Part I - Data Processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">PART I</h1>\n",
    "<h1 align=\"center\">Data Processing and Preparation for Training</h1>\n",
    "\n",
    "The aim was to process the semantic analysis of English texts, so the collection of positive and negative reviews was taken from [here](http://www.cs.jhu.edu/~mdredze/datasets/sentiment/), this dataset is based on a reviews of various Amazon products.\n",
    "\n",
    "To get started, install and unzip the archive ([unprocessed.tar.gz](http://www.cs.jhu.edu/~mdredze/datasets/sentiment/unprocessed.tar.gz)) and move the data to the project folder. Now, we import some libraries we need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also let's write a style for alignment in the middle of all graphs, images, etc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       ".output_png {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    ".output_png {\n",
    "    display: table-cell;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "</style>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we only need files with positive and negative reviews, let's delete the others. And for our negative and positive reviews, we will add a parent tag in which all the reviews will be, so it will be easier to make xml tree. Also, our xml library will complain about the character `&` when parsing, so we will replace it with the word `and`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove unnecessary files!\n",
      "Add additional tag to reviews!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "for parent, dirnames, filenames in os.walk('sorted_data'):\n",
    "    for fn in filenames:\n",
    "        filepath = os.path.join(parent, fn)\n",
    "        if not (fn.startswith('positive') or fn.startswith('negative')):\n",
    "            os.remove(filepath)\n",
    "        else:\n",
    "            with open(filepath, 'r') as original: data = original.read().replace(\"&\", \"and\").replace(\"\u001a\", \"\")\n",
    "            with open(filepath, 'w') as modified: modified.writelines([\"<reviews>\\n\"] + data.splitlines(True)[1:-1] + [\"</reviews>\"])\n",
    "            # if you do it for the first time - uncomment line below \n",
    "            # with open(filepath, 'w') as modified: modified.writelines([\"<reviews>\\n\"] + data.splitlines(True) + [\"</reviews>\"])    \n",
    "\n",
    "print(\"Remove unnecessary files!\\nAdd additional tag to reviews!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, since our reviews are in XML format, we need to process each line to get the value from the tags.\n",
    "\n",
    "**Note**: \n",
    "* **`errors='ignore'`** used to skip all possible incorrect characters in our documents;\n",
    "\n",
    "* **`readLines`** return all lines in the file, as a list where each line is an item in the list object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<reviews>\n",
      "\n",
      "<review><unique_id>\n",
      "\n",
      "B00008QS9V:it's_a_mighty_wind_a_blowin'!:carla_j._dinsmore_\"acoustic_music_junkie\"\n",
      "\n",
      "</unique_id>\n",
      "\n",
      "<unique_id>\n",
      "\n",
      "3276\n",
      "\n",
      "</unique_id>\n",
      "\n",
      "<asin>\n",
      "\n",
      "B00008QS9V\n",
      "\n",
      "</asin>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_read = open('sorted_data/music/positive.review', 'r', encoding='utf8', errors='ignore').readlines()\n",
    "for i in range(10):\n",
    "    print(test_read[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our directory with test training sets we have two files - with positive and negative reviews, each of these files contains 1000 unprocessed reviews. Let's check this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of Positive Reviews (Music): 1000 \n",
      "Number of Negative Reviews (Music): 1000\n"
     ]
    }
   ],
   "source": [
    "pos_reviews = open('sorted_data/music/positive.review', 'r', encoding='utf8', errors='ignore').read()\n",
    "neg_reviews = open('sorted_data/music/negative.review', 'r', encoding='utf8', errors='ignore').read()\n",
    "\n",
    "pos_rev_tree = ET.fromstring(pos_reviews)\n",
    "neg_rev_tree = ET.fromstring(neg_reviews)\n",
    "\n",
    "pos_tags = pos_rev_tree.findall('review')\n",
    "neg_tags = neg_rev_tree.findall('review')\n",
    "\n",
    "print('\\nNumber of Positive Reviews (Music):', len(pos_tags),\n",
    "      '\\nNumber of Negative Reviews (Music):', len(neg_tags))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** \n",
    "* **`fromstring()`** parses XML from a string directly into an Element, which is the root element of the parsed tree;\n",
    "* **`findall() `** finds all elements with a tag which are direct children of the current element (`reviews`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">XML Parse</h2>\n",
    "\n",
    "Now we will try to parse our XML file at a basic level. We will create a dictionary from the lists. Each list will contain certain tags and their content for single review. We already have a tree with positive and negative reviews, so all that remains is to parse it. But before that, let's list all the tags in our xml files so we don't have to repeat:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "REVIEW_TAGS = ['unique_id', 'asin', 'product_name', 'helpful', 'rating', 'title',\n",
    "               'date', 'reviewer', 'reviewer_location', 'review_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseXML(xml_reviews):\n",
    "    reviews = {}\n",
    "    count = 0\n",
    "\n",
    "    for item in xml_reviews:\n",
    "        count += 1\n",
    "        rev_name = 'review' + str(count)\n",
    "        reviews[rev_name] = [\n",
    "            REVIEW_TAGS[0] + ' | ' + item.find(REVIEW_TAGS[0]).text.strip(),\n",
    "            REVIEW_TAGS[1] + ' | ' + item.find(REVIEW_TAGS[1]).text.strip(),\n",
    "            REVIEW_TAGS[2] + ' | ' + item.find(REVIEW_TAGS[2]).text.strip(),\n",
    "            REVIEW_TAGS[3] + ' | ' + item.find(REVIEW_TAGS[3]).text.strip(),\n",
    "            REVIEW_TAGS[4] + ' | ' + item.find(REVIEW_TAGS[4]).text.strip(),\n",
    "            REVIEW_TAGS[5] + ' | ' + item.find(REVIEW_TAGS[5]).text.strip(),\n",
    "            REVIEW_TAGS[6] + ' | ' + item.find(REVIEW_TAGS[6]).text.strip(),\n",
    "            REVIEW_TAGS[7] + ' | ' + item.find(REVIEW_TAGS[7]).text.strip(),\n",
    "            REVIEW_TAGS[8] + ' | ' + item.find(REVIEW_TAGS[8]).text.strip(),\n",
    "            REVIEW_TAGS[9] + ' | ' + item.find(REVIEW_TAGS[9]).text.strip()\n",
    "        ]\n",
    "\n",
    "    return reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's actually create dictionaries with the required values and check output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['unique_id | B00008QS9V:it\\'s_a_mighty_wind_a_blowin\\'!:carla_j._dinsmore_\"acoustic_music_junkie\"',\n",
       " 'asin | B00008QS9V',\n",
       " 'product_name | A Mighty Wind: The Album: Music: Various Artists',\n",
       " 'helpful | ',\n",
       " 'rating | 5.0',\n",
       " \"title | It's a Mighty Wind a blowin'!\",\n",
       " 'date | July 5, 2006',\n",
       " 'reviewer | Carla J. Dinsmore \"Acoustic music junkie\"',\n",
       " 'reviewer_location | Wilmington, DE USA',\n",
       " 'review_text | This is a wonderful album, that evokes memories of the 60\\'s folk boom, yet contains original songs. I was amazed at the fantastic harmonies and musical arrangements.\\nAnyone who loves the movie \"A Mighty Wind\" and who loves folk music will fall in love with this album. I know I did']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_reviews_dict = parseXML(pos_tags)\n",
    "neg_reviews_dict = parseXML(neg_tags)\n",
    "pos_reviews_dict['review1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will turn our reviews dictionaries into a Pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dict_to_dataframe(reviews_dict):\n",
    "    # prepare our dataframe for the data\n",
    "    df = pd.DataFrame(columns=REVIEW_TAGS)\n",
    "    count = 0\n",
    "    for val in reviews_dict.values():\n",
    "        df.loc[count] = [\n",
    "            val[0].split(\"|\")[1], val[1].split(\"|\")[1],\n",
    "            val[2].split(\"|\")[1], val[3].split(\"|\")[1],\n",
    "            val[4].split(\"|\")[1], val[5].split(\"|\")[1],\n",
    "            val[6].split(\"|\")[1], val[7].split(\"|\")[1],\n",
    "            val[8].split(\"|\")[1], val[9].split(\"|\")[1]\n",
    "        ]\n",
    "\n",
    "        count = count + 1\n",
    "\n",
    "    return df\n",
    "\n",
    "pos_music = dict_to_dataframe(pos_reviews_dict)\n",
    "neg_music = dict_to_dataframe(neg_reviews_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check for correctness:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>unique_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>product_name</th>\n",
       "      <th>helpful</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer</th>\n",
       "      <th>reviewer_location</th>\n",
       "      <th>review_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00008QS9V:it's_a_mighty_wind_a_blowin'!:carl...</td>\n",
       "      <td>B00008QS9V</td>\n",
       "      <td>A Mighty Wind: The Album: Music: Various Artists</td>\n",
       "      <td></td>\n",
       "      <td>5.0</td>\n",
       "      <td>It's a Mighty Wind a blowin'!</td>\n",
       "      <td>July 5, 2006</td>\n",
       "      <td>Carla J. Dinsmore \"Acoustic music junkie\"</td>\n",
       "      <td>Wilmington, DE USA</td>\n",
       "      <td>This is a wonderful album, that evokes memori...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00005JJ04:sometime_tuesday_morning_defies_de...</td>\n",
       "      <td>B00005JJ04</td>\n",
       "      <td>Sometime Tuesday Morning: Music: Johnny A.</td>\n",
       "      <td>4 of 4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Sometime Tuesday Morning defies description</td>\n",
       "      <td>May 3, 2005</td>\n",
       "      <td>Tim Withee</td>\n",
       "      <td>Auburn, WA United States</td>\n",
       "      <td>On one hand, this CD is a straight ahead inst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0002W4SGS:atreyu_jr:hellrun_\"dustin\"</td>\n",
       "      <td>B0002W4SGS</td>\n",
       "      <td>The Caitiff Choir: Music: It Dies Today</td>\n",
       "      <td>0 of 1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>atreyu JR</td>\n",
       "      <td>June 12, 2006</td>\n",
       "      <td>hellrun \"dustin\"</td>\n",
       "      <td>wisconsin</td>\n",
       "      <td>this band reminds me of the thrill i first go...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           unique_id         asin  \\\n",
       "0   B00008QS9V:it's_a_mighty_wind_a_blowin'!:carl...   B00008QS9V   \n",
       "1   B00005JJ04:sometime_tuesday_morning_defies_de...   B00005JJ04   \n",
