EDA and Cleaning - Amazon Reviwes.ipynb

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    "# Sentiment Analysis\n",
    "(This is a type of a more general text classification problem)\n",
    "#### References\n",
    "Dataset - https://www.kaggle.com/snap/amazon-fine-food-reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.\n",
    "\n",
    "Number of reviews: 568,454<br>\n",
    "Number of users: 256,059<br>\n",
    "Number of products: 74,258<br>\n",
    "Timespan: Oct 1999 - Oct 2012<br>\n",
    "Number of Attributes/Columns in data: 10 \n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. Id\n",
    "2. ProductId - unique identifier for the product\n",
    "3. UserId - unqiue identifier for the user\n",
    "4. ProfileName\n",
    "5. HelpfulnessNumerator - number of users who found the review helpful\n",
    "6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not\n",
    "7. Score - rating between 1 and 5\n",
    "8. Time - timestamp for the review\n",
    "9. Summary - brief summary of the review\n",
    "10. Text - text of the review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Objective:\n",
    "Given a review, determine whether the review is positive (Rating of 4 or 5) or negative (rating of 1 or 2).\n",
    "\n",
    "[Q] How to determine if a review is positive or negative?<br>\n",
    "<br> \n",
    "[Ans] We could use the Score/Rating. A rating of 4 or 5 could be cosnidered a positive review. A review of 1 or 2 could be considered negative. A review of 3 is nuetral and ignored. This is an approximate way of determining the polarity (positivity/negativity) of a review."
   ]
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   "cell_type": "markdown",
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   "source": [
    "#### Loading the data\n",
    "The dataset is available in two forms\n",
    "1. .csv file\n",
    "2. SQLite Database\n",
    "\n",
    "Here as we only want to get the global sentiment of the recommendations (positive or negative), we will purposefully ignore all Scores equal to 3. If the score id above 3, then the recommendation wil be set to \"positive\". Otherwise, it will be set to \"negative\"."
   ]
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  {
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    "## Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import sqlite3    ## SQL Interface\n",
    "import pickle     ## Used to save your data - Converts objects to byte stream and vice versa\n",
    "import time       ## Time module - to caclulate time taken for execution\n",
    "\n",
    "## Modules to perform Text Preprocessing\n",
    "import re  # Regular Expressions\n",
    "# References:\n",
    "# https://docs.python.org/3/library/re.html (Official Documentation)\n",
    "# https://pymotw.com/3/re/ (This is nice tutorial with examples)\n",
    "\n",
    "import nltk # Natural Language Tool Kit\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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   "source": [
    "# using the SQLite Table to read data.\n",
    "conn = sqlite3.connect('database.sqlite')\n",
    "\n",
    "# filtering only positive and negative reviews i.e. \n",
    "# not taking into consideration those reviews with Score=3\n",
    "filtered_data = pd.read_sql_query(\"\"\"\n",
    "SELECT *\n",
    "FROM Reviews\n",
    "WHERE Score != 3\n",
    "\"\"\", conn)\n",
    "\n",
    "# close the connection to the database\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <th>HelpfulnessDenominator</th>\n",
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       "      <th>Summary</th>\n",
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       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
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       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      5  1303862400   \n",
       "1                     0                       0      1  1346976000   \n",
       "2                     1                       1      4  1219017600   \n",
       "3                     3                       3      2  1307923200   \n",
       "4                     0                       0      5  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...  \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...  "
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   "source": [
    "# View the top 5 rows\n",
    "filtered_data.head()"
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   "source": [
    "# shape of the dataset\n",
    "filtered_data.shape"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mark all the reviews with score 1 and 2 as negative while those with score 4 and 5 as positive. Ignore the reviews with score 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Give reviews with Score > 3 a positive rating, and reviews with a Score < 3 a negative rating.\n",
    "def partition(x):\n",
    "    if x < 3:\n",
    "        return 0 # indicating negative\n",
