Final MovieReviews.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "df=pd.read_csv('./train.tsv',delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhraseId</th>\n",
       "      <th>SentenceId</th>\n",
       "      <th>Phrase</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>A series of escapades demonstrating the adage that what is good for the goose</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>A series</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>series</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhraseId  SentenceId  \\\n",
       "0         1           1   \n",
       "1         2           1   \n",
       "2         3           1   \n",
       "3         4           1   \n",
       "4         5           1   \n",
       "\n",
       "                                                                                                                                                                                         Phrase  \\\n",
       "0  A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .   \n",
       "1                                                                                                                 A series of escapades demonstrating the adage that what is good for the goose   \n",
       "2                                                                                                                                                                                      A series   \n",
       "3                                                                                                                                                                                             A   \n",
       "4                                                                                                                                                                                        series   \n",
       "\n",
       "   Sentiment  \n",
       "0          1  \n",
       "1          2  \n",
       "2          2  \n",
       "3          2  \n",
       "4          2  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Positive if sentiment greater than 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .</td>\n",
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       "      <td>A series of escapades demonstrating the adage that what is good for the goose</td>\n",
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       "      <td>8544</td>\n",
       "      <td>Hearst 's</td>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
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       "      <td>8544</td>\n",
       "      <td>avuncular chortles</td>\n",
       "      <td>3</td>\n",
       "      <td>pos</td>\n",
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       "    <tr>\n",
       "      <th>156058</th>\n",
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       "      <td>8544</td>\n",
       "      <td>avuncular</td>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
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       "      <th>156059</th>\n",
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       "      <td>8544</td>\n",
       "      <td>chortles</td>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156060 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "        PhraseId  SentenceId  \\\n",
       "0              1           1   \n",
       "1              2           1   \n",
       "2              3           1   \n",
       "3              4           1   \n",
       "4              5           1   \n",
       "...          ...         ...   \n",
       "156055    156056        8544   \n",
       "156056    156057        8544   \n",
       "156057    156058        8544   \n",
       "156058    156059        8544   \n",
       "156059    156060        8544   \n",
       "\n",
       "                                                                                                                                                                                              Phrase  \\\n",
       "0       A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .   \n",
       "1                                                                                                                      A series of escapades demonstrating the adage that what is good for the goose   \n",
       "2                                                                                                                                                                                           A series   \n",
       "3                                                                                                                                                                                                  A   \n",
       "4                                                                                                                                                                                             series   \n",
       "...                                                                                                                                                                                              ...   \n",
       "156055                                                                                                                                                                                     Hearst 's   \n",
       "156056                                                                                                                                                                     forced avuncular chortles   \n",
       "156057                                                                                                                                                                            avuncular chortles   \n",
       "156058                                                                                                                                                                                     avuncular   \n",
       "156059                                                                                                                                                                                      chortles   \n",
       "\n",
       "        Sentiment  rev  \n",
       "0               1  neg  \n",
       "1               2  neg  \n",
       "2               2  neg  \n",
       "3               2  neg  \n",
       "4               2  neg  \n",
       "...           ...  ...  \n",
       "156055          2  neg  \n",
       "156056          1  neg  \n",
       "156057          3  pos  \n",
       "156058          2  neg  \n",
       "156059          2  neg  \n",
       "\n",
       "[156060 rows x 5 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rev']=df['Sentiment'].apply(lambda x:'pos' if x>2 else 'neg')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lower']=df['Phrase'].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
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       "      <th>156058</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>avuncular</td>\n",
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       "      <th>156059</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>chortles</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>156060 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "2               2  neg   \n",
       "3               2  neg   \n",
       "4               2  neg   \n",
       "...           ...  ...   \n",
       "156055          2  neg   \n",
       "156056          1  neg   \n",
       "156057          3  pos   \n",
       "156058          2  neg   \n",
       "156059          2  neg   \n",
       "\n",
       "                                                                                                                                                                                               lower  \n",
       "0       a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story .  \n",
       "1                                                                                                                      a series of escapades demonstrating the adage that what is good for the goose  \n",
       "2                                                                                                                                                                                           a series  \n",
       "3                                                                                                                                                                                                  a  \n",
       "4                                                                                                                                                                                             series  \n",
       "...                                                                                                                                                                                              ...  \n",
       "156055                                                                                                                                                                                     hearst 's  \n",
       "156056                                                                                                                                                                     forced avuncular chortles  \n",
       "156057                                                                                                                                                                            avuncular chortles  \n",
       "156058                                                                                                                                                                                     avuncular  \n",
       "156059                                                                                                                                                                                      chortles  \n",
       "\n",
       "[156060 rows x 3 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['PhraseId','SentenceId','Phrase'],axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lower']=df['lower'].str.replace(\"[^a-zA-Z#]\", \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