       "2              B0002W4SGS:atreyu_jr:hellrun_\"dustin\"   B0002W4SGS   \n",
       "\n",
       "                                        product_name  helpful rating  \\\n",
       "0   A Mighty Wind: The Album: Music: Various Artists             5.0   \n",
       "1         Sometime Tuesday Morning: Music: Johnny A.   4 of 4    5.0   \n",
       "2            The Caitiff Choir: Music: It Dies Today   0 of 1    5.0   \n",
       "\n",
       "                                          title            date  \\\n",
       "0                 It's a Mighty Wind a blowin'!    July 5, 2006   \n",
       "1   Sometime Tuesday Morning defies description     May 3, 2005   \n",
       "2                                     atreyu JR   June 12, 2006   \n",
       "\n",
       "                                     reviewer          reviewer_location  \\\n",
       "0   Carla J. Dinsmore \"Acoustic music junkie\"         Wilmington, DE USA   \n",
       "1                                  Tim Withee   Auburn, WA United States   \n",
       "2                            hellrun \"dustin\"                  wisconsin   \n",
       "\n",
       "                                         review_text  \n",
       "0   This is a wonderful album, that evokes memori...  \n",
       "1   On one hand, this CD is a straight ahead inst...  \n",
       "2   this band reminds me of the thrill i first go...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_music.head(n=3)"
   ]
  },
  {
   "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>unique_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>product_name</th>\n",
       "      <th>helpful</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer</th>\n",
       "      <th>reviewer_location</th>\n",
       "      <th>review_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00004YWGC:what_can_i_say?:ms._aj_\"right\"</td>\n",
       "      <td>B00004YWGC</td>\n",
       "      <td>Back For The First Time: Music: Ludacris</td>\n",
       "      <td>1 of 13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>What can I say?</td>\n",
       "      <td>May 4, 2006</td>\n",
       "      <td>Ms. AJ \"Right\"</td>\n",
       "      <td>North Carolina,  USA</td>\n",
       "      <td>I've always held the philosophy you are what ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B000621498:not_quite_ready_for_prime_time.:g</td>\n",
       "      <td>B000621498</td>\n",
       "      <td>Things Aren't So Beautiful Now: Music: A Thor...</td>\n",
       "      <td>1 of 2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>not quite ready for prime time.</td>\n",
       "      <td>May 8, 2006</td>\n",
       "      <td>g</td>\n",
       "      <td>san francisco</td>\n",
       "      <td>someone get this band a producer and put them...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0000AQS1A:disapointment:super_dave_\"super_dave\"</td>\n",
       "      <td>B0000AQS1A</td>\n",
       "      <td>Chicken N Beer: Music: Ludacris</td>\n",
       "      <td>1 of 2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Disapointment</td>\n",
       "      <td>July 1, 2006</td>\n",
       "      <td>Super Dave \"Super Dave\"</td>\n",
       "      <td>Knoxville, TN</td>\n",
       "      <td>Tihs Album is not all that good when it came ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           unique_id         asin  \\\n",
       "0          B00004YWGC:what_can_i_say?:ms._aj_\"right\"   B00004YWGC   \n",
       "1       B000621498:not_quite_ready_for_prime_time.:g   B000621498   \n",
       "2   B0000AQS1A:disapointment:super_dave_\"super_dave\"   B0000AQS1A   \n",
       "\n",
       "                                        product_name   helpful rating  \\\n",
       "0           Back For The First Time: Music: Ludacris   1 of 13    2.0   \n",
       "1   Things Aren't So Beautiful Now: Music: A Thor...    1 of 2    2.0   \n",
       "2                    Chicken N Beer: Music: Ludacris    1 of 2    2.0   \n",
       "\n",
       "                              title           date                  reviewer  \\\n",
       "0                   What can I say?    May 4, 2006            Ms. AJ \"Right\"   \n",
       "1   not quite ready for prime time.    May 8, 2006                         g   \n",
       "2                     Disapointment   July 1, 2006   Super Dave \"Super Dave\"   \n",
       "\n",
       "       reviewer_location                                        review_text  \n",
       "0   North Carolina,  USA   I've always held the philosophy you are what ...  \n",
       "1          san francisco   someone get this band a producer and put them...  \n",
       "2          Knoxville, TN   Tihs Album is not all that good when it came ...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neg_music.head(n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything is correct, wonderful! \n",
    "\n",
    "And now we will do what we did above, but for a reviews of all categories of Amazon datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "————————————————————————————————————————————————————————————\n",
      "| Parse        sorted_data\\apparel\\negative.review         |\n",
      "| Parse        sorted_data\\apparel\\positive.review         |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse       sorted_data\\automotive\\negative.review       |\n",
      "| Parse       sorted_data\\automotive\\positive.review       |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse          sorted_data\\baby\\negative.review          |\n",
      "| Parse          sorted_data\\baby\\positive.review          |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse         sorted_data\\beauty\\negative.review         |\n",
      "| Parse         sorted_data\\beauty\\positive.review         |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse         sorted_data\\books\\negative.review          |\n",
      "| Parse         sorted_data\\books\\positive.review          |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse     sorted_data\\camera_&_photo\\negative.review     |\n",
      "| Parse     sorted_data\\camera_&_photo\\positive.review     |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse sorted_data\\cell_phones_&_service\\negative.review  |\n",
      "| Parse sorted_data\\cell_phones_&_service\\positive.review  |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse sorted_data\\computer_&_video_games\\negative.review |\n",
      "| Parse sorted_data\\computer_&_video_games\\positive.review |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse          sorted_data\\dvd\\negative.review           |\n",
      "| Parse          sorted_data\\dvd\\positive.review           |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse      sorted_data\\electronics\\negative.review       |\n",
      "| Parse      sorted_data\\electronics\\positive.review       |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse      sorted_data\\gourmet_food\\negative.review      |\n",
      "| Parse      sorted_data\\gourmet_food\\positive.review      |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse        sorted_data\\grocery\\negative.review         |\n",
      "| Parse        sorted_data\\grocery\\positive.review         |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse sorted_data\\health_&_personal_care\\negative.review |\n",
      "| Parse sorted_data\\health_&_personal_care\\positive.review |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse   sorted_data\\jewelry_&_watches\\negative.review    |\n",
      "| Parse   sorted_data\\jewelry_&_watches\\positive.review    |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse  sorted_data\\kitchen_&_housewares\\negative.review  |\n",
      "| Parse  sorted_data\\kitchen_&_housewares\\positive.review  |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse       sorted_data\\magazines\\negative.review        |\n",
      "| Parse       sorted_data\\magazines\\positive.review        |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse         sorted_data\\music\\negative.review          |\n",
      "| Parse         sorted_data\\music\\positive.review          |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse  sorted_data\\musical_instruments\\negative.review   |\n",
      "| Parse  sorted_data\\musical_instruments\\positive.review   |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse    sorted_data\\office_products\\negative.review     |\n",
      "| Parse    sorted_data\\office_products\\positive.review     |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse     sorted_data\\outdoor_living\\negative.review     |\n",
      "| Parse     sorted_data\\outdoor_living\\positive.review     |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse        sorted_data\\software\\negative.review        |\n",
      "| Parse        sorted_data\\software\\positive.review        |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse   sorted_data\\sports_&_outdoors\\negative.review    |\n",
      "| Parse   sorted_data\\sports_&_outdoors\\positive.review    |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse    sorted_data\\tools_&_hardware\\negative.review    |\n",
      "| Parse    sorted_data\\tools_&_hardware\\positive.review    |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse      sorted_data\\toys_&_games\\negative.review      |\n",
      "| Parse      sorted_data\\toys_&_games\\positive.review      |\n",
      "————————————————————————————————————————————————————————————\n",
      "| Parse         sorted_data\\video\\negative.review          |\n",
      "| Parse         sorted_data\\video\\positive.review          |\n",
      "———————————————————————————————————————————————————————————— \n",
      "\n",
      "!!! Reviews parsing completed successfully !!!\n"
     ]
    }
   ],
   "source": [
    "pos_reviews, neg_reviews = [], []\n",
    "counter = 0 # for styled output \n",
    "\n",
    "for parent, dirnames, filenames in os.walk('sorted_data'):\n",
    "    for fn in filenames:\n",
    "        filepath = os.path.join(parent, fn)\n",
    "        if counter % 2 == 0: print(\"—\" * 60)\n",
    "        \n",
    "        if fn == \"positive.review\":\n",
    "            pos_rev_tree = ET.fromstring(open(filepath, 'r').read())\n",
    "            pos_tags = pos_rev_tree.findall('review')\n",
    "            pos_reviews.append(parseXML(pos_tags))\n",
    "            counter += 1\n",
    "            print(\"| Parse\", filepath.center(50), \"|\")\n",
    "        \n",
    "        if fn == \"negative.review\":\n",
    "            neg_rev_tree = ET.fromstring(open(filepath, 'r').read())\n",
    "            neg_tags = neg_rev_tree.findall('review')\n",
    "            neg_reviews.append(parseXML(neg_tags))\n",
    "            counter += 1\n",
    "            print(\"| Parse\", filepath.center(50), \"|\")\n",
    "\n",