    "    return 1 # indicating positive\n",
    "\n",
    "## Pandas Series have a map function which apply function object to all the elements\n",
    "filtered_data['Score'] = filtered_data['Score'].map(partition)"
   ]
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   "cell_type": "code",
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       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
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       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "3                     3                       3      0  1307923200   \n",
       "4                     0                       0      1  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...  \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...  "
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    "filtered_data.head()"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Time is stored in unix timestamp format, however it only includes the date information and not the time information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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   "source": [
    "filtered_data['Time'].nunique()"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis and Data Cleaning\n",
    "### Cleaning Part 1 - Deduplication (Removing duplicates from the data)\n",
    "Many real world data contain duplicate entries which must be removed otherwise we may get biased results<br>\n",
    "There are sayings in ML that **Garbage in - Garbage out**, and **Better Data beats Fancier Algorithms**\n",
    "\n",
    "Let's check for some random user (Note: this was found after doing some experimentations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "      <td>15</td>\n",
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       "      <td>The price is right</td>\n",
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       "            Id   ProductId         UserId       ProfileName  \\\n",
       "8166      8935  B0007A0AP8  A74SHV5ZD3RLT  R. Ellis \"Bobby\"   \n",
       "162162  175817  B0014DUUFC  A74SHV5ZD3RLT  R. Ellis \"Bobby\"   \n",
       "494170  534267  B0007A0AOY  A74SHV5ZD3RLT  R. Ellis \"Bobby\"   \n",
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       "\n",
       "        HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "8166                      15                      15      1  1303862400   \n",
       "162162                    15                      15      1  1303862400   \n",
       "494170                    15                      15      1  1303862400   \n",
       "504729                    15                      15      1  1303862400   \n",
       "\n",
       "                   Summary                                               Text  \n",
       "8166    The price is right  We have a little Maltese that we spoil to no e...  \n",
       "162162  The price is right  We have a little Maltese that we spoil to no e...  \n",
       "494170  The price is right  We have a little Maltese that we spoil to no e...  \n",
       "504729  The price is right  We have a little Maltese that we spoil to no e...  "
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data[(filtered_data['ProfileName'] == 'R. Ellis \"Bobby\"') & \\\n",
    "              (filtered_data['Summary'] == 'The price is right')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Why these duplicate rows are present in the dataset?**\n",
    "1. On doing some research and using some domain knowledge it was found that if a user gives a review for a product, then sometimes, that review appears on all the different brands of the same product.\n",
    "2. Since this data was collected by scraping the HTML pages, we are getting the same review multiple times.\n",
    "3. For example refer the below pages:<br>\n",
    "https://www.amazon.com/dp/B0007A0AP8<br>\n",
    "https://www.amazon.com/dp/B0007A0AOY\n",
    "4. Getting the same text in train and test will lead to biased results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(363859, 10)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deduplication - If there are multiple rows with same user id and text, keep ony one and remove rest\n",
    "final=filtered_data.drop_duplicates(subset={\"UserId\", \"Text\"})\n",
    "final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observe:** We are left with approx 69% of the original data after cleaning which means more than 30% was duplicate data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### HelpfulnessNumerator must always be less than or equal to HelpfulnessDenominator. Lets check if this always the case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41159</th>\n",
       "      <td>44737</td>\n",
       "      <td>B001EQ55RW</td>\n",
       "      <td>A2V0I904FH7ABY</td>\n",
       "      <td>Ram</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1212883200</td>\n",
       "      <td>Pure cocoa taste with crunchy almonds inside</td>\n",