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       "      <th>Sentiment</th>\n",
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       "      <td>3</td>\n",
       "      <td>pos</td>\n",
       "      <td>avuncular chortles</td>\n",
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       "      <th>156058</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>avuncular</td>\n",
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       "      <th>156059</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>chortles</td>\n",
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       "<p>156060 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "2               2  neg   \n",
       "3               2  neg   \n",
       "4               2  neg   \n",
       "...           ...  ...   \n",
       "156055          2  neg   \n",
       "156056          1  neg   \n",
       "156057          3  pos   \n",
       "156058          2  neg   \n",
       "156059          2  neg   \n",
       "\n",
       "                                                                                                                                                                                               lower  \n",
       "0       a series of escapades demonstrating the adage that what is good for the goose is also good for the gander   some of which occasionally amuses but none of which amounts to much of a story    \n",
       "1                                                                                                                      a series of escapades demonstrating the adage that what is good for the goose  \n",
       "2                                                                                                                                                                                           a series  \n",
       "3                                                                                                                                                                                                  a  \n",
       "4                                                                                                                                                                                             series  \n",
       "...                                                                                                                                                                                              ...  \n",
       "156055                                                                                                                                                                                     hearst  s  \n",
       "156056                                                                                                                                                                     forced avuncular chortles  \n",
       "156057                                                                                                                                                                            avuncular chortles  \n",
       "156058                                                                                                                                                                                     avuncular  \n",
       "156059                                                                                                                                                                                      chortles  \n",
       "\n",
       "[156060 rows x 3 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove stopwords ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lower']=df['lower'].apply(lambda x: ' '.join([w for w in x.split() if w not in stop]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
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       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "2               2  neg   \n",
       "3               2  neg   \n",
       "4               2  neg   \n",
       "...           ...  ...   \n",
       "156055          2  neg   \n",
       "156056          1  neg   \n",
       "156057          3  pos   \n",
       "156058          2  neg   \n",
       "156059          2  neg   \n",
       "\n",
       "                                                                                                               lower  \n",
       "0       series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story  \n",
       "1                                                                    series escapades demonstrating adage good goose  \n",
       "2                                                                                                             series  \n",
       "3                                                                                                                     \n",
       "4                                                                                                             series  \n",
       "...                                                                                                              ...  \n",
       "156055                                                                                                        hearst  \n",
       "156056                                                                                     forced avuncular chortles  \n",
       "156057                                                                                            avuncular chortles  \n",
       "156058                                                                                                     avuncular  \n",
       "156059                                                                                                      chortles  \n",
       "\n",
       "[156060 rows x 3 columns]"
      ]
     },
     "execution_count": 138,
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  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates('lower',keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
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       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "2               2  neg   \n",
       "3               2  neg   \n",
       "5               2  neg   \n",
       "...           ...  ...   \n",
       "156054          2  neg   \n",
       "156056          1  neg   \n",
       "156057          3  pos   \n",
       "156058          2  neg   \n",
       "156059          2  neg   \n",
       "\n",
       "                                                                                                               lower  \n",
       "0       series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story  \n",
       "1                                                                    series escapades demonstrating adage good goose  \n",
       "2                                                                                                             series  \n",
       "3                                                                                                                     \n",
       "5                                                                           escapades demonstrating adage good goose  \n",
       "...                                                                                                              ...  \n",
       "156054                                                                              hearst forced avuncular chortles  \n",
       "156056                                                                                     forced avuncular chortles  \n",
       "156057                                                                                            avuncular chortles  \n",
       "156058                                                                                                     avuncular  \n",
       "156059                                                                                                      chortles  \n",
       "\n",
       "[83922 rows x 3 columns]"
      ]
     },
     "execution_count": 140,
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   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lower']=df['lower'].apply(lambda x:x.split())"
   ]
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  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
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      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "2               2  neg   \n",
       "3               2  neg   \n",
       "5               2  neg   \n",
       "...           ...  ...   \n",
       "156054          2  neg   \n",
       "156056          1  neg   \n",
       "156057          3  pos   \n",
       "156058          2  neg   \n",
       "156059          2  neg   \n",
       "\n",
       "                                                                                                                               lower  \n",
       "0       [series, escapades, demonstrating, adage, good, goose, also, good, gander, occasionally, amuses, none, amounts, much, story]  \n",
       "1                                                                             [series, escapades, demonstrating, adage, good, goose]  \n",
       "2                                                                                                                           [series]  \n",
       "3                                                                                                                                 []  \n",
       "5                                                                                     [escapades, demonstrating, adage, good, goose]  \n",
       "...                                                                                                                              ...  \n",
       "156054                                                                                         [hearst, forced, avuncular, chortles]  \n",
       "156056                                                                                                 [forced, avuncular, chortles]  \n",
       "156057                                                                                                         [avuncular, chortles]  \n",
       "156058                                                                                                                   [avuncular]  \n",