    "print(\"—\" * 60, \"\\n\\n!!! Reviews parsing completed successfully !!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\">Data to Pandas Dataframe</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have parsed positive and negative reviews for 25 different categories, so let's check this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Positive Categories: 25 \n",
      "Number of Negative Categories: 25\n",
      "\n",
      "Number of Positive Reviews (Automotive Category): 584 \n",
      "Number of Negative Reviews (Books Category): 1000\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Positive Categories:\", len(pos_reviews), \n",
    "    \"\\nNumber of Negative Categories:\", len(neg_reviews))\n",
    "\n",
    "print(\"\\nNumber of Positive Reviews (Automotive Category):\", len(pos_reviews[1]), \n",
    "      \"\\nNumber of Negative Reviews (Books Category):\", len(neg_reviews[4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will turn our reviews dictionaries in each category into a Pandas DataFrame:\n",
    "\n",
    "**Note:** `pd.concat` - Concatenate pandas objects along a particular axis with optional set logic along the other axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df, negative_df = pd.DataFrame(columns=REVIEW_TAGS), pd.DataFrame(columns=REVIEW_TAGS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['unique_id | B0007QCQA4:good_sneakers:christopher_w._damico_\"macman\"', 'asin | B0007QCQA4', \"product_name | adidas Originals Men's Superstar II Basketball Shoe: Apparel\", 'helpful | 0 of 1', 'rating | 4.0', 'title | GOOD SNEAKERS', 'date | July 15, 2006', 'reviewer | Christopher W. Damico \"MACMAN\"', 'reviewer_location | NYC', \"review_text | GOOD LOOKING KICKS IF YOUR KICKIN IT OLD SCHOOL LIKE ME. AND COMFORTABLE. AND RELATIVELY CHEAP. I'LL ALWAYS KEEP A PAIR OF STAN SMITH'S AROUND FOR WEEKENDS\"] \n",
      "\n",
      "['unique_id | B0009N4F6I:works_very_well:i_have_9_cats', 'asin | B0009N4F6I', 'product_name | Wedgie Cup Holder: Automotive', 'helpful | 1 of 1', 'rating | 5.0', 'title | Works very well', 'date | August 19, 2006', 'reviewer | I have 9 cats', 'reviewer_location | Dela where?', \"review_text | I bought one today for my Mazda Tribute because I didn't like the built-in cup holders. It's actually very sturdy and fits very well.\"] \n",
      "\n",
      "['unique_id | B0002U1SFE:love_it!:a._kurczewski', 'asin | B0002U1SFE', 'product_name | Baby Einstein Discover and Play Activity Center: Baby', 'helpful | 1 of 1', 'rating | 4.0', 'title | Love it!', 'date | November 11, 2006', 'reviewer | A. Kurczewski', 'reviewer_location | Detroit, MI', 'review_text | My daughter loves this activity center.  I wish they would change two things about it.  I wish we could change out the toys and it folded up'] \n",
      "\n",
      "['unique_id | B0009U8XUU:one_of_the_products_i_am_pleased_with!:maggierose', 'asin | B0009U8XUU', 'product_name | Jonathan Product Redo Freshen-Up Mist For Hair and Skin(R): Beauty', 'helpful | 9 of 14', 'rating | 4.0', 'title | One of the products I am pleased with!', 'date | July 3, 2005', 'reviewer | maggierose', 'reviewer_location | Long Island, NY', 'review_text | I bought the Shampoo Theory and Condition Theory and this Redo Freshen-Up Mist. I have normal-type hair that needs to be washed every other day -- and if I use a lot of product, it needs to be washed every day. I have very sensitive skin too. This mist worked very well on my hair, definitely freshened it up and allowed me to restyle it with a quick blow dry. And it feels awesome on my skin. Well done, Jonathan'] \n",
      "\n",
      "['unique_id | 0375416811:founder_of_chick_lit:erin_oakes_\"avid_reader\"', 'asin | 0375416811', 'product_name | Bridget Jones Diary: Books: Helen Fielding,Tracie Bennett', 'helpful | ', 'rating | 5.0', 'title | Founder of chick lit', 'date | August 24, 2006', 'reviewer | Erin Oakes \"avid reader\"', 'reviewer_location | Central PA', \"review_text | Bridget Jones, modern day woman, brillant and doesn't know it, prone to accidents and mess ups but manages to come out of them.  \\n\\nThis is the book that started it all with the chick lit fever.  Bridget Jones is my hero!\"] \n",
      "\n",
      "['unique_id | B00009R6TA:great__backpack:w._mutual', 'asin | B00009R6TA', 'product_name | Canon Deluxe Photo Backpack 200EG for Canon EOS SLR Cameras: Camera and Photo', 'helpful | 1 of 1', 'rating | 5.0', 'title | great  backpack', 'date | November 12, 2006', 'reviewer | W. Mutual', 'reviewer_location | California', \"review_text | This is a great back pack! If there is anything I would change it would be the logo on the front. It's brite and shinny, and I'd rather not advertise that I have a camera in there! I colored it in with a felt pen and it works great....... Overall, I love the backpack!!\"] \n",
      "\n",
      "[\"unique_id | B0006J419Q:best_earpiece_i've_owned:d._jackson\", 'asin | B0006J419Q', 'product_name | Jabra C150 Headset for 2.5mm Universal Headset Jack: Cell Phones and Service', 'helpful | 1 of 2', 'rating | 5.0', \"title | Best earpiece I've owned\", 'date | August 29, 2006', 'reviewer | D. Jackson', 'reviewer_location | Washington, DC USA', \"review_text | I owned one of these for about three months, then it started to fall apart.  It was $30 at Best Buy.  I haven't replaced it, as $30 is a *lot*.\\n\\n$10?  I'm buying two more.  I can be moving 20 mph on my bicycle, talking to someone, with no problems.  It has far better sound quality than my 'backup', which is a Jabra with no boom.  The ear volume is adjustable on my phone, and I'm wondering what phones other reviewers have that won't let them adjust the volume\"] \n",
      "\n",
      "['unique_id | B0009Z3K9E:you_can\\'t_go_wrong_with_\"dawn_of_sorrow.\":k._m._bowles_\"i_like_pie\"', 'asin | B0009Z3K9E', 'product_name | Castlevania Dawn of Sorrow: Computer andamp; Video Games', 'helpful | 1 of 1', 'rating | 5.0', 'title | You can\\'t go wrong with \"Dawn of Sorrow.\"', 'date | October 7, 2006', 'reviewer | K. M. Bowles \"I Like pie\"', 'reviewer_location | VA Beach, VA', \"review_text | Castlevania: Dawn of Sorrow is the latest iteration of Konami's decades-old franchise and the first to appear on the Nintendo DS. Don't let the title fool you; this really isn't the dawn of anything, except maybe a bunch more Castlevania games on the DS. And the subtitle here is simply a workable way to include the letters D and S in the title of the game, as seems to be the requirement for the platform at this point. Dawn of Sorrow actually takes place a year after the events in Aria of Sorrow, which came out in 2003 on the Game Boy Advance. If you've played that game--or if you've played just about any recent Castlevania game actually--you'll be right at home with Dawn of Sorrow, because it's basically the same game repackaged to fit the DS. That isn't a bad thing though, because the Castlevania games have long carried the torch for good, old-fashioned 2D side-scrolling gameplay, and Dawn of Sorrow keeps that flame burning as bright as ever. \\n\\nThe star of Dawn of Sorrow is Soma Cruz, the young adventurer who narrowly avoided becoming the next Dracula in Aria of Sorrow. Soma has the ability to collect the souls of monsters he kills, thus inheriting their powers. A year after Soma escaped Dracula's castle, a mysterious woman named Celia shows up and attacks him. Celia is a priestess of a new cult that is attempting to resurrect Dracula. Since Soma already proved once before that he's capable of foiling the resurrection of the dark lord, the cult decides to eliminate him before he can mess up their plans as well. Soma survives the attack quite easily and Celia is forced to retreat. Apparently Soma doesn't take kindly to people who try to kill him, so he sets out to find Celia and prevent her from reviving Dracula. \\n\\nThe entire game takes place at the cult's base, which is a replica of Dracula's castle. In true Castlevania form you'll start off relatively weak, with only a knife and some mediocre armor. As you progress through areas of the castle, you'll fight monsters and search the mazelike hallways where you'll gain experience, as well as find new and better equipment. As you gain experience, you'll level up and your stats will increase in attack, defense, strength, constitution, intelligence, and luck. \\n\\nThroughout the game you'll also collect the souls of monsters you kill. These souls can be equipped to give Soma new abilities. There are three types of souls you can equip, as well as ability-type souls that grant you special powers without having to equip them. Ability souls are collected when you defeat bosses, and these abilities are usually required to progress through the game. By using these ability souls Soma can learn how to double-jump, move underwater, and break crystal blocks--all of which are required to access certain areas of the castle. \\n\\nCastlevania: Dawn of Sorrow sticks close to the tried-and-true formula, so it will be an instant hit with anyone with an affinity for the sort of explorative gameplay the series is known for. While it doesn't do much to distinguish itself as the first Castlevania on the DS, the updated look, excellent music, and added controls help to refine the series even further. The bottom line is: Whether you're familiar with the series or not, you can't go wrong with Dawn of Sorrow.\"] \n",
      "\n",
