ImdbReviews.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web Scraping using BeautifulSoup on Game of thrones series reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us perform web scrapping of reviews from Imdb of Game of thrones. <br> The reviews can be accessed at https://www.imdb.com/title/tt0944947/reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![gameofthrones](https://i2.wp.com/www.deteched.com/wp-content/uploads/2017/07/game-of-thrones-logo-wallpaper-hd.jpg)>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will first scrape the reviews and then using NLP and machine learning perform sentiment analysis on it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Importing the required libraries and extracting the Movie reviews and their ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I have saved the reviews on a html page \n",
    "soup = BeautifulSoup(open(\"1.html\",encoding=\"utf8\"), \"html.parser\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us take a look at the html structure "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bs4.element.ResultSet'>\n",
      "1124\n"
     ]
    }
   ],
   "source": [
    "got_containers = soup.find_all('div' , class_ = 'review-container')\n",
    "print(type(got_containers))\n",
    "print(len(got_containers))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see there are 1124 containers consisting of the reviews and the ratings, let us try to extract this information!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" Finally, a Show that stays true to it's written roots\\n\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_got = got_containers[0]\n",
    "first_got.a.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By carefully looking at the html structure we try to access the reviews, Let us try to find the ratings of the reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n\\n\\n\\n\\n9/10\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating = first_got.span.text\n",
    "rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We are able to access the elements, this can be used in an iterator to obtain all the reviews and the ratings. <br> We can see that the reviews and ratings require cleaning which we will deal later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lists to store the scraped data in\n",
    "reviews = []\n",
    "ratings = []\n",
    "\n",
    "# Extract data from individual movie container\n",
    "for container in got_containers:\n",
    "    \n",
    "    #review\n",
    "    review = container.a.text\n",
    "    reviews.append(review)\n",
    "    \n",
    "    #rating\n",
    "    rating = container.span.text\n",