       "156059                                                                                                                    [chortles]  \n",
       "\n",
       "[83922 rows x 3 columns]"
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     },
     "execution_count": 142,
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   "source": [
    "df"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing reviews with words less then 3 because they dont contribute much to sentiments "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "2               2  neg   \n",
       "3               2  neg   \n",
       "5               2  neg   \n",
       "...           ...  ...   \n",
       "156054          2  neg   \n",
       "156056          1  neg   \n",
       "156057          3  pos   \n",
       "156058          2  neg   \n",
       "156059          2  neg   \n",
       "\n",
       "                                                                                                                               lower  \n",
       "0       [series, escapades, demonstrating, adage, good, goose, also, good, gander, occasionally, amuses, none, amounts, much, story]  \n",
       "1                                                                             [series, escapades, demonstrating, adage, good, goose]  \n",
       "2                                                                                                                                NaN  \n",
       "3                                                                                                                                NaN  \n",
       "5                                                                                     [escapades, demonstrating, adage, good, goose]  \n",
       "...                                                                                                                              ...  \n",
       "156054                                                                                         [hearst, forced, avuncular, chortles]  \n",
       "156056                                                                                                 [forced, avuncular, chortles]  \n",
       "156057                                                                                                                           NaN  \n",
       "156058                                                                                                                           NaN  \n",
       "156059                                                                                                                           NaN  \n",
       "\n",
       "[83922 rows x 3 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
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   "source": [
    "df['lower']=df['lower'].apply(lambda x:'NaN' if len(x)<3 else x)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
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       "      <td>[demonstrating, adage, good, goose]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>[also, good, gander, occasionally, amuses, none, amounts, much, story]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>156048</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>[suggesting, sadness, obsession, beneath, hearst, forced, avuncular, chortles]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156049</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>[suggesting, sadness, obsession]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156053</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>[beneath, hearst, forced, avuncular, chortles]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156054</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>[hearst, forced, avuncular, chortles]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156056</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>[forced, avuncular, chortles]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47923 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "5               2  neg   \n",
       "9               2  neg   \n",
       "27              2  neg   \n",
       "...           ...  ...   \n",
       "156048          2  neg   \n",
       "156049          2  neg   \n",
       "156053          2  neg   \n",
       "156054          2  neg   \n",
       "156056          1  neg   \n",
       "\n",
       "                                                                                                                               lower  \n",
       "0       [series, escapades, demonstrating, adage, good, goose, also, good, gander, occasionally, amuses, none, amounts, much, story]  \n",
       "1                                                                             [series, escapades, demonstrating, adage, good, goose]  \n",
       "5                                                                                     [escapades, demonstrating, adage, good, goose]  \n",
       "9                                                                                                [demonstrating, adage, good, goose]  \n",
       "27                                                            [also, good, gander, occasionally, amuses, none, amounts, much, story]  \n",
       "...                                                                                                                              ...  \n",
       "156048                                                [suggesting, sadness, obsession, beneath, hearst, forced, avuncular, chortles]  \n",
       "156049                                                                                              [suggesting, sadness, obsession]  \n",
       "156053                                                                                [beneath, hearst, forced, avuncular, chortles]  \n",
       "156054                                                                                         [hearst, forced, avuncular, chortles]  \n",
       "156056                                                                                                 [forced, avuncular, chortles]  \n",
       "\n",
       "[47923 rows x 3 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['lower']!='NaN']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>156054</th>\n",
       "      <td>2</td>\n",
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       "      <th>156056</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>forced avuncular chortles</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>45896 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "5               2  neg   \n",
       "9               2  neg   \n",
       "27              2  neg   \n",
       "...           ...  ...   \n",
       "156048          2  neg   \n",
       "156049          2  neg   \n",
       "156053          2  neg   \n",
       "156054          2  neg   \n",
       "156056          1  neg   \n",
       "\n",
       "                                                                                                               lower  \n",
       "0       series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story  \n",
       "1                                                                    series escapades demonstrating adage good goose  \n",
       "5                                                                           escapades demonstrating adage good goose  \n",
       "9                                                                                     demonstrating adage good goose  \n",
       "27                                                      also good gander occasionally amuses none amounts much story  \n",
       "...                                                                                                              ...  \n",
       "156048                                         suggesting sadness obsession beneath hearst forced avuncular chortles  \n",
       "156049                                                                                  suggesting sadness obsession  \n",
       "156053                                                                      beneath hearst forced avuncular chortles  \n",
       "156054                                                                              hearst forced avuncular chortles  \n",
       "156056                                                                                     forced avuncular chortles  \n",
       "\n",
       "[45896 rows x 3 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['lower']=df['lower'].apply(lambda x: ' '.join([w for w in x if len(w)>3]))\n",
    "df.drop_duplicates('lower',keep='first',inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now we have cleaned reviews "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>forced avuncular chortles</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>45896 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "5               2  neg   \n",
       "9               2  neg   \n",
       "27              2  neg   \n",
       "...           ...  ...   \n",
       "156048          2  neg   \n",
       "156049          2  neg   \n",
       "156053          2  neg   \n",
       "156054          2  neg   \n",
       "156056          1  neg   \n",
       "\n",
       "                                                                                                               lower  \n",
       "0       series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story  \n",
       "1                                                                    series escapades demonstrating adage good goose  \n",
       "5                                                                           escapades demonstrating adage good goose  \n",
       "9                                                                                     demonstrating adage good goose  \n",
       "27                                                      also good gander occasionally amuses none amounts much story  \n",
       "...                                                                                                              ...  \n",
       "156048                                         suggesting sadness obsession beneath hearst forced avuncular chortles  \n",
       "156049                                                                                  suggesting sadness obsession  \n",