      "['unique_id | B0000TG9E2:excellent_fantasy,_with_provisos_:lonnie_e._holder_\"the_review\\'s_the_thing\"', 'asin | B0000TG9E2', \"product_name | Alice in Wonderland (Masterpiece Edition): DVD: Kathryn Beaumont,Ed Wynn,Richard Haydn,Sterling Holloway,Jerry Colonna,Verna Felton,J. Pat O'Malley,Bill Thompson,Heather Angel,Joseph Kearns,Larry Grey,Queenie Leonard,Dink Trout,Doris Lloyd,James MacDonald (II),Bill Lee (IV),Thurl Ravenscroft,Max Smith,Bob Hamlin,Don Barclay,Wilfred Jackson,Clyde Geronimi,Hamilton Luske\", 'helpful | 7 of 12', 'rating | 4.0', 'title | Excellent Fantasy, with Provisos', 'date | July 3, 2006', 'reviewer | Lonnie E. Holder \"The Review\\'s the Thing\"', 'reviewer_location | Sullivan, Illinois United States', 'review_text | The story of \"Alice in Wonderland\" was controversial from the moment that the Reverend Charles Lutwidge Dodgson, aka Lewis Carroll, wrote it.  Some took the story as horror rather than a childhood fantasy.  Others consider Alice\\'s journey as a trip into or through insanity.  For better or worse, Disney did capture many of these elements, and the same criticisms of the books that form the basis of this film apply to the film.  However, there are also those who loved the books and loved the film.  Normally I recommend Disney classis without reservations.  In the case of this movie, I will keep a couple of provisos.\\n\\nAlice is a young girl bored by her lessons.  As the length of the lessons grows, Alice soon drifts off, until a white rabbit passes by, muttering to himself that he is late.  Alice follows the white rabbit and falls into a rabbit hole that seems to fall forever.  Luckily for Alice her dress opens out into a kind of parachute and slows her descent.\\nOnce at the bottom of the hole, Alice discovers a door into which she is unable to pass because the door is too small.  However, there is a vial labeled \"drink me\" that Alice discovers makes her small enough to go through the door.\\n\\nAlice is soon on her way into a series of surreal adventures that are in the best tradition of adult animation.  Indeed, Alice might have worked better as an adult story given the number of references to adult vices and frailties throughout the film.  Even the fundamental basis of Alice\\'s journey through Wonderland owes as much to psychological references as it does childhood fantasies.  The ending of Alice\\'s journey in particular is quite frightening, especially to younger children, embodying all that can be horrible in nightmares.  I remember seeing \"Alice in Wonderland\" as a child, and I remember I thought the ending was scary, and I was relieved when Alice was able to escape back to the normal world.\\n\\nGiven that this film is well over fifty years old, I was disturbed by the smoking caterpillar, if for no other reason than our current knowledge of what smoking does.  The smoke did negatively affect Alice as it made her cough.  Perhaps there is an object lesson for children here.  While some consider many of the references to allude to drugs, I think that is a modern interpretation.  I think eating or drinking items to get larger or smaller was merely a convenient plot device for the author rather than an allegory for drug use.  Remember that this story was first published in 1869.\\n\\nI believe this film is excessively criticized by those who do not like its style and the post-era associations with negative elements that were never intended by the original author and by Disney.\\n\\nFor all these provisos, this film is excellent.  It matches \"Fantasia\" in its surreal visions, and the construct and tying together of the individual stories is superb.  The animation is marvelous as well, readily illustrating the vision of Alice\\'s journey.\\n\\nThis movie is one of those rare Disney films that I am unable to recommend to all viewers.  Whether you will enjoy this film is strictly a matter of personal taste.  If you enjoy animation oriented toward adults, then you may find this animation a treat.  If you are open-minded about the story and consider when the story was written and animated, you may also find that you enjoy it.  I do recommend this movie for slightly older children, perhaps 8 and above, considering some of the disturbing images.  Good luck!'] \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['unique_id | B00008SCFU:fast_and_accurate:j._n._fong', 'asin | B00008SCFU', 'product_name | Kingston 256 MB Secure Digital Card ( SD/256 ): Electronics', 'helpful | ', 'rating | 5.0', 'title | Fast and Accurate', 'date | March 19, 2006', 'reviewer | J. N. Fong', 'reviewer_location | Castro Valley, CA  USA', \"review_text | I received my Kingston 256MB SD card just as advertised.\\nThe unit came in the mail exactly 2 days after I\\nordered. Worked perfectly and I'm very satisfied\"] \n",
      "\n",
      "['unique_id | B0000TVVEA:an_excellent_italian_gift_basket._.,:stewart__l', 'asin | B0000TVVEA', 'product_name | Italian Gift Basket - Classic (3.8 Pound) by igourmet.com: Gourmet Food', 'helpful | 5 of 11', 'rating | 5.0', 'title | An excellent italian gift basket. .,', 'date | November 24, 2005', 'reviewer | stewart  L', 'reviewer_location | flushing ,ny', 'review_text | This italian gift basket,captures the feeling of italy,at it best...When you send this to family or friends,they will definitly,feel the love and warmth,of italy,and is sure to please...5 star recommended..'] \n",
      "\n",
      "['unique_id | B000CN7BNE:fantastically_refreshing!:sn', 'asin | B000CN7BNE', 'product_name | Zico Pure Coconut Water, Passion Fruit with Orange Peel Flavor, 11-Ounce Units (Pack of 12): Grocery', 'helpful | ', 'rating | 4.0', 'title | fantastically refreshing!', 'date | November 18, 2006', 'reviewer | SN', 'reviewer_location | Austin,TX', \"review_text | I bought the natural flavour. Didn't find it very palatable the first time. But the next time was much better and now I am hooked on to it! An acquired taste, indeed! The 11-ounce is difficult to down in one go, though.\"] \n",
      "\n",
      "['unique_id | B00006WNPU:omron_body_fat_analyyzer:nicholas_tompanis', 'asin | B00006WNPU', 'product_name | Omron HBF-306 Body Fat Analyzer: Health and Personal Care', 'helpful | 5 of 6', 'rating | 5.0', 'title | Omron Body Fat Analyyzer', 'date | August 19, 2006', 'reviewer | Nicholas Tompanis', 'reviewer_location | Virginia Beach, VA', \"review_text | I've found the Omron very easy and practical to use.  It gives very good estimates of body fat percentages and BMI (body mass index) which can easily be used to illustrate a personal rate of progress for any dietary or weight training program.  For the money, it would be difficult to find something better\"] \n",
      "\n",
      "['unique_id | B000EX8N3E:outstanding_in_almost_every_way:b._siao', 'asin | B000EX8N3E', \"product_name | Casio Men's G-Shock Atomic Solar Watch # MTG920DA-1: Jewelry and Watches: Casio\", 'helpful | ', 'rating | 5.0', 'title | Outstanding In Almost Every Way', 'date | November 9, 2006', 'reviewer | B. Siao', 'reviewer_location | San Diego, CA', 'review_text | Elegant yet tough, this particular Casio has a metal bracelet and is extremely comfortable to wear.  The display is sharp, clear, and very easy to read.  I only wish it had a thermometer.  The watch is big and looks very macho on your wrist'] \n",
      "\n",
      "['unique_id | B0002D31QU:immediate_results!:a._sanford', 'asin | B0002D31QU', 'product_name | Premier Gentle Spray Anti-Bark Dog Collar: Kitchen and Housewares', 'helpful | 1 of 1', 'rating | 5.0', 'title | Immediate results!', 'date | October 24, 2006', 'reviewer | A. Sanford', 'reviewer_location | Blacksburg, VA USA', \"review_text | My grandma's 10 year old Yorkie just would never shut up.  He barked at our cat, at our parrot, and anything that moved really.  We bought this collar, and it worked perfectly and immediately.  One bark and it sprayed, and he would go running off...and stop barking!  I felt kinda bad for him, but his reaction was hilarious.  I couldn't stop laughing at him, poor thing.  However, he did learn that if the collar wasn't on, he wasn't going to get sprayed, so he would bark\"] \n",
      "\n",
      "['unique_id | B00005UMOQ:good_magazine:david_allen_hazlewood', 'asin | B00005UMOQ', 'product_name | Body + Soul: Magazines', 'helpful | ', 'rating | 5.0', 'title | Good magazine', 'date | November 15, 2006', 'reviewer | David Allen Hazlewood', 'reviewer_location | Tacoma, WA', 'review_text | I was sceptical at first. But after getting a few of the magazines I really got into the way they looked at the Whole Body health and didn\\'t JUST focus on \"body\" exercises like most magazines'] \n",
      "\n",
      "['unique_id | B00008QS9V:it\\'s_a_mighty_wind_a_blowin\\'!:carla_j._dinsmore_\"acoustic_music_junkie\"', 'asin | B00008QS9V', 'product_name | A Mighty Wind: The Album: Music: Various Artists', 'helpful | ', 'rating | 5.0', \"title | It's a Mighty Wind a blowin'!\", 'date | July 5, 2006', 'reviewer | Carla J. Dinsmore \"Acoustic music junkie\"', 'reviewer_location | Wilmington, DE USA', 'review_text | This is a wonderful album, that evokes memories of the 60\\'s folk boom, yet contains original songs. I was amazed at the fantastic harmonies and musical arrangements.\\nAnyone who loves the movie \"A Mighty Wind\" and who loves folk music will fall in love with this album. I know I did'] \n",
      "\n",
      "[\"unique_id | B000CNTDY4:good_series_if_you're_interested_in_rock,_and_metal.:a._bailey\", 'asin | B000CNTDY4', 'product_name | Metal Method Complete Basic Course Level 1 Thru Level 6 on DVD Doug Marks Classic Rock: Musical Instruments', 'helpful | 1 of 1', 'rating | 4.0', \"title | Good Series if you're interested in Rock, and Metal.\", 'date | June 15, 2006', 'reviewer | A. Bailey', 'reviewer_location | New York, NY', 'review_text | The videos are amazing! I would have to say better than amazing. The riffs, and solos that are taught are fun and work well with the level they correspond to, and I feel progresses well with a beginner and beyond at a consistant speed. The only problem is... that there is a grey area that overlaps the term beginner and intermediate so you may have to skip around the lessons to get on track with the course. For instance I decided to learn the riffs and practice the pull offs from the beginner DVDs and go straight into the intermediate levels of the course as the beginner sections where way to easy. But I would have to say if powerful riffs and electrifying solos is your passion then I know of no other course that will get you there quicker while having more fun doing it'] \n",
      "\n",
      "['unique_id | B0007LTJFO:excellent:book_reader_\"sam\"', 'asin | B0007LTJFO', 'product_name | Study Stand Wire Book Stand, 9-3/8\"x6\"x5-3/8\", Chrome Finish FEL10024: Office Products', 'helpful | 1 of 1', 'rating | 5.0', 'title | Excellent', 'date | August 12, 2006', 'reviewer | Book Reader \"Sam\"', 'reviewer_location | Baden, PA United States', \"review_text | This wire stand will hold large books easily and will allow you to turn the pages with ease. I should have bought two of them.\\nExcellent product and it's cheap\"] \n",
      "\n",
      "['unique_id | B00006WS5E:ignore_all_the_bad_press,_this_is_great:harriet_epstein_falkin_\"larry\"', 'asin | B00006WS5E', 'product_name | Brinkmann 810-5000-0 All-In-One Gas Outdoor Cooker: Outdoor Living', 'helpful | ', 'rating | 5.0', 'title | Ignore all the bad press, this is great', 'date | July 5, 2006', 'reviewer | Harriet Epstein Falkin \"larry\"', 'reviewer_location | Los Angeles, CA USA', 'review_text | I use this BBQ with Mesquite lump charcoal and have grilled excellent steaks, and smoked some pork ribs, a chicken, a London Broil and a giant hamburger so far.  No marinade, just a dry rub and on the grill.  The book included has accurate cook times as long as you keep the temperature needle on the \"i\" in Ideal for as much of the cook time as possible, which is not hard to do.  Make sure to \"cure\" it before you use it.  This burns off all the smells and chemicals that are in it from the manufacturing. Cure it using the propane burner.   Easy to assemble too'] \n",
      "\n",
