notebook dummy movie dataset

Dummy movie dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy Movie Review Dataset to demonstrate main NLP concepts to get you started with text analysis using Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading in the data\n",
    "\n",
    "movie_df = pd.read_csv('movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>movie</th>\n",
       "      <th>review</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alive in Wonderland</td>\n",
       "      <td>Tim Burton’s“Alice in Wonderland” was probably...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Beautifil Mind</td>\n",
       "      <td>Beautiful Mind is a biographical movie about J...</td>\n",
       "      <td>Such movies, telling us about the life of a fa...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Wanted</td>\n",
       "      <td>The well-known Universal Studios film company ...</td>\n",
       "      <td>The atmosphere of comics entirely suited to th...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "      <td>Entangled evidence, amazing deduction of Holme...</td>\n",
       "      <td>The plot is as follows: Holmes (Robert Downey ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "      <td>Entangled evidence, amazing deduction of Holme...</td>\n",
       "      <td>The plot is as follows: Holmes (Robert Downey ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Kites</td>\n",
       "      <td>“Kites” is not a traditional Indian movie, but...</td>\n",
       "      <td>This story is very exciting. Moreover, it is a...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                movie                                             review  \\\n",
       "0   1  Alive in Wonderland  Tim Burton’s“Alice in Wonderland” was probably...   \n",
       "1   2     A Beautifil Mind  Beautiful Mind is a biographical movie about J...   \n",
       "2   3               Wanted  The well-known Universal Studios film company ...   \n",
       "3   4      Sherlock Holmes  Robert Downey Jr. and Jude Law tried to embody...   \n",
       "4   4      Sherlock Holmes  Robert Downey Jr. and Jude Law tried to embody...   \n",
       "5   5                Kites  “Kites” is not a traditional Indian movie, but...   \n",
       "\n",
       "                                          Unnamed: 3  \\\n",
       "0                                                NaN   \n",
       "1  Such movies, telling us about the life of a fa...   \n",
       "2  The atmosphere of comics entirely suited to th...   \n",
       "3  Entangled evidence, amazing deduction of Holme...   \n",
       "4  Entangled evidence, amazing deduction of Holme...   \n",
       "5  This story is very exciting. Moreover, it is a...   \n",
       "\n",
       "                                          Unnamed: 4  \n",
       "0                                                NaN  \n",
       "1                                                NaN  \n",
       "2                                                NaN  \n",
       "3  The plot is as follows: Holmes (Robert Downey ...  \n",
       "4  The plot is as follows: Holmes (Robert Downey ...  \n",
       "5                                                NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh no - when copy-pasting the reviews into a csv file, single paragraphs in each review ended up in different columns. And it seems like we also have a duplicate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's remove duplicates first\n",
    "\n",
    "movie_df['dup'] = movie_df.duplicated(subset=None, keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>movie</th>\n",
       "      <th>review</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>dup</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alive in Wonderland</td>\n",
       "      <td>Tim Burton’s“Alice in Wonderland” was probably...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Beautifil Mind</td>\n",
       "      <td>Beautiful Mind is a biographical movie about J...</td>\n",
       "      <td>Such movies, telling us about the life of a fa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Wanted</td>\n",
       "      <td>The well-known Universal Studios film company ...</td>\n",
       "      <td>The atmosphere of comics entirely suited to th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "      <td>Entangled evidence, amazing deduction of Holme...</td>\n",
       "      <td>The plot is as follows: Holmes (Robert Downey ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "      <td>Entangled evidence, amazing deduction of Holme...</td>\n",
       "      <td>The plot is as follows: Holmes (Robert Downey ...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                movie                                             review  \\\n",
       "0   1  Alive in Wonderland  Tim Burton’s“Alice in Wonderland” was probably...   \n",
       "1   2     A Beautifil Mind  Beautiful Mind is a biographical movie about J...   \n",
       "2   3               Wanted  The well-known Universal Studios film company ...   \n",
       "3   4      Sherlock Holmes  Robert Downey Jr. and Jude Law tried to embody...   \n",
       "4   4      Sherlock Holmes  Robert Downey Jr. and Jude Law tried to embody...   \n",
       "\n",
       "                                          Unnamed: 3  \\\n",
       "0                                                NaN   \n",
       "1  Such movies, telling us about the life of a fa...   \n",
       "2  The atmosphere of comics entirely suited to th...   \n",
       "3  Entangled evidence, amazing deduction of Holme...   \n",
       "4  Entangled evidence, amazing deduction of Holme...   \n",
       "\n",
       "                                          Unnamed: 4    dup  \n",