       "156053                                                                      beneath hearst forced avuncular chortles  \n",
       "156054                                                                              hearst forced avuncular chortles  \n",
       "156056                                                                                     forced avuncular chortles  \n",
       "\n",
       "[45896 rows x 3 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.options.display.max_colwidth = 200\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sentiment']=df['Sentiment'].apply(lambda x:1 if x>2 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
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       "<p>45896 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "5               2  neg   \n",
       "9               2  neg   \n",
       "27              2  neg   \n",
       "...           ...  ...   \n",
       "156048          2  neg   \n",
       "156049          2  neg   \n",
       "156053          2  neg   \n",
       "156054          2  neg   \n",
       "156056          1  neg   \n",
       "\n",
       "                                                                                                               lower  \\\n",
       "0       series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story   \n",
       "1                                                                    series escapades demonstrating adage good goose   \n",
       "5                                                                           escapades demonstrating adage good goose   \n",
       "9                                                                                     demonstrating adage good goose   \n",
       "27                                                      also good gander occasionally amuses none amounts much story   \n",
       "...                                                                                                              ...   \n",
       "156048                                         suggesting sadness obsession beneath hearst forced avuncular chortles   \n",
       "156049                                                                                  suggesting sadness obsession   \n",
       "156053                                                                      beneath hearst forced avuncular chortles   \n",
       "156054                                                                              hearst forced avuncular chortles   \n",
       "156056                                                                                     forced avuncular chortles   \n",
       "\n",
       "        sentiment  \n",
       "0               0  \n",
       "1               0  \n",
       "5               0  \n",
       "9               0  \n",
       "27              0  \n",
       "...           ...  \n",
       "156048          0  \n",
       "156049          0  \n",
       "156053          0  \n",
       "156054          0  \n",
       "156056          0  \n",
       "\n",
       "[45896 rows x 4 columns]"
      ]
     },
     "execution_count": 148,
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    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'lower':'Cleaned_Reviews'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'sentiment':'Sentiment_0_or_1'}, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>escapades demonstrating adage good goose</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>demonstrating adage good goose</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>also good gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>good gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>none amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining independent worth seeking</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>3</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining independent</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>even fans ismail merchant work suspect would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>even fans ismail merchant work</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>ismail merchant work</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>suspect would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0</td>\n",
       "      <td>neg</td>\n",
       "      <td>hard time sitting</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sentiment  rev  \\\n",
       "0            1  neg   \n",
       "1            2  neg   \n",
       "5            2  neg   \n",
       "9            2  neg   \n",
       "27           2  neg   \n",
       "31           2  neg   \n",
       "32           2  neg   \n",
       "38           2  neg   \n",
       "45           2  neg   \n",
       "47           1  neg   \n",
       "51           2  neg   \n",
       "63           4  pos   \n",
       "64           3  pos   \n",
       "67           3  pos   \n",
       "81           1  neg   \n",
       "82           2  neg   \n",
       "86           2  neg   \n",
       "94           1  neg   \n",
       "100          1  neg   \n",
       "103          0  neg   \n",
       "\n",
       "                                                                                                  Cleaned_Reviews  \\\n",
       "0    series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story   \n",
       "1                                                                 series escapades demonstrating adage good goose   \n",
       "5                                                                        escapades demonstrating adage good goose   \n",
       "9                                                                                  demonstrating adage good goose   \n",
       "27                                                   also good gander occasionally amuses none amounts much story   \n",
       "31                                                        good gander occasionally amuses none amounts much story   \n",
       "32                                                             gander occasionally amuses none amounts much story   \n",
       "38                                                                    occasionally amuses none amounts much story   \n",
       "45                                                                                 amuses none amounts much story   \n",
       "47                                                                                        none amounts much story   \n",
       "51                                                                                             amounts much story   \n",
       "63                                                     quiet introspective entertaining independent worth seeking   \n",
       "64                                                                   quiet introspective entertaining independent   \n",
       "67                                                                               quiet introspective entertaining   \n",
       "81                                                 even fans ismail merchant work suspect would hard time sitting   \n",
       "82                                                                                 even fans ismail merchant work   \n",
       "86                                                                                           ismail merchant work   \n",
       "94                                                                                suspect would hard time sitting   \n",
       "100                                                                                       would hard time sitting   \n",
       "103                                                                                             hard time sitting   \n",
       "\n",
       "     Sentiment_0_or_1  \n",
       "0                   0  \n",
       "1                   0  \n",
       "5                   0  \n",
       "9                   0  \n",
       "27                  0  \n",
       "31                  0  \n",
       "32                  0  \n",
       "38                  0  \n",
       "45                  0  \n",
       "47                  0  \n",
       "51                  0  \n",
       "63                  1  \n",
       "64                  1  \n",
       "67                  1  \n",
       "81                  0  \n",
       "82                  0  \n",
       "86                  0  \n",
       "94                  0  \n",
       "100                 0  \n",
       "103                 0  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #we will not use stemming because it just cutsoff the word without seeing the context\n",
    "# from nltk import PorterStemmer\n",
    "# ps=PorterStemmer()\n",
    "# df['Cleaned_Reviews']=df['Cleaned_Reviews'].apply(lambda x:' '.join([ps.stem(word) for word in x.split()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>Sentiment</th>\n",
       "      <th>rev</th>\n",
       "      <th>Cleaned_Reviews</th>\n",
       "      <th>Sentiment_0_or_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>series escapades demonstrating adage good goose</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>escapades demonstrating adage good goose</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>demonstrating adage good goose</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>also good gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156048</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>suggesting sadness obsession beneath hearst forced avuncular chortles</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156049</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>suggesting sadness obsession</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156053</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>beneath hearst forced avuncular chortles</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156054</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>hearst forced avuncular chortles</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156056</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>forced avuncular chortles</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45896 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sentiment  rev  \\\n",