      "['unique_id | B00081I76A:photoshop_cs2_wow!:orvsal', 'asin | B00081I76A', 'product_name | Adobe Photoshop CS2: Software', 'helpful | ', 'rating | 5.0', 'title | Photoshop CS2 WOW!', 'date | November 9, 2006', 'reviewer | OrvSal', 'reviewer_location | Sacramento,Ca', 'review_text | I am amazed what this program can do. It is a big difference from PS 5.5. It is a new learning curve but well worth it. What used to take me hours to do is now an action and done in 2 minutes. Amazon had the best price for me from a source I trust ordering from. It is worth it'] \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['unique_id | B00024J7C6:which_is_the_biggest_monster:man_from_u.k._\"inside_canon\"', 'asin | B00024J7C6', 'product_name | Toilet Monster: Sports andamp; Outdoors', 'helpful | 14 of 22', 'rating | 5.0', 'title | Which is the biggest monster', 'date | January 30, 2006', 'reviewer | Man From U.K. \"Inside Canon\"', 'reviewer_location | MD USA', \"review_text | This monster is great...my old millionaire nan came over recently to tell us that she was about to take us out of her will because we had too much money already, and as luck would have it she had to use the bathroom during that same visit. We heard this scream from the bathroom and when we broke in we found our nan dead on the floor...luckily she hadn't gone to her attorney's office to sign the new will and we got our share after all...thanks toilet monster..you were worth the $15 + $5 shipping we paid for you\"] \n",
      "\n",
      "['unique_id | B000630AR4:make_life_easier:m._i._guerrero_\"sunsad\"', 'asin | B000630AR4', 'product_name | Car Wash Wipes: Tools and Hardware', 'helpful | 1 of 1', 'rating | 5.0', 'title | make life easier', 'date | March 16, 2006', 'reviewer | M. I. Guerrero \"sunsad\"', 'reviewer_location | ', \"review_text | These are great cleaning wipes. Living here on the east coast when it snows and the salt and road dirt get all over the car it's not so easy to get to a car wash right away, pull one of these out and it cleans so well and leaves a great shine. My husband and I use these even after going to a car wash when you see water spots just 1 sheet will clean it all up. I hope they don't stop making them. Lately have not been able to find them in the stores....\"] \n",
      "\n",
      "['unique_id | B0006I5G3M:now_a_clue_mystery_game_with_more_mysteries_to_solve!:melissa_lewis_\"sunflowergirl\"', 'asin | B0006I5G3M', 'product_name | Clue Mysteries: Toys andamp; Games', 'helpful | 7 of 9', 'rating | 4.0', 'title | Now a clue mystery game with more mysteries to solve!', 'date | May 31, 2006', 'reviewer | Melissa Lewis \"Sunflowergirl\"', 'reviewer_location | PA, USA', 'review_text | Excellent product! I really appreciate the option to choose any of the 50 given mystery cases to solve. After playing all 50, you can just start all over again! Fun, fun, fun game! THANKS'] \n",
      "\n",
      "['unique_id | 6301968972:just_as_true_today!:gary_d._thomas_\"brass_crass\"', 'asin | 6301968972', 'product_name | Hucksters: Video: Clark Gable,Deborah Kerr,Sydney Greenstreet,Adolphe Menjou,Ava Gardner,Keenan Wynn,Edward Arnold,Aubrey Mather,Richard Gaines,Frank Albertson,Douglas Fowley,Clinton Sundberg,Gloria Holden,Connie Gilchrist,Kathryn Card,Lillian Bronson,Vera Marshe,Ralph Bunker,Virginia Dale,Jimmy Conlin,Jack Conway', 'helpful | 1 of 1', 'rating | 5.0', 'title | Just as true today!', 'date | August 4, 2000', 'reviewer | Gary D. Thomas \"Brass Crass\"', 'reviewer_location | Studio City, CaliforniA', 'review_text | Picture this: Clark Gable and the whole passle of MGM stars and starlets!  Spins a great story, and all this in 1947, could be today, except they  dressed better. You will not forget the characterizations, especially  Sydney Greenstreet, eat your heart out Eddie Murphy, just a marvy little  film for all ages (God I love cliches!'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for review in pos_reviews:\n",
    "    print(review['review1'], '\\n')\n",
    "    df = dict_to_dataframe(review)\n",
    "    positive_df = pd.concat([positive_df, df])\n",
    "    df.empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for review in neg_reviews:\n",
    "    df = dict_to_dataframe(review)\n",
    "    negative_df = pd.concat([negative_df, df])\n",
    "    df.empty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate descriptive statistics. Pandas Dataframe descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. We will also check whether the number of positive and negative reviews has been preserved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of Positive Reviews (Dataframe): 21972 \n",
      "Number of Negative Reviews (Dataframe): 16576\n"
     ]
    },
    {
     "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>unique_id</th>\n",
       "      <th>asin</th>\n",
       "      <th>product_name</th>\n",
       "      <th>helpful</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer</th>\n",
       "      <th>reviewer_location</th>\n",
       "      <th>review_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "      <td>16576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>15442</td>\n",
       "      <td>10905</td>\n",
       "      <td>10773</td>\n",
       "      <td>908</td>\n",
       "      <td>2</td>\n",
       "      <td>13330</td>\n",
       "      <td>1664</td>\n",
       "      <td>13427</td>\n",
       "      <td>5736</td>\n",
       "      <td>14726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>B0000AFQQU:this_is_a_big,_glossy_brochure_for...</td>\n",
       "      <td>B00099QAQK</td>\n",
       "      <td>TIME [6-month subscription] [with $5 Bonus]: ...</td>\n",
       "      <td></td>\n",
       "      <td>1.0</td>\n",
       "      <td>Disappointed</td>\n",
       "      <td>January 9, 2007</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>This was my first download of this type and a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>2748</td>\n",
       "      <td>10443</td>\n",
       "      <td>79</td>\n",
       "      <td>216</td>\n",
       "      <td>452</td>\n",
       "      <td>3572</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                unique_id         asin  \\\n",
       "count                                               16576        16576   \n",
       "unique                                              15442        10905   \n",
       "top      B0000AFQQU:this_is_a_big,_glossy_brochure_for...   B00099QAQK   \n",
       "freq                                                    4           18   \n",
       "\n",
       "                                             product_name helpful rating  \\\n",
       "count                                               16576   16576  16576   \n",
       "unique                                              10773     908      2   \n",
       "top      TIME [6-month subscription] [with $5 Bonus]: ...            1.0   \n",
       "freq                                                   20    2748  10443   \n",
       "\n",
       "                title              date reviewer reviewer_location  \\\n",
       "count           16576             16576    16576             16576   \n",
       "unique          13330              1664    13427              5736   \n",
       "top      Disappointed   January 9, 2007                              \n",
       "freq               79               216      452              3572   \n",
       "\n",
       "                                              review_text  \n",
       "count                                               16576  \n",
       "unique                                              14726  \n",
       "top      This was my first download of this type and a...  \n",
       "freq                                                   18  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\\nNumber of Positive Reviews (Dataframe):', len(positive_df),\n",
    "      '\\nNumber of Negative Reviews (Dataframe):', len(negative_df))\n",
    "\n",
    "positive_df.describe()\n",
    "negative_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, `unique_id` has the same information as tags `asin`, `title` and `reviewer` so we can remove it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df.drop('unique_id', axis=1, inplace=True)\n",
    "negative_df.drop('unique_id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also add a new column `Class`, which will have the value of either `pos` or `neg`, it is necessary to distinguish between positive and negative reviews after concatenation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_df['Class'] = \"pos\" # positive reviews\n",
    "negative_df['Class'] = \"neg\" # negative reviews\n",
    "\n",
    "reviews = pd.concat([positive_df, negative_df])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what we got:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>asin</th>\n",
       "      <th>product_name</th>\n",
       "      <th>helpful</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>reviewer</th>\n",
       "      <th>reviewer_location</th>\n",
       "      <th>review_text</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B0007QCQA4</td>\n",
       "      <td>adidas Originals Men's Superstar II Basketbal...</td>\n",
       "      <td>0 of 1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>GOOD SNEAKERS</td>\n",
       "      <td>July 15, 2006</td>\n",
       "      <td>Christopher W. Damico \"MACMAN\"</td>\n",
       "      <td>NYC</td>\n",
       "      <td>GOOD LOOKING KICKS IF YOUR KICKIN IT OLD SCHO...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B0002Y2JYY</td>\n",
       "      <td>Elite Metal Aviator Sunglasses with Mirror Le...</td>\n",
       "      <td>3 of 5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Pretty Good</td>\n",
       "      <td>August 13, 2006</td>\n",
       "      <td>Sharon Civile \"Jackbauerfreak\"</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>These sunglasses are all right. They were a l...</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          asin                                       product_name  helpful  \\\n",
       "0   B0007QCQA4   adidas Originals Men's Superstar II Basketbal...   0 of 1   \n",
       "1   B0002Y2JYY   Elite Metal Aviator Sunglasses with Mirror Le...   3 of 5   \n",
       "\n",
       "  rating           title              date                         reviewer  \\\n",
       "0    4.0   GOOD SNEAKERS     July 15, 2006   Christopher W. Damico \"MACMAN\"   \n",
       "1    4.0     Pretty Good   August 13, 2006   Sharon Civile \"Jackbauerfreak\"   \n",
       "\n",
       "   reviewer_location                                        review_text Class  \n",
       "0                NYC   GOOD LOOKING KICKS IF YOUR KICKIN IT OLD SCHO...   pos  \n",
       "1   Philadelphia, PA   These sunglasses are all right. They were a l...   pos  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print the full text of the review (`review_text`) for the text with id 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" GOOD LOOKING KICKS IF YOUR KICKIN IT OLD SCHOOL LIKE ME. AND COMFORTABLE. AND RELATIVELY CHEAP. I'LL ALWAYS KEEP A PAIR OF STAN SMITH'S AROUND FOR WEEKENDS\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.iloc[0, 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our work with the formation of datasets is complete, let's move on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">NLTK (Natural Language Toolkit)</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK is a leading platform for building Python programs to work with human language data. \n",
