ages',\n",
       " 'aggravate',\n",
       " 'aggravating',\n",
       " 'aggravatingly',\n",
       " 'aggravation',\n",
       " 'aggression',\n",
       " 'aggressive',\n",
       " 'aggressively',\n",
       " 'aggrivate',\n",
       " 'aghast',\n",
       " 'agile',\n",
       " 'aging',\n",
       " 'agitated',\n",
       " 'agitation',\n",
       " 'agnes',\n",
       " 'agnostic',\n",
       " 'agnostics',\n",
       " 'ago',\n",
       " 'agonies',\n",
       " 'agonisingly',\n",
       " 'agonizing',\n",
       " 'agony',\n",
       " 'agree',\n",
       " 'agreeable',\n",
       " 'agreeably',\n",
       " 'agreed',\n",
       " 'agreeing',\n",
       " 'agreement',\n",
       " 'agreements',\n",
       " 'agrees',\n",
       " 'agricultural',\n",
       " 'aguilar',\n",
       " 'aguirresarobe',\n",
       " 'ah',\n",
       " 'ahab',\n",
       " 'ahead',\n",
       " 'ahem',\n",
       " 'ahmad',\n",
       " 'ahmed',\n",
       " 'aid',\n",
       " 'aidan',\n",
       " 'aide',\n",
       " 'aided',\n",
       " 'aides',\n",
       " 'aiding',\n",
       " 'aids',\n",
       " 'aiello',\n",
       " 'ailing',\n",
       " 'ailment',\n",
       " 'aim',\n",
       " 'aime',\n",
       " 'aimed',\n",
       " 'aimee',\n",
       " 'aiming',\n",
       " 'aimless',\n",
       " 'aimlessly',\n",
       " 'aims',\n",
       " 'ain',\n",
       " 'air',\n",
       " 'airborne',\n",
       " 'airbrushed',\n",
       " 'aircraft',\n",
       " 'aired',\n",
       " 'aires',\n",
       " 'airline',\n",
       " 'airliner',\n",
       " 'airplane',\n",
       " 'airplanes',\n",
       " 'airplay',\n",
       " 'airport',\n",
       " 'airs',\n",
       " 'airtime',\n",
       " ...]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 52,\n",
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       " 28,\n",
       " 6,\n",
       " 3,\n",
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       " 44,\n",
       " 4,\n",
       " 5,\n",
       " 6,\n",
       " 2,\n",
       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 5,\n",
       " 1,\n",
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       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 23,\n",
       " 1,\n",
       " 1,\n",
       " 38,\n",
       " 1,\n",
       " 16,\n",
       " 29,\n",
       " 19,\n",
       " 7,\n",
       " 1,\n",
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       " 6,\n",
       " 4,\n",
       " 3,\n",
       " 1,\n",
       " 10,\n",
       " 1,\n",
       " 2,\n",
       " 3,\n",
       " 5,\n",
       " 8,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 120,\n",
       " 16,\n",
       " 1,\n",
       " 2,\n",
       " 1,\n",
       " 9,\n",
       " 11,\n",
       " 1,\n",
       " 87,\n",
       " 25,\n",
       " 1,\n",
       " 23,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
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       " 5,\n",
       " 12,\n",
       " 3,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
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       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 102,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 1,\n",
       " 28,\n",
       " 9,\n",
       " 1,\n",
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       " 3,\n",
       " 7,\n",
       " 1,\n",
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       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 35,\n",
       " 1,\n",
       " 3,\n",
       " 3,\n",
       " 22,\n",
       " 2,\n",
       " 6,\n",
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       " 1,\n",
       " 2,\n",
       " 15,\n",
       " 2,\n",
       " 3,\n",
       " 1,\n",
       " 9,\n",
       " 1,\n",
       " 12,\n",
       " 2,\n",
       " 6,\n",
       " 2,\n",
       " 3,\n",
       " 7,\n",
       " 13,\n",
       " 61,\n",
       " 1,\n",
       " 1,\n",
       " 7,\n",
       " 4,\n",
       " 3,\n",
       " 6,\n",
       " 1,\n",
       " 29,\n",
       " 1,\n",
       " 1,\n",
       " 15,\n",
       " 1,\n",
       " 1,\n",
       " ...]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_sparse_matrix.sum(axis=0).tolist()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 words used for Positive reviews.\n",