       "0               1  neg   \n",
       "1               2  neg   \n",
       "5               2  neg   \n",
       "9               2  neg   \n",
       "27              2  neg   \n",
       "...           ...  ...   \n",
       "156048          2  neg   \n",
       "156049          2  neg   \n",
       "156053          2  neg   \n",
       "156054          2  neg   \n",
       "156056          1  neg   \n",
       "\n",
       "                                                                                                     Cleaned_Reviews  \\\n",
       "0       series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story   \n",
       "1                                                                    series escapades demonstrating adage good goose   \n",
       "5                                                                           escapades demonstrating adage good goose   \n",
       "9                                                                                     demonstrating adage good goose   \n",
       "27                                                      also good gander occasionally amuses none amounts much story   \n",
       "...                                                                                                              ...   \n",
       "156048                                         suggesting sadness obsession beneath hearst forced avuncular chortles   \n",
       "156049                                                                                  suggesting sadness obsession   \n",
       "156053                                                                      beneath hearst forced avuncular chortles   \n",
       "156054                                                                              hearst forced avuncular chortles   \n",
       "156056                                                                                     forced avuncular chortles   \n",
       "\n",
       "        Sentiment_0_or_1  \n",
       "0                      0  \n",
       "1                      0  \n",
       "5                      0  \n",
       "9                      0  \n",
       "27                     0  \n",
       "...                  ...  \n",
       "156048                 0  \n",
       "156049                 0  \n",
       "156053                 0  \n",
       "156054                 0  \n",
       "156056                 0  \n",
       "\n",
       "[45896 rows x 4 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## we have cleaned dataset and removed almost 90k useless reviews Let's go for EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud,ImageColorGenerator\n",
    "from PIL import Image\n",
    "import urllib\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words_positive = ' '.join(text for text in df['Cleaned_Reviews'][df['Sentiment_0_or_1']==1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combining the image with the dataset\n",
    "import numpy as np\n",
    "Mask = np.array(Image.open(requests.get('http://clipart-library.com/image_gallery2/Twitter-PNG-Image.png', stream=True).raw))\n",
    "\n",
    "# We use the ImageColorGenerator library from Wordcloud \n",
    "# Here we take the color of the image and impose it over our wordcloud\n",
    "image_colors = ImageColorGenerator(Mask)\n",
    "\n",
    "# Now we use the WordCloud function from the wordcloud library \n",
    "wc = WordCloud(background_color='black', height=1500, width=4000,mask=Mask).generate(all_words_positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "# Size of the image generated \n",
    "plt.figure(figsize=(10,20))\n",
    "\n",
    "# Here we recolor the words from the dataset to the image's color\n",
    "# recolor just recolors the default colors to the image's blue color\n",
    "# interpolation is used to smooth the image generated \n",
    "plt.imshow(wc.recolor(color_func=image_colors),interpolation=\"hamming\")\n",
    "\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words_negative = ' '.join(text for text in df['Cleaned_Reviews'][df['Sentiment_0_or_1']==0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combining the image with the dataset\n",
    "import numpy as np\n",
    "Mask = np.array(Image.open(requests.get('http://clipart-library.com/image_gallery2/Twitter-PNG-Image.png', stream=True).raw))\n",
    "\n",
    "# We use the ImageColorGenerator library from Wordcloud \n",
    "# Here we take the color of the image and impose it over our wordcloud\n",
    "image_colors = ImageColorGenerator(Mask)\n",
    "\n",
    "# Now we use the WordCloud function from the wordcloud library \n",
    "wc = WordCloud(background_color='black', height=1500, width=4000,mask=Mask).generate(all_words_negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import re\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "# Size of the image generated \n",
    "plt.figure(figsize=(10,20))\n",
    "\n",
    "# Here we recolor the words from the dataset to the image's color\n",
    "# recolor just recolors the default colors to the image's blue color\n",
    "# interpolation is used to smooth the image generated \n",
    "plt.imshow(wc.recolor(color_func=image_colors),interpolation=\"hamming\")\n",
    "\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'film': 1389, 'movie': 903, 'good': 517, 'story': 443, 'well': 411, 'funny': 406, 'like': 396, 'life': 360, 'comedy': 358, 'best': 326, ...})"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "word_freq_positive = nltk.FreqDist(all_words_positive.split())\n",
    "\n",
    "word_freq_positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FreqDist({'movie': 1916, 'film': 1877, 'like': 1350, 'story': 752, 'much': 628, 'time': 624, 'even': 585, 'characters': 568, 'little': 512, 'comedy': 507, ...})"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_negative = nltk.FreqDist(all_words_negative.split())\n",
    "\n",
    "word_freq_negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "sid = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    30623\n",
       "1    15273\n",
       "Name: Sentiment_0_or_1, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sentiment_0_or_1'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['scores']=df['Cleaned_Reviews'].apply(lambda review : sid.polarity_scores(review))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>Sentiment</th>\n",
       "      <th>rev</th>\n",
       "      <th>Cleaned_Reviews</th>\n",
       "      <th>Sentiment_0_or_1</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.594, 'pos': 0.406, 'compound': 0.8024}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>series escapades demonstrating adage good goose</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.633, 'pos': 0.367, 'compound': 0.4404}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>escapades demonstrating adage good goose</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.58, 'pos': 0.42, 'compound': 0.4404}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>demonstrating adage good goose</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>also good gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.569, 'pos': 0.431, 'compound': 0.6494}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>good gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.531, 'pos': 0.469, 'compound': 0.6494}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.714, 'pos': 0.286, 'compound': 0.3415}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.675, 'pos': 0.325, 'compound': 0.3415}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.597, 'pos': 0.403, 'compound': 0.4019}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining independent worth seeking</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.455, 'pos': 0.545, 'compound': 0.5859}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>3</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining independent</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.408, 'pos': 0.592, 'compound': 0.4404}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>even fans ismail merchant work suspect would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.31, 'neu': 0.69, 'pos': 0.0, 'compound': -0.3818}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>even fans ismail merchant work</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>ismail merchant work</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>suspect would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.545, 'neu': 0.455, 'pos': 0.0, 'compound': -0.3818}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.318, 'neu': 0.682, 'pos': 0.0, 'compound': -0.1027}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0</td>\n",
       "      <td>neg</td>\n",
       "      <td>hard time sitting</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.412, 'neu': 0.588, 'pos': 0.0, 'compound': -0.1027}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sentiment  rev  \\\n",
       "0            1  neg   \n",
       "1            2  neg   \n",
       "5            2  neg   \n",
       "9            2  neg   \n",
       "27           2  neg   \n",
       "31           2  neg   \n",
       "32           2  neg   \n",
       "38           2  neg   \n",
       "45           2  neg   \n",
       "47           1  neg   \n",
       "51           2  neg   \n",
       "63           4  pos   \n",
       "64           3  pos   \n",
       "67           3  pos   \n",
       "81           1  neg   \n",
       "82           2  neg   \n",
       "86           2  neg   \n",
       "94           1  neg   \n",
       "100          1  neg   \n",
       "103          0  neg   \n",
       "\n",
       "                                                                                                  Cleaned_Reviews  \\\n",
       "0    series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story   \n",
       "1                                                                 series escapades demonstrating adage good goose   \n",
       "5                                                                        escapades demonstrating adage good goose   \n",
       "9                                                                                  demonstrating adage good goose   \n",
       "27                                                   also good gander occasionally amuses none amounts much story   \n",