       "      <td>It was almost a 'love at first bite' - the per...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59301</th>\n",
       "      <td>64422</td>\n",
       "      <td>B000MIDROQ</td>\n",
       "      <td>A161DK06JJMCYF</td>\n",
       "      <td>J. E. Stephens \"Jeanne\"</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1224892800</td>\n",
       "      <td>Bought This for My Son at College</td>\n",
       "      <td>My son loves spaghetti so I didn't hesitate or...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id   ProductId          UserId              ProfileName  \\\n",
       "41159  44737  B001EQ55RW  A2V0I904FH7ABY                      Ram   \n",
       "59301  64422  B000MIDROQ  A161DK06JJMCYF  J. E. Stephens \"Jeanne\"   \n",
       "\n",
       "       HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "41159                     3                       2      1  1212883200   \n",
       "59301                     3                       1      1  1224892800   \n",
       "\n",
       "                                            Summary  \\\n",
       "41159  Pure cocoa taste with crunchy almonds inside   \n",
       "59301             Bought This for My Son at College   \n",
       "\n",
       "                                                    Text  \n",
       "41159  It was almost a 'love at first bite' - the per...  \n",
       "59301  My son loves spaghetti so I didn't hesitate or...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final[final['HelpfulnessNumerator'] > final['HelpfulnessDenominator']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is something wrong in these two rows and must be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(363857, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final=final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]\n",
    "final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    306779\n",
       "0     57078\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final['Score'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Not a very balanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Part 2 -  We can see that many reviews contain HTML tags and special characters which are unwanted for our purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I don't know if it's the cactus or the tequila or just the unique combination of ingredients, but the flavour of this hot sauce makes it one of a kind!  We picked up a bottle once on a trip we were on and brought it back home with us and were totally blown away!  When we realized that we simply couldn't find it anywhere in our city we were bummed.<br /><br />Now, because of the magic of the internet, we have a case of the sauce and are ecstatic because of it.<br /><br />If you love hot sauce..I mean really love hot sauce, but don't want a sauce that tastelessly burns your throat, grab a bottle of Tequila Picante Gourmet de Inclan.  Just realize that once you taste it, you will never want to use any other sauce.<br /><br />Thank you for the personal, incredible service! \n",
      "\n",
      "\n",
      "Twizzlers, Strawberry my childhood favorite candy, made in Lancaster Pennsylvania by Y & S Candies, Inc. one of the oldest confectionery Firms in the United States, now a Subsidiary of the Hershey Company, the Company was established in 1845 as Young and Smylie, they also make Apple Licorice Twists, Green Color and Blue Raspberry Licorice Twists, I like them all<br /><br />I keep it in a dry cool place because is not recommended it to put it in the fridge. According to the Guinness Book of Records, the longest Licorice Twist ever made measured 1.200 Feet (370 M) and weighted 100 Pounds (45 Kg) and was made by Y & S Candies, Inc. This Record-Breaking Twist became a Guinness World Record on July 19, 1998. This Product is Kosher! Thank You \n",
      "\n",
      "\n",
      "I bought these for my husband who is currently overseas. He loves these, and apparently his staff likes them also.<br />There are generous amounts of Twizzlers in each 16-ounce bag, and this was well worth the price. <a href=\"http://www.amazon.com/gp/product/B001GVISJM\">Twizzlers, Strawberry, 16-Ounce Bags (Pack of 6)</a> \n",
      "\n",
      "\n",
      "I have lived out of the US for over 7 yrs now, and I so miss my Twizzlers!!  When I go back to visit or someone visits me, I always stock up.  All I can say is YUM!<br />Sell these in Mexico and you will have a faithful buyer, more often than I'm able to buy them right now. \n",
      "\n",
      "\n",
      "Product received is as advertised.<br /><br /><a href=\"http://www.amazon.com/gp/product/B001GVISJM\">Twizzlers, Strawberry, 16-Ounce Bags (Pack of 6)</a> \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Refer - https://docs.python.org/3/library/re.html#re.findall\n",
    "\n",
    "# Print text of rows containing HTML tags\n",
    "i = 0\n",
    "for sen in final['Text'].values:\n",