    "    ratings.append(rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us put all the ratings and reviews in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1124 entries, 0 to 1123\n",
      "Data columns (total 2 columns):\n",
      "Review    1124 non-null object\n",
      "Rating    1124 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 17.6+ KB\n",
      "None\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>Review</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finally, a Show that stays true to it's writt...</td>\n",
       "      <td>\\n\\n\\n\\n\\n\\n9/10\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Excellent adaptation.\\n</td>\n",
       "      <td>\\n\\n\\n\\n\\n\\n10/10\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>One of the most stunning shows on television\\n</td>\n",
       "      <td>\\n\\n\\n\\n\\n\\n10/10\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lord of the Rings - I think not!\\n</td>\n",
       "      <td>\\n\\n\\n\\n\\n\\n10/10\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fantasy has never seemed so real\\n</td>\n",
       "      <td>\\n\\n\\n\\n\\n\\n10/10\\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review               Rating\n",
       "0   Finally, a Show that stays true to it's writt...   \\n\\n\\n\\n\\n\\n9/10\\n\n",
       "1                            Excellent adaptation.\\n  \\n\\n\\n\\n\\n\\n10/10\\n\n",
       "2     One of the most stunning shows on television\\n  \\n\\n\\n\\n\\n\\n10/10\\n\n",
       "3                 Lord of the Rings - I think not!\\n  \\n\\n\\n\\n\\n\\n10/10\\n\n",
       "4                 Fantasy has never seemed so real\\n  \\n\\n\\n\\n\\n\\n10/10\\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "test_df = pd.DataFrame({'Review': reviews,'Rating': ratings})\n",
    "print(test_df.info())\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And voila we have scrapped all the reviews and the ratings <br> Let us perform cleaning on the reviews and the ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using regular expression to replace character with NaN value as some reviews\n",
    "import re\n",
    "def split_it(rating):\n",
    "    return re.sub('[a-zA-Z]+','NaN', rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing next line seperator with NaN values and then keeping only numeric values\n",
    "test_df.loc[:, 'Rating'] = test_df['Rating'].str[6:8]\n",
    "test_df['Rating'] = test_df['Rating'].apply(split_it)\n",
    "test_df.loc[:, 'Review'] = test_df['Review'].str.replace('\\n', '')\n",
    "test_df.loc[:, 'Rating'] = test_df['Rating'].str.replace('/', '')\n",
    "test_df.loc[:, 'Rating'] = test_df['Rating'].str.replace('-', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = test_df[test_df.Rating.str.contains(\"NaN\") == False]\n",