       "31                                                        good gander occasionally amuses none amounts much story   \n",
       "32                                                             gander occasionally amuses none amounts much story   \n",
       "38                                                                    occasionally amuses none amounts much story   \n",
       "45                                                                                 amuses none amounts much story   \n",
       "47                                                                                        none amounts much story   \n",
       "51                                                                                             amounts much story   \n",
       "63                                                     quiet introspective entertaining independent worth seeking   \n",
       "64                                                                   quiet introspective entertaining independent   \n",
       "67                                                                               quiet introspective entertaining   \n",
       "81                                                 even fans ismail merchant work suspect would hard time sitting   \n",
       "82                                                                                 even fans ismail merchant work   \n",
       "86                                                                                           ismail merchant work   \n",
       "94                                                                                suspect would hard time sitting   \n",
       "100                                                                                       would hard time sitting   \n",
       "103                                                                                             hard time sitting   \n",
       "\n",
       "     Sentiment_0_or_1  \\\n",
       "0                   0   \n",
       "1                   0   \n",
       "5                   0   \n",
       "9                   0   \n",
       "27                  0   \n",
       "31                  0   \n",
       "32                  0   \n",
       "38                  0   \n",
       "45                  0   \n",
       "47                  0   \n",
       "51                  0   \n",
       "63                  1   \n",
       "64                  1   \n",
       "67                  1   \n",
       "81                  0   \n",
       "82                  0   \n",
       "86                  0   \n",
       "94                  0   \n",
       "100                 0   \n",
       "103                 0   \n",
       "\n",
       "                                                            scores  \n",
       "0     {'neg': 0.0, 'neu': 0.594, 'pos': 0.406, 'compound': 0.8024}  \n",
       "1     {'neg': 0.0, 'neu': 0.633, 'pos': 0.367, 'compound': 0.4404}  \n",
       "5       {'neg': 0.0, 'neu': 0.58, 'pos': 0.42, 'compound': 0.4404}  \n",
       "9     {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}  \n",
       "27    {'neg': 0.0, 'neu': 0.569, 'pos': 0.431, 'compound': 0.6494}  \n",
       "31    {'neg': 0.0, 'neu': 0.531, 'pos': 0.469, 'compound': 0.6494}  \n",
       "32    {'neg': 0.0, 'neu': 0.714, 'pos': 0.286, 'compound': 0.3415}  \n",
       "38    {'neg': 0.0, 'neu': 0.675, 'pos': 0.325, 'compound': 0.3415}  \n",
       "45    {'neg': 0.0, 'neu': 0.597, 'pos': 0.403, 'compound': 0.4019}  \n",
       "47           {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}  \n",
       "51           {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}  \n",
       "63    {'neg': 0.0, 'neu': 0.455, 'pos': 0.545, 'compound': 0.5859}  \n",
       "64    {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}  \n",
       "67    {'neg': 0.0, 'neu': 0.408, 'pos': 0.592, 'compound': 0.4404}  \n",
       "81     {'neg': 0.31, 'neu': 0.69, 'pos': 0.0, 'compound': -0.3818}  \n",
       "82           {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}  \n",
       "86           {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}  \n",
       "94   {'neg': 0.545, 'neu': 0.455, 'pos': 0.0, 'compound': -0.3818}  \n",
       "100  {'neg': 0.318, 'neu': 0.682, 'pos': 0.0, 'compound': -0.1027}  \n",
       "103  {'neg': 0.412, 'neu': 0.588, 'pos': 0.0, 'compound': -0.1027}  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <th>27</th>\n",
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       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.569, 'pos': 0.431, 'compound': 0.6494}</td>\n",
       "      <td>0.6494</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sentiment  rev  \\\n",
       "0           1  neg   \n",
       "1           2  neg   \n",
       "5           2  neg   \n",
       "9           2  neg   \n",
       "27          2  neg   \n",
       "\n",
       "                                                                                                 Cleaned_Reviews  \\\n",
       "0   series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story   \n",
       "1                                                                series escapades demonstrating adage good goose   \n",
       "5                                                                       escapades demonstrating adage good goose   \n",
       "9                                                                                 demonstrating adage good goose   \n",
       "27                                                  also good gander occasionally amuses none amounts much story   \n",
       "\n",
       "    Sentiment_0_or_1  \\\n",
       "0                  0   \n",
       "1                  0   \n",
       "5                  0   \n",
       "9                  0   \n",
       "27                 0   \n",
       "\n",
       "                                                          scores  compound  \n",
       "0   {'neg': 0.0, 'neu': 0.594, 'pos': 0.406, 'compound': 0.8024}    0.8024  \n",
       "1   {'neg': 0.0, 'neu': 0.633, 'pos': 0.367, 'compound': 0.4404}    0.4404  \n",
       "5     {'neg': 0.0, 'neu': 0.58, 'pos': 0.42, 'compound': 0.4404}    0.4404  \n",
       "9   {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}    0.4404  \n",
       "27  {'neg': 0.0, 'neu': 0.569, 'pos': 0.431, 'compound': 0.6494}    0.6494  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['compound']  = df['scores'].apply(lambda score_dict: score_dict['compound'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
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       "      <td>gander occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.714, 'pos': 0.286, 'compound': 0.3415}</td>\n",
       "      <td>0.3415</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>occasionally amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.675, 'pos': 0.325, 'compound': 0.3415}</td>\n",
       "      <td>0.3415</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>amuses none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.597, 'pos': 0.403, 'compound': 0.4019}</td>\n",
       "      <td>0.4019</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>none amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>amounts much story</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining independent worth seeking</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.455, 'pos': 0.545, 'compound': 0.5859}</td>\n",
       "      <td>0.5859</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>3</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining independent</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>3</td>\n",
       "      <td>pos</td>\n",
       "      <td>quiet introspective entertaining</td>\n",
       "      <td>1</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.408, 'pos': 0.592, 'compound': 0.4404}</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>even fans ismail merchant work suspect would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.31, 'neu': 0.69, 'pos': 0.0, 'compound': -0.3818}</td>\n",
       "      <td>-0.3818</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>even fans ismail merchant work</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>ismail merchant work</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>suspect would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.545, 'neu': 0.455, 'pos': 0.0, 'compound': -0.3818}</td>\n",
       "      <td>-0.3818</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>would hard time sitting</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.318, 'neu': 0.682, 'pos': 0.0, 'compound': -0.1027}</td>\n",
       "      <td>-0.1027</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0</td>\n",
       "      <td>neg</td>\n",
       "      <td>hard time sitting</td>\n",
       "      <td>0</td>\n",
       "      <td>{'neg': 0.412, 'neu': 0.588, 'pos': 0.0, 'compound': -0.1027}</td>\n",
       "      <td>-0.1027</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sentiment  rev  \\\n",
       "0            1  neg   \n",
       "1            2  neg   \n",
       "5            2  neg   \n",
       "9            2  neg   \n",
       "27           2  neg   \n",
       "31           2  neg   \n",
       "32           2  neg   \n",
       "38           2  neg   \n",
       "45           2  neg   \n",
       "47           1  neg   \n",
       "51           2  neg   \n",
       "63           4  pos   \n",
       "64           3  pos   \n",
       "67           3  pos   \n",
       "81           1  neg   \n",
       "82           2  neg   \n",
       "86           2  neg   \n",
       "94           1  neg   \n",
       "100          1  neg   \n",
       "103          0  neg   \n",
       "\n",
       "                                                                                                  Cleaned_Reviews  \\\n",
       "0    series escapades demonstrating adage good goose also good gander occasionally amuses none amounts much story   \n",
       "1                                                                 series escapades demonstrating adage good goose   \n",
       "5                                                                        escapades demonstrating adage good goose   \n",
       "9                                                                                  demonstrating adage good goose   \n",