    "\n",
    "It provides easy-to-use interfaces and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.\n",
    "\n",
    "**Note:** \n",
    "- **`word_tokenize`** - extract words from the text;\n",
    "- **`sent_tokenize`** - extract sentences from the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tReview ID4 - Words:\n",
      " ['Very', 'nice', 'basic', 'clothing', '.', 'I', 'think', 'the', 'size', 'is', 'fine', '.', 'I', 'really', 'like', 'being', 'able', 'to', 'find', 'these', 'shades', 'of', 'green', ',', 'though', 'I', 'have', 'decided', 'the', 'lighter', 'shade', 'is', 'really', 'a', 'feminine', 'color', '.', 'This', 'is', 'the', 'only', 'brand', 'that', 'I', 'can', 'find', 'these', 'muted', 'greens'] \n",
      "\n",
      "\tReview ID4 - Sentences:\n",
      " [' Very nice basic clothing.', 'I think the size is fine.', 'I really like being able to find these shades of green, though I have decided the lighter shade is really a feminine color.', 'This is the only brand that I can find these muted greens']\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize, sent_tokenize, RegexpTokenizer\n",
    "\n",
    "def word_sent_tokenize(sent):\n",
    "    return word_tokenize(sent), sent_tokenize(sent)\n",
    "\n",
    "# also at this method we will bring the words to lowercase to make it easier to work with them\n",
    "def regex_tokenizer(sent):\n",
    "    return RegexpTokenizer(r'\\w+').tokenize(sent.lower())\n",
    "\n",
    "words, sents = word_sent_tokenize(reviews.iloc[3, 8])\n",
    "print(\"\\tReview ID4 - Words:\\n\", words, \"\\n\\n\\tReview ID4 - Sentences:\\n\", sents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\">StopWords Elimination</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will take stopwords from the NLTK library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "STOP_WORDS = set(stopwords.words(\"english\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================== STOP WORDS ========================\n",
      " {'in', 'about', 'once', 'hers', \"hadn't\", \"don't\", 'y', 'by', 'after', 'below', 'wasn', 'but', 'have', 'mightn', \"it's\", 'ourselves', 'an', \"hasn't\", \"won't\", 'themselves', 't', 'off', 'can', 'them', 'aren', 'doing', \"wouldn't\", 'our', \"you're\", 'haven', 'further', 'yourself', 'and', 'her', \"mustn't\", 'should', 'll', 'you', 'what', 'all', 'ours', 'until', 'his', 'from', 'i', 'for', \"that'll\", 'under', 'too', 'didn', 'ma', 'she', 'the', 'or', 'to', 'doesn', 'him', \"shan't\", \"aren't\", 's', \"mightn't\", 'each', 'couldn', 'm', 'are', \"doesn't\", \"didn't\", \"wasn't\", 'we', 'while', 'between', 'than', 'out', 'whom', 'before', 'd', 'isn', 'myself', 'yours', 'here', 'of', 'was', \"you've\", 'will', 'most', 'such', 've', 'into', 'your', 'because', 'itself', 'through', \"weren't\", 'its', 'been', 'theirs', 'it', 'their', 'needn', 'these', 'ain', 'did', 'me', 'only', 'then', 'up', 'hasn', 'does', 'do', 'no', 'against', 'won', 'not', 'were', \"haven't\", 'again', 'where', 'there', \"needn't\", 'nor', 'mustn', 'who', 'having', 'as', 'with', 'both', 'he', 'above', 'weren', 'more', 'a', 'over', 'why', 'shouldn', 'which', 'has', 'had', 'himself', 'just', 'they', \"should've\", 'at', \"couldn't\", 'when', 'so', 're', 'yourselves', 'some', 'if', 'shan', \"she's\", \"you'd\", 'o', 'few', 'be', 'same', 'now', 'that', \"isn't\", 'other', 'on', 'don', \"you'll\", 'very', 'this', 'own', 'hadn', \"shouldn't\", 'my', 'am', 'those', 'how', 'herself', 'any', 'being', 'during', 'is', 'wouldn', 'down'} \n",
      "\n",
      "==================== WORDS FROM REVIEW 4 ====================\n",
      " ['Very', 'nice', 'basic', 'clothing', '.', 'I', 'think', 'the', 'size', 'is', 'fine', '.', 'I', 'really', 'like', 'being', 'able', 'to', 'find', 'these', 'shades', 'of', 'green', ',', 'though', 'I', 'have', 'decided', 'the', 'lighter', 'shade', 'is', 'really', 'a', 'feminine', 'color', '.', 'This', 'is', 'the', 'only', 'brand', 'that', 'I', 'can', 'find', 'these', 'muted', 'greens'] \n",
      "\n",
      "================== FILTERED WORDS REVIEW 4 ==================\n",
      " ['nice', 'basic', 'clothing', '.', 'think', 'size', 'fine', '.', 'really', 'like', 'able', 'find', 'shades', 'green', ',', 'though', 'decided', 'lighter', 'shade', 'really', 'feminine', 'color', '.', 'brand', 'find', 'muted', 'greens']\n"
     ]
    }
   ],
   "source": [
    "def stopwords_elimination(stop_words, sent):\n",
    "     return [w.lower() for w in sent if w.lower() not in stop_words]\n",
    "\n",
    "\n",
    "filtered_words = stopwords_elimination(STOP_WORDS, words)\n",
    "\n",
    "print(\"\\n======================== STOP WORDS ========================\\n\", STOP_WORDS,\n",
    "      \"\\n\\n==================== WORDS FROM REVIEW 4 ====================\\n\", words,\n",
    "      \"\\n\\n================== FILTERED WORDS REVIEW 4 ==================\\n\", filtered_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 align=\"center\">StopWords WordCloud Visualization</h4>\n",
    "\n",
    "Next, let's visualize using the WordCloud library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "wordcloud = WordCloud().generate(' '.join(STOP_WORDS))\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\">Stemming</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For grammatical purposes, documents are going to use various forms of a word, such as `running`, `runs`, `runned`. In certain cases, it appears that it would be useful for a search for one of these words to return documents that contain another word in the set. Both stemming and lemmatization have the aim of reducing a word's inflectional and often derivationally related forms to a common base form.\n",
    "\n",
    "**Stamming** is the process of shortening a word to the base by discarding auxiliary parts, such as endings or suffixes. The results of stemming are sometimes very similar to determining the root of a word, but its algorithms are based on other principles. Therefore, the word after processing by the algorithm of steming (stematization) may differ from the morphological root of the word.\n",
    "\n",
    "*Example*: \n",
    "\n",
    "*`running`*, *`runs`*, *`runned`* → **`run`**\n",
    "\n",
    "We would use `PorterStemmer` from `nltk.stem` for our stemming:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['nice', 'basic', 'cloth', '.', 'think', 'size', 'fine', '.', 'realli', 'like', 'abl', 'find', 'shade', 'green', ',', 'though', 'decid', 'lighter', 'shade', 'realli', 'feminin', 'color', '.', 'brand', 'find', 'mute', 'green']\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "def stem_words(words):\n",
    "    return [PorterStemmer().stem(word) for word in words]\n",
    "\n",
    "stemmed_words = stem_words(filtered_words)\n",
    "print(stemmed_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\">Lemmatizing</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<cite><b>Lemmatization</b>, unlike <b>Stemming</b>, reduces the inflected words properly ensuring that the root word belongs to the language. In Lemmatization root word is called <b>Lemma</b>. A lemma is the canonical form, dictionary form, or citation form of a set of words.</cite>\n",
    "\n",
    "At lematization the word loses inflectional endings and finds the basic form. For example, `[bats — bat]`, `[feet — foot]`, `[mice — mouse]`.\n",
    "\n",
    "Python `NLTK` provides `WordNet Lemmatizer` that uses the `WordNet Database` to lookup lemmas of words.\n",
    "\n",
    "Let's connect the library and see how it works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['nice', 'basic', 'clothing', '.', 'think', 'size', 'fine', '.', 'really', 'like', 'able', 'find', 'shade', 'green', ',', 'though', 'decided', 'lighter', 'shade', 'really', 'feminine', 'color', '.', 'brand', 'find', 'muted', 'green']\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "def lemmatize_words(words):\n",
    "    return [WordNetLemmatizer().lemmatize(word) for word in words]\n",
    "\n",
    "lemmatized_words = lemmatize_words(filtered_words)\n",
    "print(lemmatized_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">Processing of All Reviews</h2>\n",
    "\n",
    "Now let's start processing all reviews using the methods described above. Lematization is quite a time-consuming procedure, so you have to wait:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review without punctuation:  ['these', 'sunglasses', 'are', 'all', 'right', 'they', 'were', 'a', 'little', 'crooked', 'but', 'still', 'cool']\n",
      "\n",
      "\n",
      "Review after stopwords elimination:  ['sunglasses', 'right', 'little', 'crooked', 'still', 'cool']\n",
      "\n",
      "\n",
      "Review after lemmatizing:  ['sunglass', 'right', 'little', 'crooked', 'still', 'cool']\n"
     ]
    }
   ],
   "source": [
    "# Use regex_tokenizer to get rid of punctuation\n",
    "words_list = [regex_tokenizer(item) for item in list(reviews['review_text'])]\n",
    "print(\"Review without punctuation: \", words_list[1])\n",
    "\n",
    "# Eliminate stopwords\n",
    "words_list = [stopwords_elimination(STOP_WORDS, word) for word in words_list]\n",
    "print(\"\\n\\nReview after stopwords elimination: \", words_list[1])\n",
    "\n",
    "# Lemmatizing\n",
    "words_list = [lemmatize_words(word) for word in words_list]\n",
    "print(\"\\n\\nReview after lemmatizing: \", words_list[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\">Transition from multidimensional array to one-dimensional</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most popular words:  [('one', 19296), ('like', 14891), ('time', 12019), ('get', 11957), ('would', 11771), ('great', 11248), ('good', 10940), ('product', 8722), ('use', 8499), ('well', 8117), ('even', 7809), ('also', 7785), ('work', 7738), ('really', 7716), ('make', 7519), ('game', 7505), ('much', 7093), ('year', 6953), ('first', 6669), ('love', 6619), ('movie', 6592), ('book', 6230), ('thing', 5957), ('bought', 5626), ('better', 5435), ('buy', 5355), ('little', 5317), ('way', 5272), ('back', 5270), ('go', 5207), ('2', 5163), ('could', 5052), ('two', 4971), ('still', 4941), ('look', 4918), ('new', 4819), ('day', 4726), ('best', 4645), ('used', 4609), ('take', 4507), ('want', 4506), ('got', 4488), ('film', 4481), ('many', 4479), ('problem', 4436), ('think', 4368), ('camera', 4355), ('see', 4305), ('know', 4276), ('never', 4241), ('say', 4201), ('find', 4137), ('need', 4079), ('magazine', 4065), ('old', 4049), ('made', 4009), ('quality', 3977), ('people', 3946), ('come', 3879), ('price', 3834), ('easy', 3809), ('lot', 3733), ('read', 3687), ('3', 3522), ('5', 3466), ('review', 3451), ('put', 3448), ('money', 3388), ('every', 3387), ('month', 3367), ('another', 3321), ('give', 3292), ('play', 3282), ('1', 3268), ('right', 3225), ('using', 3175), ('set', 3170), ('since', 3163), ('recommend', 3146), ('nice', 3144), ('however', 3143), ('found', 3109), ('long', 3087), ('enough', 3018), ('ever', 3016), ('amazon', 3015), ('story', 2956), ('without', 2948), ('keep', 2945), ('something', 2915), ('picture', 2873), ('around', 2865), ('life', 2842), ('watch', 2840), ('bad', 2833), ('star', 2816), ('item', 2770), ('sound', 2763), ('though', 2753), ('feel', 2753)]\n",