    "    if(len(re.findall('<.*?>', sen))): # Find all strings starting with '<' and ending with '>'\n",
    "        print(sen,\"\\n\\n\")\n",
    "        i += 1\n",
    "    if i == 5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Part 3 -  Stopword Removal\n",
    "**Stopwords are those words which do not provide much meaning to the sentences**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"isn't\", 'because', 'i', 's', 'don', \"couldn't\", 'you', \"that'll\", 'up', 'than', 'all', 'doesn', 'is', 'm', 'its', \"you've\", \"weren't\", 'having', \"didn't\", \"mightn't\", 'off', \"she's\", 'this', 'being', 'herself', 'very', 'into', 'itself', 'more', 'such', 'nor', 'each', 'their', 'so', 'who', 'no', 'to', 'hadn', 'those', 'have', \"should've\", 'most', 'was', 're', 'ourselves', 'which', 'wasn', 'again', 'o', 'aren', 'isn', 'too', 'yourselves', 'he', 'ours', 'shan', 'weren', 'that', 'did', 'our', 'your', 'couldn', 'a', \"wasn't\", 'same', 'once', 'were', \"aren't\", 'am', 'below', 'mightn', 'she', 'an', 'these', 'after', 'only', \"wouldn't\", 'will', 'has', 've', \"haven't\", \"you're\", 'some', 'just', 'above', \"mustn't\", 't', 'where', 'himself', \"doesn't\", 'during', 'y', 'as', \"hadn't\", 'are', \"won't\", 'shouldn', 'now', 'about', 'here', \"you'll\", 'haven', 'doing', 'my', 'ma', \"it's\", 'why', 'should', 'out', 'won', 'wouldn', 'been', 'against', 'under', 'how', 'his', 'me', 'them', 'and', \"you'd\", 'had', 'ain', 'until', 'd', 'with', 'yours', 'further', 'by', 'it', 'other', 'through', 'down', 'needn', 'but', 'hers', 'while', 'own', 'him', 'not', 'be', 'can', \"shan't\", 'any', 'her', 'the', \"needn't\", 'didn', 'theirs', \"hasn't\", 'for', 'there', 'when', 'yourself', 'hasn', 'mustn', 'we', 'on', 'in', 'or', 'they', 'both', 'few', \"don't\", 'themselves', 'at', 'll', 'myself', 'if', 'before', 'whom', 'between', 'then', 'what', 'does', 'of', \"shouldn't\", 'from', 'over', 'do'}\n"
     ]
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english')) #set of stopwords\n",
    "print(stop_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Part 4 - Stemming\n",
    "**In linguistic morphology and information retrieval, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sno = nltk.stem.SnowballStemmer('english') #initialising the snowball stemmer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example of stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oper\n",
      "oper\n",
      "oper\n",
      "oper\n"
     ]
    }
   ],
   "source": [
    "print(sno.stem(\"operating\"))\n",
    "print(sno.stem(\"operated\"))\n",
    "print(sno.stem(\"operation\"))\n",
    "print(sno.stem(\"operate\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to perform all the cleaning on the reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_cleaning(series):\n",
    "    '''The function takes a Pandas Series object containing text in all the cells\n",
    "       And performs following Preprocessing steps on each cell:\n",
    "       1. Clean text from html tags\n",
    "       2. Clean text from punctuations and special characters\n",
    "       3. Retain only non-numeric Latin characters with lenght > 2\n",
    "       4. Remove stopwords from the sentence\n",
    "       5. Apply lower casing\n",
    "       6. Apply stemming to all the words in the sentence\n",
    "       \n",
    "       Return values:\n",
    "       1. final_string - List of cleaned sentences\n",
    "       2. list_of_sent - List of lists which will be used as input to the W2V model'''\n",
    "    \n",
    "    i = 0\n",
    "    string = \"\"\n",
    "    final_string = []    ## This list will contain cleaned sentences\n",
    "    list_of_sent = []    ## This is a list of lists used as input to the W2V model at a later stage\n",
    "    cleanr = re.compile('<.*?>') # Compile re to remove html tags\n",
    "    \n",
    "    for sent in series.values:\n",
    "        filtered_sent = []\n",
    "        sent = re.sub(cleanr, ' ', sent) # remove html tags\n",
    "        sent = re.sub('[^a-zA-Z0-9\\n]', ' ', sent) # remove special characters\n",
    "        sent = re.sub('\\s+',' ', sent) # replace multiple spaces with single space\n",
    "        sent = sent.lower() # convert all characters to lower case\n",
    "        for word in sent.split():\n",
    "            if word not in stop_words and len(word)>2:\n",
    "                word = sno.stem(word) # Apply Stemming using snowball stemmer\n",
    "                filtered_sent.append(word)\n",
    "        list_of_sent.append(filtered_sent) # This list is used later\n",
    "        string = \" \".join(filtered_sent) # Cleaned sentence\n",
    "        final_string.append(string) # List of cleaned sentences\n",
    "        i+=1\n",
    "    return final_string, list_of_sent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First 5 reviwes before cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than  most. \n",
      "\n",
      "\n",
      "Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as \"Jumbo\". \n",