    "test_df['Rating'] = test_df['Rating'].apply(pd.to_numeric)"
   ]
  },
  {
   "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>Review</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finally, a Show that stays true to it's writt...</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Excellent adaptation.</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>One of the most stunning shows on television</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lord of the Rings - I think not!</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fantasy has never seemed so real</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  Rating\n",
       "0   Finally, a Show that stays true to it's writt...     9.0\n",
       "1                              Excellent adaptation.    10.0\n",
       "2       One of the most stunning shows on television    10.0\n",
       "3                   Lord of the Rings - I think not!    10.0\n",
       "4                   Fantasy has never seemed so real    10.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see that we have cleaned the Ratings and the Reviews <br> Let us perform Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_df = test_df[test_df.Rating <= 10]\n",
    "\n",
    "plt.figure(figsize=(14,5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.countplot(test_df['Rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1065.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.071362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.015090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rating\n",
       "count  1065.000000\n",
       "mean      8.071362\n",
       "std       3.015090\n",
       "min       1.000000\n",
       "25%       7.000000\n",
       "50%      10.000000\n",
       "75%      10.000000\n",
       "max      10.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us look at the descriptions\n",
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see there are 1065 reviews, with the mean rating of 8 and most of the ratings are 10. <br> Let us look if there is a relation between a review length and a Rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KwuvU/rlcLtv1j0AGAADkdrvl8Xjkcrnk8XjUv39/0yWFldvt1oUXXihJuvDCC23XPwIZAACQdGIUadiwYbYbPQoHv9+vDz74QJJ06NAhnrIEAAD25Ha7VVJSYrvRo3DgKUsAAADDysrK1NraKklqbW3lKUsAAIBoGz16dIfjMWPGGKrkzAhkAAAAhhHIAACA47388ssdjrdu3WqokjMjkAEAAMfLzc1VQkKCJCkhIYGFYQEAAKLN6/UqLu5E7ImPj7fd0h4EMgAA4Hh2X/g2wXQBAAAA0eD1elVVVWW70TGJETIAAADjIhbI9uzZo8LCQknSgQMHdNttt6mgoEDz589Xe3u7JOk3v/mNbrnlFk2ePFnl5eWRKgUAAEA+n0/l5eW2W6VfilAgW758uebMmaPm5mZJ0uLFizV9+nQ9/fTTsixLmzZt0t69e/Xqq6/qj3/8o5YsWaKf/vSnkSgFAABAfr9fpaWlsixLpaWlPWMvy6ysLJWUlASP9