      "[('film', 5002), ('movie', 2389), ('like', 1721), ('just', 1273), ('story', 1199), ('good', 1193), ('time', 1175), ('character', 1037), ('life', 1032), ('characters', 957), ('way', 864), ('films', 851), ('does', 828), ('best', 788), ('people', 769), ('make', 764), ('little', 751), ('really', 731), ('man', 728), ('new', 702)]\n"
     ]
    }
   ],
   "source": [
    "pos_freqs = zip(count_vectorizer.get_feature_names(), pos_sparse_matrix.sum(axis=0).tolist()[0])\n",
    "\n",
    "# sort from largest to smallest\n",
    "print('Top 20 words used for Positive reviews.')\n",
    "print(sorted(pos_freqs, key=lambda x:-x[1])[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Negative Ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vectorizer = CountVectorizer(stop_words='english')\n",
    "\n",
    "neg_sparse_matrix = count_vectorizer.fit_transform(df[df['label'] == 'neg']['review'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg_freqs = zip(count_vectorizer.get_feature_names(), neg_sparse_matrix.sum(axis=0).tolist()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 words used for Negative reviews.\n",
      "[('film', 4063), ('movie', 3131), ('like', 1808), ('just', 1480), ('time', 1127), ('good', 1117), ('bad', 997), ('character', 926), ('story', 908), ('plot', 888), ('characters', 838), ('make', 813), ('really', 743), ('way', 734), ('little', 696), ('don', 683), ('does', 666), ('doesn', 648), ('action', 635), ('scene', 634)]\n"
     ]
    }
   ],
   "source": [
    "print('Top 20 words used for Negative reviews.')\n",
    "print(sorted(neg_freqs, key=lambda x: -x[1])[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and Data\n",
    "\n",
    "**TASK: Split the data into features and a label (X and y) and then preform a train/test split. You may use whatever settings you like. To compare your results to the solution notebook, use `test_size=0.20, random_state=101`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['review'], df['label'], test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1550,), (388,), (1550,), (388,))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Model\n",
    "\n",
    "**TASK: Create a PipeLine that will both create a TF-IDF Vector out of the raw text data and fit a supervised learning model of your choice. Then fit that pipeline on the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "params ={\n",
    "    'C': [0.01, 0.1, 1],\n",
    "    'kernel': ['linear', 'poly', 'rbf'],\n",
    "    'degree': [3, 4],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer()),\n",
    "    ('grid_cv', GridSearchCV(SVC(), params, cv=5, verbose=1)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  90 out of  90 | elapsed: 10.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer()),\n",
       "                ('grid_cv',\n",
       "                 GridSearchCV(cv=5, estimator=SVC(),\n",
       "                              param_grid={'C': [0.01, 0.1, 1], 'degree': [3, 4],\n",
       "                                          'kernel': ['linear', 'poly', 'rbf']},\n",
       "                              verbose=1))])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         neg       0.81      0.84      0.83       191\n",
      "         pos       0.84      0.81      0.83       197\n",
      "\n",
      "    accuracy                           0.83       388\n",
      "   macro avg       0.83      0.83      0.83       388\n",
      "weighted avg       0.83      0.83      0.83       388\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(pipe, X_test, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# linearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer()),\n",
    "    ('svc', LinearSVC())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('svc', LinearSVC())])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         neg       0.81      0.86      0.83       191\n",
      "         pos       0.85      0.81      0.83       197\n",
      "\n",
      "    accuracy                           0.83       388\n",
      "   macro avg       0.83      0.83      0.83       388\n",
      "weighted avg       0.83      0.83      0.83       388\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(pipe, X_test, y_test);"
   ]
  }
 ],
 "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": 2
}