       "27                                                   also good gander occasionally amuses none amounts much story   \n",
       "31                                                        good gander occasionally amuses none amounts much story   \n",
       "32                                                             gander occasionally amuses none amounts much story   \n",
       "38                                                                    occasionally amuses none amounts much story   \n",
       "45                                                                                 amuses none amounts much story   \n",
       "47                                                                                        none amounts much story   \n",
       "51                                                                                             amounts much story   \n",
       "63                                                     quiet introspective entertaining independent worth seeking   \n",
       "64                                                                   quiet introspective entertaining independent   \n",
       "67                                                                               quiet introspective entertaining   \n",
       "81                                                 even fans ismail merchant work suspect would hard time sitting   \n",
       "82                                                                                 even fans ismail merchant work   \n",
       "86                                                                                           ismail merchant work   \n",
       "94                                                                                suspect would hard time sitting   \n",
       "100                                                                                       would hard time sitting   \n",
       "103                                                                                             hard time sitting   \n",
       "\n",
       "     Sentiment_0_or_1  \\\n",
       "0                   0   \n",
       "1                   0   \n",
       "5                   0   \n",
       "9                   0   \n",
       "27                  0   \n",
       "31                  0   \n",
       "32                  0   \n",
       "38                  0   \n",
       "45                  0   \n",
       "47                  0   \n",
       "51                  0   \n",
       "63                  1   \n",
       "64                  1   \n",
       "67                  1   \n",
       "81                  0   \n",
       "82                  0   \n",
       "86                  0   \n",
       "94                  0   \n",
       "100                 0   \n",
       "103                 0   \n",
       "\n",
       "                                                            scores  compound  \\\n",
       "0     {'neg': 0.0, 'neu': 0.594, 'pos': 0.406, 'compound': 0.8024}    0.8024   \n",
       "1     {'neg': 0.0, 'neu': 0.633, 'pos': 0.367, 'compound': 0.4404}    0.4404   \n",
       "5       {'neg': 0.0, 'neu': 0.58, 'pos': 0.42, 'compound': 0.4404}    0.4404   \n",
       "9     {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}    0.4404   \n",
       "27    {'neg': 0.0, 'neu': 0.569, 'pos': 0.431, 'compound': 0.6494}    0.6494   \n",
       "31    {'neg': 0.0, 'neu': 0.531, 'pos': 0.469, 'compound': 0.6494}    0.6494   \n",
       "32    {'neg': 0.0, 'neu': 0.714, 'pos': 0.286, 'compound': 0.3415}    0.3415   \n",
       "38    {'neg': 0.0, 'neu': 0.675, 'pos': 0.325, 'compound': 0.3415}    0.3415   \n",
       "45    {'neg': 0.0, 'neu': 0.597, 'pos': 0.403, 'compound': 0.4019}    0.4019   \n",
       "47           {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}    0.0000   \n",
       "51           {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}    0.0000   \n",
       "63    {'neg': 0.0, 'neu': 0.455, 'pos': 0.545, 'compound': 0.5859}    0.5859   \n",
       "64    {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}    0.4404   \n",
       "67    {'neg': 0.0, 'neu': 0.408, 'pos': 0.592, 'compound': 0.4404}    0.4404   \n",
       "81     {'neg': 0.31, 'neu': 0.69, 'pos': 0.0, 'compound': -0.3818}   -0.3818   \n",
       "82           {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}    0.0000   \n",
       "86           {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}    0.0000   \n",
       "94   {'neg': 0.545, 'neu': 0.455, 'pos': 0.0, 'compound': -0.3818}   -0.3818   \n",
       "100  {'neg': 0.318, 'neu': 0.682, 'pos': 0.0, 'compound': -0.1027}   -0.1027   \n",
       "103  {'neg': 0.412, 'neu': 0.588, 'pos': 0.0, 'compound': -0.1027}   -0.1027   \n",
       "\n",
       "    comp_score  \n",
       "0          pos  \n",
       "1          pos  \n",
       "5          pos  \n",
       "9          pos  \n",
       "27         pos  \n",
       "31         pos  \n",
       "32         pos  \n",
       "38         pos  \n",
       "45         pos  \n",
       "47         pos  \n",
       "51         pos  \n",
       "63         pos  \n",
       "64         pos  \n",
       "67         pos  \n",
       "81         neg  \n",
       "82         pos  \n",
       "86         pos  \n",
       "94         neg  \n",
       "100        neg  \n",
       "103        neg  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['comp_score'] = df['compound'].apply(lambda c: 'pos' if c >=0 else 'neg')\n",
    "\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4698884434373366"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "accuracy_score(df['rev'],df['comp_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         neg       0.83      0.26      0.39     30623\n",
      "         pos       0.38      0.89      0.53     15273\n",
      "\n",
      "    accuracy                           0.47     45896\n",
      "   macro avg       0.60      0.58      0.46     45896\n",
      "weighted avg       0.68      0.47      0.44     45896\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(df['rev'],df['comp_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df['Cleaned_Reviews']  # this time we want to look at the text\n",
    "y = df['Sentiment_0_or_1']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.feature_extraction.text import CountVectorizer\n",
    "# count_vect = CountVectorizer()\n",
    "\n",
    "# X_train_counts = count_vect.fit_transform(X_train)\n",
    "# X_train_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.feature_extraction.text import TfidfTransformer\n",
    "# tfidf_transformer = TfidfTransformer()\n",
    "\n",
    "# X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
    "# X_train_tfidf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', LinearSVC())])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "text_clf = Pipeline([('tfidf', TfidfVectorizer()),\n",
    "                     ('clf', LinearSVC()),\n",
    "])\n",
    "\n",
    "# Feed the training data through the pipeline\n",
    "text_clf.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Form a prediction set\n",
    "predictions = text_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8927 1169]\n",
      " [1712 3338]]\n"
     ]
    }
   ],
   "source": [
    "# Report the confusion matrix\n",
    "from sklearn import metrics\n",
    "print(metrics.confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.88      0.86     10096\n",
      "           1       0.74      0.66      0.70      5050\n",
      "\n",
      "    accuracy                           0.81     15146\n",
      "   macro avg       0.79      0.77      0.78     15146\n",
      "weighted avg       0.81      0.81      0.81     15146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print a classification report\n",
    "print(metrics.classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8097847616532418\n"
     ]
    }
   ],
   "source": [
    "# Print the overall accuracy\n",
    "print(metrics.accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction1 = text_clf.predict(['not good worst moview bad acting'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction2 = text_clf.predict(['good best movie worth watching'])\n",
    "prediction2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', LogisticRegression())])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "text_clf = Pipeline([('tfidf', TfidfVectorizer()),\n",
    "                     ('clf', LogisticRegression()),\n",
    "])\n",
    "\n",
    "# Feed the training data through the pipeline\n",
    "text_clf.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Form a prediction set\n",
    "predictions = text_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9474  622]\n",
      " [2421 2629]]\n"
     ]
    }
   ],
   "source": [
    "# Report the confusion matrix\n",
    "from sklearn import metrics\n",
    "print(metrics.confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.94      0.86     10096\n",
      "           1       0.81      0.52      0.63      5050\n",
      "\n",
      "    accuracy                           0.80     15146\n",
      "   macro avg       0.80      0.73      0.75     15146\n",
      "weighted avg       0.80      0.80      0.79     15146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print a classification report\n",
    "print(metrics.classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1 = text_clf.predict(['not good worst moview bad acting'])\n",
    "prediction1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction2 = text_clf.predict(['good best movie worth watching'])\n",
    "prediction2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer()),\n",
       "                ('clf', RandomForestClassifier())])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "text_clf = Pipeline([('tfidf', TfidfVectorizer()),\n",
    "                     ('clf', RandomForestClassifier()),\n",
    "])\n",
    "\n",
    "# Feed the training data through the pipeline\n",
    "text_clf.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Form a prediction set\n",
    "predictions = text_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9093 1003]\n",
      " [2002 3048]]\n"
     ]
    }
   ],
   "source": [
    "# Report the confusion matrix\n",
    "from sklearn import metrics\n",
    "print(metrics.confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.90      0.86     10096\n",