      "\n",
      "Total number of unique words:  64209\n"
     ]
    }
   ],
   "source": [
    "from nltk import FreqDist\n",
    "\n",
    "all_words = np.concatenate(words_list)\n",
    "\n",
    "all_words_freq = FreqDist(all_words) # count the number of times that each outcome of an experiment occurs\n",
    "    \n",
    "print(\"Most popular words: \", all_words_freq.most_common(100))\n",
    "print(\"\\nTotal number of unique words: \", len(all_words_freq.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\">Visualization of the most used words</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(\n",
    "    background_color=\"black\",\n",
    "    width=1000,\n",
    "    height=500,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "wordcloud.generate_from_frequencies(all_words_freq)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "count, words = [], []\n",
    "for w in all_words_freq.most_common(10):\n",
    "    words.append(w[0])\n",
    "    count.append(w[1])\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.barplot(x=words,y=count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we still have a lot of words that are not important in the context (`one`, `get`), this needs to be corrected, so we are writing new methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\">Irrelevent Words Elimination</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will delete all words with a length of less than 3 and numbers. I don't know why, but for one cycle, it doesn't delete all superfluous words therefore it was necessary to put for this purpose a `while` cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from re import match \n",
    "\n",
    "def irr_words_elim(words):\n",
    "    while True:\n",
    "        ready = True\n",
    "        \n",
    "        for word in words:\n",
    "            if word.isdigit() or len(word) <= 3:\n",
    "                ready = False\n",
    "                words.remove(word)\n",
    "        \n",
    "        if ready: break\n",
    "        \n",
    "    return words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now actually delete all irrelevant words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of unique words: 59837\n"
     ]
    }
   ],
   "source": [
    "for review in words_list: \n",
    "    irr_words_elim(review)\n",
    "\n",
    "all_words = np.concatenate(words_list)\n",
    "all_words_freq = FreqDist(all_words)\n",
    "\n",
    "print(\"\\nTotal number of unique words:\", len(all_words_freq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\">Visualization of the most used words after processing</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's visualize using the WordCloud library. Also add a little style and make a special mask. So that we can add our own mask for WordCloud, we need to write a method that will allow us to do this, but before that we import the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "from urllib.request import urlopen\n",
    "from wordcloud import ImageColorGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we load simple mask as numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_mask(mask_url):\n",
    "    with urlopen(mask_url) as response:\n",
    "        mask = np.asarray(bytearray(response.read()), dtype=\"uint8\")\n",
    "        mask = cv2.imdecode(mask, cv2.IMREAD_COLOR)\n",
    "        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)\n",
    "        \n",
    "    return mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we create and show default wordcloud. In particular, we can color the wordcloud using ImageColorGenerator's image-based coloring technique. It uses the average color of the region occupied by the word in a source image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = load_mask(\"https://i.imgur.com/UVe6Nas.png\")\n",
    "\n",
    "wordcloud = WordCloud(\n",
    "    background_color=\"white\", \n",
    "    mask=mask,\n",
    "    max_words=500,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "wordcloud.generate_from_frequencies(all_words_freq)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top 20 Most Common Words'}, xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.set()\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_facecolor('xkcd:white')\n",
    "\n",
    "all_words_freq.plot(20, color=\"green\", title=\"Top 20 Most Common Words\", linewidth=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">Data Preparation For Training</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create our training documents in the form of a tuple. Why tuple? The tuple is immutable, ie they have a fixed size in nature, while the lists are dynamic, they can be changed. Now we don't need to add or remove items, so tuples are a great option, and tuples are faster than lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(['thought', 'purse', 'cute', 'upon', 'receiving', 'disappointed', 'purse', 'black', 'silver', 'buckle', 'stitching', 'gold', 'color', 'well', 'rest', 'purse', 'also', 'tried', 'return', 'able', 'hold', 'anybody', 'help'], 'neg') \n",
      "\n",
      "Number of documents: 38544\n"
     ]
    }
   ],
   "source": [
    "from random import shuffle\n",
    "\n",
    "def create_doc(reviews, words_list):\n",
    "    category = list(reviews['Class'])\n",
    "    docs = [(words_list[word], category[word]) for word in range(len(words_list)) if words_list[word]]\n",
    "    shuffle(docs)\n",
    "    return docs\n",
    "\n",
    "docs = create_doc(reviews, words_list)\n",
    "print(docs[1], \"\\n\\nNumber of documents:\", len(docs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In next part we also need to work with our data, which we have processed here, so let's `pickle` them.\n",
    "\n",
    "`pickle`  - module, which implements a powerful algorithm for serializing and deserializing Python objects. \"*Pickling*\" is the process of converting a Python object to a stream of bytes, and \"*unpickling*\" is the reverse operation, which converts a stream of bytes back to a Python object. Since a stream of bytes can be easily written to a file, the pickle module is widely used to store and load complex objects in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"documents.pql\", \"wb\") as ms:   # Pickling\n",
    "    pickle.dump(docs, ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will create a method **`find_matches`** that will return the dictionary with words as key and the presence in our dictionary of most common words (`all_words_freq`) as a value of `True` or `False`, an additional parameter will represent a number of most frequently repeated words we want to use (default - 50)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'like': True, 'time': True, 'would': False, 'great': False, 'good': True, 'product': False, 'well': False, 'even': False, 'also': False, 'work': False, 'really': False, 'make': False, 'game': False, 'much': False, 'year': False, 'first': False, 'love': False, 'movie': False, 'book': False, 'thing': False, 'bought': False, 'better': False, 'little': False, 'back': False, 'could': False, 'still': False, 'look': False, 'best': False, 'used': False, 'take': False, 'want': False, 'film': True, 'many': False, 'problem': False, 'think': True, 'camera': False, 'know': False, 'never': True, 'find': False, 'need': False, 'magazine': False, 'made': False, 'quality': False, 'people': False, 'come': False, 'price': False, 'easy': False, 'read': False, 'review': False, 'money': False}\n"
     ]
    }
   ],
   "source": [
    "def find_matches(doc, all_words, num_freq=50):\n",
    "    word_matches = [word[0] for word in all_words.most_common(num_freq)]\n",
    "    words = regex_tokenizer(doc)\n",
    "\n",
    "    matches = {}\n",
    "    for word in word_matches:\n",
    "        matches[word] = (word in words)\n",
    "\n",
    "    return matches\n",
    "\n",
    "print(find_matches(\"All is amazing. I never seen anything like this. It's a film that leads you to think about time, about yourself and your problems and plans in your life. This was not good, but awesome.\", all_words_freq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`create_matches_set`** creates for each of our documents, which we generated in the **`create_doc`** method, a map of occurrences of popular words in a sentence using the **`find_matches`** method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_matches_set(docs, all_words, num_freq=100):\n",
    "    matches = []\n",
    "    for doc, state in docs:\n",
    "        match = find_matches(' '.join(doc), all_words, num_freq)\n",
    "        matches.append((match, state))\n",
    "    return matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "({'like': False, 'time': False, 'would': False, 'great': False, 'good': False, 'product': False, 'well': True, 'even': False, 'also': True, 'work': False, 'really': False, 'make': False, 'game': False, 'much': False, 'year': False, 'first': False, 'love': False, 'movie': False, 'book': False, 'thing': False, 'bought': False, 'better': False, 'little': False, 'back': False, 'could': False, 'still': False, 'look': False, 'best': False, 'used': False, 'take': False, 'want': False, 'film': False, 'many': False, 'problem': False, 'think': False, 'camera': False, 'know': False, 'never': False, 'find': False, 'need': False, 'magazine': False, 'made': False, 'quality': False, 'people': False, 'come': False, 'price': False, 'easy': False, 'read': False, 'review': False, 'money': False, 'every': False, 'month': False, 'another': False, 'give': False, 'play': False, 'right': False, 'using': False, 'since': False, 'recommend': False, 'nice': False, 'however': False, 'found': False, 'long': False, 'enough': False, 'ever': False, 'amazon': False, 'story': False, 'without': False, 'keep': False, 'something': False, 'picture': False, 'around': False, 'life': False, 'watch': False, 'star': False, 'item': False, 'sound': False, 'though': False, 'feel': False, 'song': False, 'small': False, 'character': False, 'battery': False, 'last': False, 'going': False, 'music': False, 'hard': False, 'looking': False, 'different': False, 'part': False, 'worth': False, 'phone': False, 'album': False, 'actually': False, 'version': False, 'thought': True, 'light': False, 'always': False, 'week': False, 'pretty': False}, 'neg')\n"
     ]
    }
   ],
   "source": [
    "matches_set = create_matches_set(docs[:10], all_words_freq)\n",