       "0                                                NaN  False  \n",
       "1                                                NaN  False  \n",
       "2                                                NaN  False  \n",
       "3  The plot is as follows: Holmes (Robert Downey ...  False  \n",
       "4  The plot is as follows: Holmes (Robert Downey ...   True  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have created a new column that stores a boolean value whether the row is a duplicate row or not. We can see that for the second Sherlock Holmes the value in that column is True. We want to delete those rows where dup == True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_df = movie_df[movie_df['dup'] == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>movie</th>\n",
       "      <th>review</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>dup</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alive in Wonderland</td>\n",
       "      <td>Tim Burton’s“Alice in Wonderland” was probably...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Beautifil Mind</td>\n",
       "      <td>Beautiful Mind is a biographical movie about J...</td>\n",
       "      <td>Such movies, telling us about the life of a fa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Wanted</td>\n",
       "      <td>The well-known Universal Studios film company ...</td>\n",
       "      <td>The atmosphere of comics entirely suited to th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "      <td>Entangled evidence, amazing deduction of Holme...</td>\n",
       "      <td>The plot is as follows: Holmes (Robert Downey ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Kites</td>\n",
       "      <td>“Kites” is not a traditional Indian movie, but...</td>\n",
       "      <td>This story is very exciting. Moreover, it is a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                movie                                             review  \\\n",
       "0   1  Alive in Wonderland  Tim Burton’s“Alice in Wonderland” was probably...   \n",
       "1   2     A Beautifil Mind  Beautiful Mind is a biographical movie about J...   \n",
       "2   3               Wanted  The well-known Universal Studios film company ...   \n",
       "3   4      Sherlock Holmes  Robert Downey Jr. and Jude Law tried to embody...   \n",
       "5   5                Kites  “Kites” is not a traditional Indian movie, but...   \n",
       "\n",
       "                                          Unnamed: 3  \\\n",
       "0                                                NaN   \n",
       "1  Such movies, telling us about the life of a fa...   \n",
       "2  The atmosphere of comics entirely suited to th...   \n",
       "3  Entangled evidence, amazing deduction of Holme...   \n",
       "5  This story is very exciting. Moreover, it is a...   \n",
       "\n",
       "                                          Unnamed: 4    dup  \n",
       "0                                                NaN  False  \n",
       "1                                                NaN  False  \n",
       "2                                                NaN  False  \n",
       "3  The plot is as follows: Holmes (Robert Downey ...  False  \n",
       "5                                                NaN  False  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "del movie_df['dup'] # deleting \"dup\" column since we don't need it anymore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's use the code we found on stackoverflow in order to merge the 3 review columns into one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "movie_df['full_review'] = movie_df[movie_df.columns[2:]].apply(\n",
    "    lambda x: ' '.join(x.dropna().astype(str)),\n",
    "    axis=1\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List slicing and lambda functions in python\n",
    "Ok, a lot is going on in the function abovebut let's break it down\n",
    "1. we are creating a new column (full_review) which will contain the whole review \n",
    "2. we select all columns starting from columns 2 (review) until the end. That's what [2:] is doing: starting from column 3 (column 0 being the first) and select all the columns until the end, hence no ending slice. \n",
    "3. we are applying a lambda function to selected cell (x). A lambda function is a small anonymous function and comes handy when we are doing an operation only once and do not need to define a separate function for it\n",
    "4. the function iterates through each cell in the row starting at column 3 and joins it with the subsequent cells as a type string. If there cell is empty we ignore it (dropna()) otherwise we would have \"na\" added at the end of the review text. The reason why we are expresively defining that the cell should be of type string is because some entries might not have an incident text (all NANs) and would be picked up by python as a float type. \n",
    "5. the function goes through each row in the dataframe and does the merging described in 4.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>movie</th>\n",
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       "      <td>Alive in Wonderland</td>\n",
       "      <td>Tim Burton’s“Alice in Wonderland” was probably...</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Beautifil Mind</td>\n",
       "      <td>Beautiful Mind is a biographical movie about J...</td>\n",
       "      <td>Such movies, telling us about the life of a fa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Beautiful Mind is a biographical movie about J...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Wanted</td>\n",
       "      <td>The well-known Universal Studios film company ...</td>\n",