      "           1       0.75      0.60      0.67      5050\n",
      "\n",
      "    accuracy                           0.80     15146\n",
      "   macro avg       0.79      0.75      0.76     15146\n",
      "weighted avg       0.80      0.80      0.80     15146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print a classification report\n",
    "print(metrics.classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1 = text_clf.predict(['not good worst moview bad acting'])\n",
    "prediction1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction2 = text_clf.predict(['good best movie worth watching'])\n",
    "prediction2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is to predict exact movie rating , which gives accuracy of 0.43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer()),\n",
       "                ('clf', RandomForestClassifier())])"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df['Cleaned_Reviews']  # this time we want to look at the text\n",
    "y = df['Sentiment']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import LinearSVC\n",
    "y_train = to_categorical(y_train)\n",
    "text_clf = Pipeline([('tfidf', TfidfVectorizer()),\n",
    "                     ('clf', RandomForestClassifier()),\n",
    "])\n",
    "\n",
    "# Feed the training data through the pipeline\n",
    "text_clf.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = text_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       ...,\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# predictions[0].argmax()\n",
    "pred=[]\n",
    "b=0\n",
    "for i in predictions:\n",
    "    pred.append(predictions[b].argmax())\n",
    "    b+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 524  272  162   15    0]\n",
      " [1235  898 1114   76    1]\n",
      " [1268  466 3569  475   21]\n",
      " [1154   65 1136 1235  190]\n",
      " [ 474    6  132  422  236]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn import metrics\n",
    "print(metrics.confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.11      0.54      0.19       973\n",
      "           1       0.53      0.27      0.36      3324\n",
      "           2       0.58      0.62      0.60      5799\n",
      "           3       0.56      0.33      0.41      3780\n",
      "           4       0.53      0.19      0.27      1270\n",
      "\n",
      "    accuracy                           0.43     15146\n",
      "   macro avg       0.46      0.39      0.37     15146\n",
      "weighted avg       0.53      0.43      0.45     15146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print a classification report\n",
    "print(metrics.classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "score=text_clf.predict(['hard time sitting'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('dark_background')\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Embedding, LSTM, GlobalMaxPooling1D, SpatialDropout1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# X = df['Cleaned_Reviews']  # this time we want to look at the text\n",
    "# y = df['Sentiment']\n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "# phrase_len = X_train.apply(lambda p: len(p.split(' ')))\n",
    "# max_phrase_len = phrase_len.max()\n",
    "# print('max phrase len: {0}'.format(max_phrase_len))\n",
    "# plt.figure(figsize = (10, 8))\n",
    "# plt.hist(phrase_len, alpha = 0.2, density = True)\n",
    "# plt.xlabel('phrase len')\n",
    "# plt.ylabel('probability')\n",
    "# plt.grid(alpha = 0.25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# max_words = 8192\n",
    "# tokenizer = Tokenizer(\n",
    "#     num_words = max_words,\n",
    "#     filters = '\"#$%&()*+-/:;<=>@[\\]^_`{|}~'\n",
    "# )\n",
    "# tokenizer.fit_on_texts(X_train)\n",
    "# X_train = tokenizer.texts_to_sequences(X_train)\n",
    "# X_train = pad_sequences(X_train, maxlen = max_phrase_len)\n",
    "# y_train = to_categorical(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# batch_size = 512\n",
    "# epochs = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_lstm = Sequential()\n",
    "# model_lstm.add(Embedding(input_dim = max_words, output_dim = 256, input_length = max_phrase_len))\n",
    "# model_lstm.add(SpatialDropout1D(0.3))\n",
    "# model_lstm.add(LSTM(256, dropout = 0.3, recurrent_dropout = 0.3))\n",
    "# model_lstm.add(Dense(256, activation = 'relu'))\n",
    "# model_lstm.add(Dropout(0.3))\n",
    "# model_lstm.add(Dense(5, activation = 'softmax'))\n",
    "# model_lstm.compile(\n",
    "#     loss='categorical_crossentropy',\n",
    "#     optimizer='Adam',\n",
    "#     metrics=['accuracy']\n",
    "# )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 27675 samples, validate on 3075 samples\n",
      "Epoch 1/8\n",
      "27675/27675 [==============================] - 30s 1ms/step - loss: 1.4221 - accuracy: 0.3880 - val_loss: 1.3725 - val_accuracy: 0.4046\n",
      "Epoch 2/8\n",
      "27675/27675 [==============================] - 31s 1ms/step - loss: 1.2384 - accuracy: 0.4821 - val_loss: 1.1788 - val_accuracy: 0.4881\n",
      "Epoch 3/8\n",
      "27675/27675 [==============================] - 32s 1ms/step - loss: 1.0151 - accuracy: 0.5828 - val_loss: 1.1377 - val_accuracy: 0.5086\n",
      "Epoch 4/8\n",
      "27675/27675 [==============================] - 32s 1ms/step - loss: 0.9153 - accuracy: 0.6254 - val_loss: 1.1385 - val_accuracy: 0.5050\n",
      "Epoch 5/8\n",
      "27675/27675 [==============================] - 31s 1ms/step - loss: 0.8612 - accuracy: 0.6446 - val_loss: 1.1668 - val_accuracy: 0.5073\n",
      "Epoch 6/8\n",
      "27675/27675 [==============================] - 31s 1ms/step - loss: 0.8145 - accuracy: 0.6666 - val_loss: 1.2260 - val_accuracy: 0.5067\n",
      "Epoch 7/8\n",
      "27675/27675 [==============================] - 32s 1ms/step - loss: 0.7796 - accuracy: 0.6774 - val_loss: 1.2254 - val_accuracy: 0.5002\n",
      "Epoch 8/8\n",
      "27675/27675 [==============================] - 31s 1ms/step - loss: 0.7499 - accuracy: 0.6911 - val_loss: 1.2918 - val_accuracy: 0.5093\n"
     ]
    }
   ],
   "source": [
    "# history = model_lstm.fit(\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     validation_split = 0.1,\n",
    "#     epochs = 8,\n",
    "#     batch_size = 512\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.clf()\n",
    "# loss = history.history['loss']\n",
    "# val_loss = history.history['val_loss']\n",
    "# epochs = range(1, len(loss) + 1)\n",
    "# plt.plot(epochs, loss, 'g', label='Training loss')\n",
    "# plt.plot(epochs, val_loss, 'y', label='Validation loss')\n",
    "# plt.title('Training and validation loss')\n",
    "# plt.xlabel('Epochs')\n",
    "# plt.ylabel('Loss')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.clf()\n",
    "# acc = history.history['accuracy']\n",
    "# val_acc = history.history['val_accuracy']\n",
    "# plt.plot(epochs, acc, 'g', label='Training acc')\n",
    "# plt.plot(epochs, val_acc, 'y', label='Validation acc')\n",
    "# plt.title('Training and validation accuracy')\n",
    "# plt.xlabel('Epochs')\n",
    "# plt.ylabel('Accuracy')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 27, 256)           2097152   \n",
      "_________________________________________________________________\n",
      "spatial_dropout1d_3 (Spatial (None, 27, 256)           0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 256)               525312    \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 5)                 1285      \n",
      "=================================================================\n",
      "Total params: 2,689,541\n",
      "Trainable params: 2,689,541\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# model_lstm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# max_words = 8192\n",
    "# tokenizer = Tokenizer(\n",
    "#     num_words = max_words,\n",
    "#     filters = '\"#$%&()*+-/:;<=>@[\\]^_`{|}~'\n",
    "# )\n",
    "# tokenizer.fit_on_texts(X_test)\n",
    "# X_test = tokenizer.texts_to_sequences(X_test)\n",
    "# X_test = pad_sequences(X_test, maxlen = max_phrase_len)\n",
    "# y_test1 = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred_probabilities = model_lstm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.8846285e-03, 5.6503028e-02, 2.8439420e-01, 5.2308965e-01,\n",
       "        1.2612851e-01],\n",
       "       [2.6869031e-03, 2.7259475e-01, 5.6250691e-01, 1.6195674e-01,\n",
       "        2.5465069e-04],\n",
       "       [1.1575905e-02, 2.1634161e-01, 3.4707838e-01, 3.8168052e-01,\n",
       "        4.3323569e-02],\n",
       "       ...,\n",
       "       [9.0003549e-04, 9.7089047e-03, 8.4421641e-01, 1.4240365e-01,\n",
       "        2.7709908e-03],\n",
       "       [1.6030349e-04, 9.8412912e-03, 3.8206741e-01, 6.0343283e-01,\n",
       "        4.4981404e-03],\n",
       "       [1.0640770e-03, 3.6862543e-01, 4.6034837e-01, 1.6981265e-01,\n",
       "        1.4947154e-04]], dtype=float32)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pred_probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred=[]\n",
    "# for i in pred_probabilities:\n",
    "#     pred.append(i.argmax())\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 2,\n",
       " 3,\n",
       " 2,\n",
       " 2,\n",
       " ...]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       [0., 0., 0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84548     1\n",
       "120844    2\n",
       "78851     2\n",
       "134384    2\n",
       "72701     2\n",
       "         ..\n",
       "142379    3\n",
       "144559    1\n",
       "68908     2\n",
       "10417     4\n",
       "149135    3\n",
       "Name: Sentiment, Length: 15146, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.09      0.03      0.05       973\n",
      "           1       0.23      0.23      0.23      3324\n",
      "           2       0.44      0.53      0.48      5799\n",
      "           3       0.27      0.29      0.28      3780\n",
      "           4       0.12      0.05      0.07      1270\n",
      "\n",
      "    accuracy                           0.33     15146\n",
      "   macro avg       0.23      0.22      0.22     15146\n",
      "weighted avg       0.30      0.33      0.31     15146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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