      "\n",
      "\n",
      "This is a confection that has been around a few centuries.  It is a light, pillowy citrus gelatin with nuts - in this case Filberts. And it is cut into tiny squares and then liberally coated with powdered sugar.  And it is a tiny mouthful of heaven.  Not too chewy, and very flavorful.  I highly recommend this yummy treat.  If you are familiar with the story of C.S. Lewis' \"The Lion, The Witch, and The Wardrobe\" - this is the treat that seduces Edmund into selling out his Brother and Sisters to the Witch. \n",
      "\n",
      "\n",
      "If you are looking for the secret ingredient in Robitussin I believe I have found it.  I got this in addition to the Root Beer Extract I ordered (which was good) and made some cherry soda.  The flavor is very medicinal. \n",
      "\n",
      "\n",
      "Great taffy at a great price.  There was a wide assortment of yummy taffy.  Delivery was very quick.  If your a taffy lover, this is a deal. \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for x in final['Text'].iloc[:5].values:\n",
    "    print(x,\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First 5 reviwes after cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bought sever vital can dog food product found good qualiti product look like stew process meat smell better labrador finicki appreci product better \n",
      "\n",
      "\n",
      "product arriv label jumbo salt peanut peanut actual small size unsalt sure error vendor intend repres product jumbo \n",
      "\n",
      "\n",
      "confect around centuri light pillowi citrus gelatin nut case filbert cut tini squar liber coat powder sugar tini mouth heaven chewi flavor high recommend yummi treat familiar stori lewi lion witch wardrob treat seduc edmund sell brother sister witch \n",
      "\n",
      "\n",
      "look secret ingredi robitussin believ found got addit root beer extract order good made cherri soda flavor medicin \n",
      "\n",
      "\n",
      "great taffi great price wide assort yummi taffi deliveri quick taffi lover deal \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "final_string, list_of_sent = data_cleaning(final['Text'].iloc[:5])\n",
    "for x in final_string:\n",
    "    print(x,\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning all the reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time takes in seconds = 401.302072763443\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "final_string, list_of_sent = data_cleaning(final['Text'])\n",
    "end = time.time()\n",
    "print(\"Time takes in seconds =\", end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>CleanedText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "      <td>bought sever vital can dog food product found ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "      <td>product arriv label jumbo salt peanut peanut a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "      <td>confect around centuri light pillowi citrus ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "      <td>look secret ingredi robitussin believ found go...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "      <td>great taffi great price wide assort yummi taff...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      1  1303862400   \n",
       "1                     0                       0      0  1346976000   \n",
       "2                     1                       1      1  1219017600   \n",
       "3                     3                       3      0  1307923200   \n",
       "4                     0                       0      1  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \\\n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
       "\n",
       "                                         CleanedText  \n",
       "0  bought sever vital can dog food product found ...  \n",
       "1  product arriv label jumbo salt peanut peanut a...  \n",
       "2  confect around centuri light pillowi citrus ge...  \n",
       "3  look secret ingredi robitussin believ found go...  \n",
       "4  great taffi great price wide assort yummi taff...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding a column of CleanedText which displays the data after cleaning of the reviews\n",
    "final['CleanedText']=final_string\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the updated DataFrame as an SQL Table for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sqlite3.connect('final.sqlite')\n",
    "c=conn.cursor()\n",
    "final.to_sql('Reviews', conn, if_exists='replace', index = False)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the list_of_sent in a pickle file so that you can directly load the pickle file every time you use in future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('list_of_sent_for_input_to_w2v.pkl', 'wb') as pickle_file:\n",
    "    pickle.dump(list_of_sent, pickle_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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