       "      <td>The atmosphere of comics entirely suited to th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The well-known Universal Studios film company ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "      <td>Entangled evidence, amazing deduction of Holme...</td>\n",
       "      <td>The plot is as follows: Holmes (Robert Downey ...</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Kites</td>\n",
       "      <td>“Kites” is not a traditional Indian movie, but...</td>\n",
       "      <td>This story is very exciting. Moreover, it is a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>“Kites” is not a traditional Indian movie, but...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                movie                                             review  \\\n",
       "0   1  Alive in Wonderland  Tim Burton’s“Alice in Wonderland” was probably...   \n",
       "1   2     A Beautifil Mind  Beautiful Mind is a biographical movie about J...   \n",
       "2   3               Wanted  The well-known Universal Studios film company ...   \n",
       "3   4      Sherlock Holmes  Robert Downey Jr. and Jude Law tried to embody...   \n",
       "5   5                Kites  “Kites” is not a traditional Indian movie, but...   \n",
       "\n",
       "                                          Unnamed: 3  \\\n",
       "0                                                NaN   \n",
       "1  Such movies, telling us about the life of a fa...   \n",
       "2  The atmosphere of comics entirely suited to th...   \n",
       "3  Entangled evidence, amazing deduction of Holme...   \n",
       "5  This story is very exciting. Moreover, it is a...   \n",
       "\n",
       "                                          Unnamed: 4  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "3  The plot is as follows: Holmes (Robert Downey ...   \n",
       "5                                                NaN   \n",
       "\n",
       "                                         full_review  \n",
       "0  Tim Burton’s“Alice in Wonderland” was probably...  \n",
       "1  Beautiful Mind is a biographical movie about J...  \n",
       "2  The well-known Universal Studios film company ...  \n",
       "3  Robert Downey Jr. and Jude Law tried to embody...  \n",
       "5  “Kites” is not a traditional Indian movie, but...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Robert Downey Jr. and Jude Law tried to embody unforgettable images of Sherlock Holmes and Doctor Watson in this bold new film version. In G. Ritchie’s “Sherlock Holmes” the legendary detective shows not only his unmatched intelligence, but also considerable physical training as well. Entangled evidence, amazing deduction of Holmes and his lively mind combine into an exciting series of grueling events. The director Guy Ritchie represents a fascinating adaptation of one of the world’s best detectives. Meet the new Sherlock Holmes! The plot is as follows: Holmes (Robert Downey Jr.) is rather depressed because of the possibility of Watson’s (Jude Law) marriage. His mood is also compounded by the absence of interesting cases. Trying to relax, he invents a silencer, does not go out of his room, participates in fights without rules and plays violin.'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.iloc[3,5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "\n",
    "Great, it has worked. Let's delete the redundant columns and do some text preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [2,3,4]  #column indexes we dont need\n",
    "movie_df.drop(movie_df.columns[cols],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>movie</th>\n",
       "      <th>full_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alive in Wonderland</td>\n",
       "      <td>Tim Burton’s“Alice in Wonderland” was probably...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Beautifil Mind</td>\n",
       "      <td>Beautiful Mind is a biographical movie about J...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Wanted</td>\n",
       "      <td>The well-known Universal Studios film company ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Kites</td>\n",
       "      <td>“Kites” is not a traditional Indian movie, but...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                movie                                        full_review\n",
       "0   1  Alive in Wonderland  Tim Burton’s“Alice in Wonderland” was probably...\n",
       "1   2     A Beautifil Mind  Beautiful Mind is a biographical movie about J...\n",
       "2   3               Wanted  The well-known Universal Studios film company ...\n",
       "3   4      Sherlock Holmes  Robert Downey Jr. and Jude Law tried to embody...\n",
       "5   5                Kites  “Kites” is not a traditional Indian movie, but..."
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from nltk.stem import WordNetLemmatizer, PorterStemmer, SnowballStemmer\n",
    "stop_words_file = 'SmartStoplist.txt'\n",
    "\n",
    "stop_words = []\n",
    "\n",
    "with open(stop_words_file, \"r\") as f:\n",
    "    for line in f:\n",
    "        stop_words.extend(line.split()) \n",
    "        \n",
    "stop_words = stop_words  \n",
    "\n",
    "def preprocess(raw_text):\n",
    "    \n",
    "    #regular expression keeping only letters \n",
    "    letters_only_text = re.sub(\"[^a-zA-Z]\", \" \", raw_text)\n",
    "\n",
    "    # convert to lower case and split into words -> convert string into list ( 'hello world' -> ['hello', 'world'])\n",
    "    words = letters_only_text.lower().split()\n",
    "\n",
    "    cleaned_words = []\n",
    "    lemmatizer = PorterStemmer() #plug in here any other stemmer or lemmatiser you want to try out\n",
    "    \n",
    "    # remove stopwords\n",
    "    for word in words:\n",
    "        if word not in stop_words:\n",
    "            cleaned_words.append(word)\n",
    "    \n",