    "\n",
    "print(matches_set[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">Test Training</h2>\n",
    "\n",
    "We will now split the data set in order to perform unbiased model evaluation and identify underfitting or overfitting.\n",
    "\n",
    "To do this, we will use `scikit-learn` or `sklearn` module. It has many packages for data science and machine learning, but for now we will focus on the `model_selection` package, in particular the `train_test_split()` function.\n",
    "\n",
    "`train_test_split` - split arrays into random train and test subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "({'like': False, 'time': False, 'would': False, 'great': False, 'good': False, 'product': False, 'well': False, 'even': False, 'also': False, 'work': False, 'really': False, 'make': False, 'game': False, 'much': False, 'year': False, 'first': False, 'love': False, 'movie': False, 'book': True, 'thing': False, 'bought': True, 'better': False, 'little': False, 'back': False, 'could': False, 'still': False, 'look': False, 'best': False, 'used': True, 'take': False, 'want': False, 'film': False, 'many': False, 'problem': False, 'think': False, 'camera': False, 'know': False, 'never': False, 'find': False, 'need': False, 'magazine': False, 'made': False, 'quality': False, 'people': False, 'come': False, 'price': False, 'easy': False, 'read': False, 'review': False, 'money': False, 'every': False, 'month': False, 'another': False, 'give': False, 'play': False, 'right': False, 'using': False, 'since': False, 'recommend': False, 'nice': False, 'however': False, 'found': False, 'long': False, 'enough': False, 'ever': False, 'amazon': False, 'story': False, 'without': False, 'keep': False, 'something': False, 'picture': False, 'around': False, 'life': False, 'watch': False, 'star': False, 'item': False, 'sound': False, 'though': False, 'feel': False, 'song': False, 'small': False, 'character': False, 'battery': False, 'last': False, 'going': False, 'music': False, 'hard': False, 'looking': False, 'different': False, 'part': False, 'worth': False, 'phone': False, 'album': False, 'actually': False, 'version': False, 'thought': False, 'light': False, 'always': False, 'week': False, 'pretty': False}, 'neg')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_set, test_set = train_test_split(matches_set, random_state=42)\n",
    "print(train_set[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will save our DataFrame and `matches_set` in csv fortmat, because it took too long to generate, we will use `pickle` for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"matches_set.pql\", \"wb\") as ms:   # Pickling\n",
    "    pickle.dump(matches_set, ms)\n",
    "\n",
    "reviews.to_csv(\"reviews.csv\", sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "({'like': False, 'time': False, 'would': False, 'great': True, 'good': False, 'product': True, 'well': True, 'even': False, 'also': False, 'work': False, 'really': False, 'make': False, 'game': False, 'much': False, 'year': False, 'first': False, 'love': False, 'movie': False, 'book': False, 'thing': False, 'bought': False, 'better': True, 'little': False, 'back': False, 'could': False, 'still': False, 'look': False, 'best': False, 'used': False, 'take': False, 'want': False, 'film': False, 'many': False, 'problem': False, 'think': False, 'camera': False, 'know': False, 'never': False, 'find': False, 'need': False, 'magazine': False, 'made': False, 'quality': False, 'people': False, 'come': False, 'price': False, 'easy': False, 'read': False, 'review': False, 'money': False, 'every': False, 'month': False, 'another': False, 'give': False, 'play': False, 'right': True, 'using': False, 'since': False, 'recommend': False, 'nice': False, 'however': False, 'found': False, 'long': False, 'enough': True, 'ever': False, 'amazon': False, 'story': False, 'without': False, 'keep': False, 'something': False, 'picture': False, 'around': False, 'life': False, 'watch': False, 'star': False, 'item': False, 'sound': False, 'though': False, 'feel': False, 'song': False, 'small': False, 'character': False, 'battery': False, 'last': False, 'going': True, 'music': False, 'hard': False, 'looking': False, 'different': False, 'part': False, 'worth': False, 'phone': False, 'album': False, 'actually': False, 'version': False, 'thought': False, 'light': False, 'always': False, 'week': False, 'pretty': False}, 'neg')\n"
     ]
    }
   ],
   "source": [
    "with open(\"matches_set.pql\", \"rb\") as ms:   # Unpickling\n",
    "    r = pickle.load(ms)\n",
    "print(r[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">Classifier Creation (Naive Bayes Classification)</h2>\n",
    "\n",
    "Let's use a quick approach to Naive Bayesian classification to see if our pre-processing data can give us good results to continue further more complex models. To begin with, let's expand our `matches_set` to process all reviews (may take some time):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches_set = create_matches_set(docs, all_words_freq, 2500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good, now train and test it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Matches Set: 38544 \n",
      "Length of Train Set: 25824\n"
     ]
    }
   ],
   "source": [
    "train_set, test_set = train_test_split(matches_set, test_size=0.33, random_state=42)\n",
    "print(\"Length of Matches Set:\", len(matches_set), \"\\nLength of Train Set:\", len(train_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will finally use Naive Bayes Classification and see what accuracy in our model, and what are the most informative features in it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk.classify.util\n",
    "from nltk.classify import NaiveBayesClassifier\n",
    "\n",
    "def naive_bayes_model(train_set, test_set):\n",
    "    classifier = NaiveBayesClassifier.train(train_set) \n",
    "    informative = classifier.show_most_informative_features(10)\n",
    "    accuracy = nltk.classify.accuracy(classifier, test_set) * 100\n",
    "    return classifier, informative, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "                  refund = True              neg : pos    =     25.4 : 1.0\n",
      "               delicious = True              pos : neg    =     12.9 : 1.0\n",
      "                   waste = True              neg : pos    =     11.7 : 1.0\n",
      "                   worst = True              neg : pos    =     11.5 : 1.0\n",
      "               worthless = True              neg : pos    =     11.4 : 1.0\n",
      "                 cheaply = True              neg : pos    =     10.4 : 1.0\n",
      "              refreshing = True              pos : neg    =      9.6 : 1.0\n",
      "               contacted = True              neg : pos    =      9.5 : 1.0\n",
      "                horrible = True              neg : pos    =      9.3 : 1.0\n",
      "                 garbage = True              neg : pos    =      9.3 : 1.0\n",
      "None\n",
      "\n",
      "Classifier Accuracy: 81.26%\n"
     ]
    }
   ],
   "source": [
    "classifier, informative, accuracy = naive_bayes_model(train_set, test_set)\n",
    "print(informative)\n",
    "print(\"\\nClassifier Accuracy:\", str(round(accuracy, 2)) + \"%\") # predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 80-85% accuracy in our classifier, which is a good sign to move on!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 align=\"center\">Testing Classifier</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To properly test our input sentence, it must go through all stages of processing - tokenize, lemmatize and stop_words, irrelevant_words elimination. Let's write a method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def words_preparation(sent):\n",
    "    words = regex_tokenizer(sent)                    # remove punctuation\n",
    "    words = stopwords_elimination(STOP_WORDS, words) # eliminate stopwords\n",
    "    words = lemmatize_words(words)                   # lemmatizing\n",
    "    words = irr_words_elim(words)                    # irrelevent words elimination\n",
    "    print(\"Sentence after processing:\", words)\n",
    "    return \" \".join(words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And test it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sentence: One of the best films of all time, an absolute masterpiece. The Godfather is arguably the best gangster drama as well as setting the standard for cinema.\n",
      "Sentence after processing: ['best', 'film', 'time', 'absolute', 'masterpiece', 'godfather', 'arguably', 'best', 'gangster', 'drama', 'well', 'setting', 'standard', 'cinema']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'pos'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sent = \"One of the best films of all time, an absolute masterpiece. The Godfather is arguably the best gangster drama as well as setting the standard for cinema.\"\n",
    "print(\"Test sentence:\", test_sent)\n",
    "\n",
    "test_sent = find_matches(words_preparation(test_sent), all_words_freq, 2500)\n",
    "classifier.classify(test_sent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first test is passed, let's use less obvious words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sentence: This book is quite readable ..\n",
      "Sentence after processing: ['book', 'quite', 'readable']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'pos'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sent = \"This book is quite readable ..\"\n",
    "print(\"Test sentence:\", test_sent)\n",
    "\n",
    "test_sent = find_matches(words_preparation(test_sent), all_words_freq, 2500)\n",
    "classifier.classify(test_sent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here everything is correct. Let's check the sentences with a negative mood:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sentence: The most entertaining part of this entire movie was reading these hilariously bad reviews. It was SO boring and hard to watch.\n",
      "Sentence after processing: ['entertaining', 'part', 'entire', 'movie', 'reading', 'hilariously', 'review', 'boring', 'hard', 'watch']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'neg'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sent = \"The most entertaining part of this entire movie was reading these hilariously bad reviews. It was SO boring and hard to watch.\"\n",
    "print(\"Test sentence:\", test_sent)\n",
    "\n",
    "test_sent = find_matches(words_preparation(test_sent), all_words_freq, 2500)\n",
    "classifier.classify(test_sent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working! That's all here, next we will try to apply a different approach than this naive way, we will use another classifiers and LSTM, which takes into account the order of words in the sentence to get better accuracy."
   ]
  }
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