    "    # stemm or lemmatise words\n",
    "    stemmed_words = []\n",
    "    for word in cleaned_words:\n",
    "        word = lemmatizer.stem(word)   #dont forget to change stem to lemmatize if you are using a lemmatizer\n",
    "        stemmed_words.append(word)\n",
    "    \n",
    "    # converting list back to string\n",
    "    return \" \".join(stemmed_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'disappoint demonstr preprocess function work'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sentence = \"this is a sentence to demonstrate how the preprocessing function works...!\"\n",
    "\n",
    "preprocess(test_sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can see that \"sentence\" was stemmed to \"sentenc\", all stop words and punctuation were removed.\n",
    "Let's apply that function to the incident texts in our movies dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_df['prep'] = movie_df['full_review'].apply(preprocess)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>id</th>\n",
       "      <th>movie</th>\n",
       "      <th>full_review</th>\n",
       "      <th>prep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alive in Wonderland</td>\n",
       "      <td>Tim Burton’s“Alice in Wonderland” was probably...</td>\n",
       "      <td>tim burton alic wonderland anticip movi year g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Beautifil Mind</td>\n",
       "      <td>Beautiful Mind is a biographical movie about J...</td>\n",
       "      <td>beauti mind biograph movi john forb nash junio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Wanted</td>\n",
       "      <td>The well-known Universal Studios film company ...</td>\n",
       "      <td>univers studio film compani plan shoot movi wa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "      <td>Robert Downey Jr. and Jude Law tried to embody...</td>\n",
       "      <td>robert downey jr jude law embodi unforgett ima...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Kites</td>\n",
       "      <td>“Kites” is not a traditional Indian movie, but...</td>\n",
       "      <td>kite tradit indian movi breath take action fil...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                movie                                        full_review  \\\n",
       "0   1  Alive in Wonderland  Tim Burton’s“Alice in Wonderland” was probably...   \n",
       "1   2     A Beautifil Mind  Beautiful Mind is a biographical movie about J...   \n",
       "2   3               Wanted  The well-known Universal Studios film company ...   \n",
       "3   4      Sherlock Holmes  Robert Downey Jr. and Jude Law tried to embody...   \n",
       "5   5                Kites  “Kites” is not a traditional Indian movie, but...   \n",
       "\n",
       "                                                prep  \n",
       "0  tim burton alic wonderland anticip movi year g...  \n",
       "1  beauti mind biograph movi john forb nash junio...  \n",
       "2  univers studio film compani plan shoot movi wa...  \n",
       "3  robert downey jr jude law embodi unforgett ima...  \n",
       "5  kite tradit indian movi breath take action fil...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Common Words\n",
    "In order to get an idea about a dataset, it's useful to have a look at the most common words. Reading through all incident texts is cumbersome and inefficient. Let's extract the most common key words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('movi', 8),\n",
       " ('film', 8),\n",
       " ('holm', 5),\n",
       " ('hard', 4),\n",
       " ('alic', 3),\n",
       " ('stori', 3),\n",
       " ('life', 3),\n",
       " ('speak', 3),\n",
       " ('mind', 3),\n",
       " ('want', 3)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from collections import Counter\n",
    "Counter(\" \".join(movie_df[\"prep\"]).split()).most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#nice library to produce wordclouds\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# if uising a Jupyter notebook, include:\n",
    "%matplotlib inline\n",
    "\n",
    "all_words = '' \n",
    "\n",
    "#looping through all incidents and joining them to one text, to extract most common words\n",
    "for arg in movie_df[\"prep\"]: \n",
    "\n",
    "    tokens = arg.split()  \n",
    "      \n",
    "    all_words += \" \".join(tokens)+\" \"\n",
    "\n",
    "wordcloud = WordCloud(width = 700, height = 700, \n",
    "                background_color ='white', \n",
    "                min_font_size = 10).generate(all_words) \n",
    "  \n",
    "# plot the WordCloud image                        \n",
    "plt.figure(figsize = (5, 5), facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) \n",
    "  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('beauti', 'mind') 2\n",
      "('robert', 'downey') 2\n",
      "('downey', 'jr') 2\n",
      "('jude', 'law') 2\n",
      "('sherlock', 'holm') 3\n",
      "('love', 'stori') 2\n"
     ]
    }
   ],
   "source": [
    "from nltk.util import ngrams\n",
    "n_gram = 2\n",
    "n_gram_dic = dict(Counter(ngrams(all_words.split(), n_gram)))\n",
    "\n",
    "for i in n_gram_dic:\n",
    "    if n_gram_dic[i] >= 2:\n",
    "        print(i, n_gram_dic[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## That's it (for now)\n",
    "\n",
    "Given it's only 5 movie reviews there is of course not much else interesting to do with the tools we have covered in the medium article. However, I hope I have covered enough to get you started. Feel free to check out the US Railroad incident notebook in the same github repository as this one. Feel free to copy the preprocessing function and re-use it and any other code you might find useful"
   ]
  }
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