+7dqxEjRkg6sTLuK6+8ol27dmnUqFFyuVy68MIL1dbWptra2kiUAwAAeji772UZkZv68/LydPDgweCxZVlyuVySpNTUVNXX16uhoUEZGRnBz5x8PTMz81Pbbm5uVkVFRSTKBgAADvXiiy8qEAhIkgKBgDZs2CCPx2O4qn+LylOWJ9f9kKTGxkb16dNHaWlpamxs7PB6enp6l20lJycrJycnInUCAABnysvL0/r16xUIBJSYmKj8/Pyo5IlQB5Gi8pTlZZddpp07d0o6sVXB8OHDdeWVV2rbtm1qb2/XoUOH1N7e3uXoGAAAwNk4dakLl8tlu6UvojJCNmvWLM2dO1dLlizR4MGDlZeXp/j4eA0fPlyTJk1Se3u75s2bF41SAABAD+R2u3XRRRepqqpKF154oe0WhnVZJ+9wixEVFRVMWQIAgG7x+/2aPHmyWlpalJSUpDVr1kQllIWaW1gYFgAAON6pT1lalmW7pywJZAAAwPHKyso6PGW5ceNGwxV1RCADAACOl5ubq8TERElSYmKixo4da7iijghkAADA8bxeb3BN1Li4ONs9ZUkgAwAAjud2u+XxeORyueTxeGz3lGVUlr0AAAAwzev1qqqqynajYxKBDAAA9BBut7vDXtt2wpQlAACAYQQyAAAAw5iyBAD0aBs2bND69etD+mxtba0kdWvv5XHjxik/P/+sakPP0WUgq6+v1/bt23X8+PHga1//+tcjWpRd+P1+/fSnP9WCBQts9zQGACD6ampqJHUvkAGh6DKQTZ06VRdddJHcbrckBdfw6Al8Pp/Ky8vl8/k0Y8YM0+UAACIgPz8/5BGsadOmSZKWLl0ayZLQA3UZyCzL0uLFi6NRi634/X6VlpbKsiyVlpbK6/UySgYAACKi05v6W1pa1NLSoksuuURvvPFG8LilpSWa9Rlz6iak7e3tttuEFAAAdM+7774rj8ejyspK06WcptMRsvz8fLlcLlmWpR07dgRfd7lc2rRpU1SKM+lMm5AybQkAQOwqLi5WY2OjFi5cqCeffNJ0OR10Gsg2b94sSSovL9ewYcOCr+/cuTPyVdlAbm6u1q9fr0AgYMtNSAEAQOjeffddVVVVSZKqqqpUWVmpIUOGmC3qFJ1OWb7++utas2aNfvKTn2jNmjVas2aN/vCHP+hnP/tZNOszxu6bkIaD3+9XUVFR8KkhAACcqri4uMPxwoULDVVyZp0Gsj59+qi6ulotLS2qrq5WdXW1amtr9eMf/zia9Rlj901Iw+HUp0gBAHCyk6NjnR2b1umUZXZ2trKzszVx4kQNGDAgmjXZhp03IT1XPEUKAOhJLr74Yh08eLDDsZ10uXXShAkT9PnPf16jRo3SF77wBV155ZUaO3astm/fHo36ECE8RQoA6Ek+eb/Y0KFDDVVyZl0Gsi996Ut67rnntG3bNq1fv1433HCDli9frl//+tfRqM8oJ0/pnekpUgAAnOrVV1/tcGy3hxS7DGSHDx/W4MGDJUlZWVn68MMPNXDgQMXHx0e8OJM+OaXntBvfc3NzlZiYKEk8RQoAcLzc3NxgdomPj7fdda/LQHbeeefpF7/4hTZt2qRf/OIXcrvd2r59e/Bi7lROn9LrCU+RAgBwktfrDQayhIQE2133ugxkP//5zzVgwABt3bpVF1xwgR588EGlpKRoyZIl0ajPGKdP6fWEp0gBADjJ7te9LveyTEpK0uWXX66cnBxJJxaK/dKXvhTxwkzrCQvDOvkpUgAAPsnO170uA9kPf/hD1dXV6YILLpBlWXK5XD0ikHm9XpWWlkpy7pSe2+1WSUmJ6TIAAIgKO1/3ugxkNTU1Wr16dTRqsZWTQ5vPPvusLYc2AQCAc3R5D9mll16qI0eORKMW2/F6vRo2bJgjR8cAAIB9dDlCtnv3bl1//fXq169f8Km8bdu2RbwwO7Dz0CYAAHCOLgPZiy++GI06AAAAeqwuA9l7772n+fPnq76+XjfddJOGDh2q66+/Phq1AQAA9Ahd3kNWXFysxYsXKyMjQ7fccgtTeAAAICb5/X4VFRXZcvedLgOZJA0cOFAul0uZmZlKTU2NdE0AAABhZ+c9qrsMZH379tXq1at17NgxvfDCC+rTp0806gIAAAgbu+9R3WUge+CBB3Tw4EH169dPb7/9th544IFo1AUAiFF2nhbCp3PyubP7HtVdBrK0tDTdc889WrZsmWbNmqX/+Z//iUZdAIAYZedpIXw6J587u+9RHdI9ZKd6/vnnI1EHAMAB7D4thM45/dzl5uYqMTFRkmy5R3W3A9nJ4T4AAD7J7tNC6JzTz90nd92x2y48nQaylpaWM/4PAIDO2H1aCJ1z+rlzu91KTk6WJCUnJ9tuj+pOF4bNz88PbpV0kmVZp70GAMBJubm5Wr9+vQKBgC2nhdA5p5+7d999Vw0NDZKkhoYGVVZWasiQIYar+rdOA9nmzZujWQcAwAG8Xq9KS0slSXFxcbabFkLnnH7uiouLOxwvXLhQTz75pKFqTtfte8gAAOiM2+2Wx+ORy+WSx+Ox3bQQOuf0c1dVVfWpx6YRyAAAYeX1ejVs2DDHjbD0BE4+d4MGDfrUY9O6DGQvvPCCGhsbo1ELAMAB3G63SkpKHDfC0hM4+dzNmTOnw/G8efMMVXJmnd5DdtL777+v733ve0pPT1dubq6+9rWvKSMjIxq1AQAAhEV2drYGDRqkqqoqDRo0yFY39EshjJDdddddeuqppzR16lStXbtWo0aNikZdAAAAYTVnzhylpqbabnRMCmGE7IEHHtCePXvUr18/3XjjjXrwwQejURcAAEBYZWZmasiQIerXr5/pUk7T5QjZ8ePHlZycrAsuuEAXXnihBgwYEI26AAAAwsrOe3V2GchOrtPxjW98QytWrNDVV18djboAAADCxu57dXYZyB5//HF9+9vf1gMPPKBrr71Wf/7zn6NRFwAAQNj4fD61t7dLktra2mw3StblPWTx8fFavHixzj///GjUAwAAEHZlZWVqbW2VJLW2tmrjxo2aMWOG4ar+rcsRsmuuuUYzZszQTTfdpGXLlmnLli3RqAsAACBsRo8e3eF4zJgxhio5sy4D2aJFi7R48WJlZGTolltuUUlJSTTqsgW/36+ioiLbzTMDAABnCWnrpIEDB8rlcikzM1Opqaln9UWBQEAzZ87U5MmTVVBQoP379+vAgQO67bbbVFBQoPnz5wfndu3Czk9jAACA0L388ssdjrdu3WqokjPrMpD17dtXq1ev1rFjx/TCCy+oT58+Z/VFL730klpbW7V69WpNnTpVv/rVr7R48WJNnz5dTz/9tCzL0qZNm86q7Uiw+9MYAAAgdDE/ZfnAAw/o4MGD6tevn95++20tWrTorL7o0ksvVVtbm9rb29XQ0KCEhATt3btXI0aMkHTiD+aVV145q7YjwefzybIsSVJ7ezujZAAAIGI6fcry8OHDOv/881VdXa0JEyYEX6+rqzurvSxTUlL0wQcfyOPxqK6uTo8++qhee+01uVwuSVJqaqrq6+u7bKe5uVkVFRXd/v7uevHFFxUIBCSdmG7dsGGDPB5PxL8XAGBfTU1NkhSV6xDC66WXXupw/Ne//lXf+MY3DFVzuk4D2RNPPKHZs2eftt+Ty+XSk08+2e0vWrFihUaNGqWZM2fqww8/lNfrDQYeSWpsbAxpOjQ5OVk5OTnd/v7uysvL0/r16xUIBJSYmKj8/PyofC8AwL5SUlIkietBDPrKV76iF198MXh83XXXReU8hhreOw1ks2fPliR5vV599atfVVxcSPf/d6pPnz5KTEyUdOK+tNbWVl122WXauXOnrr76am3dulUjR448p+8IJ6/Xq9LSUklSXFycvF6v4YoAAIBTdZmyXnnlFd1888365S9/qX/+859n/UXf+ta3tHfvXhUUFMjr9eruu+/WvHnzVFJSokmTJikQCCgvL++s2w83t9stj8cjl8slj8ej/v37my4JAACcJbs/ZdnlSv3z5s1TS0uLNm3apIULFyoQCGjFihXd/qLU1FT9+te/Pu31VatWdbutaPF6vaqqqmJ0DACAGJebm6sXXnhBra2tSkhI0NixY02X1EFI85Dl5eXatm2bampqdM0110S6Jtuora1VZWWl6urqTJcCwEFYdBqIPq/XG7z9Kj4+3naDLV0GsnHjxunpp5/WjTfeqHXr1unOO++MRl22UFxcrMbGRi1cuNB0KQAchEWngeiz+61IXQayp556SgsXLlRmZmbwcd+e4N1331VVVZUkqaqqSpWVlWYLAuAILDoNmOP1ejVs2DDbjY5JIdxD9uqrr+p3v/ud2tralJ+fL5fLpR/84AfRqM2o4uLiDscLFy48q+U+AOBUZ1p0esaMGYarAmLXhg0btH79+pA+W1tbK0n66U9/GtLnx40bp/z8/LOurTu6HCF74okntHbtWmVkZOgHP/iB/vKXv0SjLuNOjo51dgwAZ6OsrKzDotMbN240XBHQc9TU1Nh2VLrLEbK4uDglJSXJ5XLJ5XKpd+/e0ajLuEGDBnUIYYMGDTJWCwDnyM3N7bDotN2e9AJiTX5+fsijWNOmTZMkLV26NJIlnZUuA9nw4cM1c+ZMHTlyRPPmzdMXv/jFaNRl3Jw5c/Td7343ePzJHQvsqDvDttK/h24zMzND+nw0h24Bp2LRaUTb2UzpcV2Ivi6nLGfMmKGbb75Zt956q6677jrde++90ajLuOzs7OCo2KBBgzRkyBCzBUWAnYduzxXLCsCu7P6kF3o2J18X7K7TEbLW1lZt3rxZffr00ZgxYzRmzBhVV1dr+vTp+tWvfhXNGo2ZM2eOfvSjH8XE6JjUvWFbyd5Dt+fq1GUFuGEadsOi04gmp0zpOV2ngeyee+5RfHy8qqurVVlZqYsvvlj333+/br/99mjWZ1R2dnZwagGx45PLCni9XkYhYCtut1slJSWmywBgI50Gsvfff1/r1q1TS0uLJkyYoMTERD355JP6zGc+E836gG5jWQEAQKzp9B6ytLQ0SVJSUpLa29v1+OOPE8YQE1hWAAAQa0Lay7J///7KyMiIdC1AWOTm5ioxMVGSWFYAABATOp2yrKys1MyZM2VZVvDfT3rkkUeiUhxwNlhWAAAQazoNZKc+STl58uSoFAOEw8llBZ599lmWFQAAxIROA9mIESOiWQcQViwrAACIJV2u1A/EIpYVAADEkpBu6gcAAEDkEMgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAKLM7/erqKhINTU1pksBYBMEMgCIMp/Pp/Lycvl8PtOlALAJAhkARJHf71dpaaksy1JpaSmjZAAkEcgAIKp8Pp8sy5Iktbe3M0oGQBKBDACiqqysTIFAQJIUCAS0ceNGwxUBsAMCGQBEUW5urhITEyVJiYmJGjt2rOGKANhBj9vLcsOGDVq/fn1In62trZUkZWZmhvT5cePGKT8//6xrA+B8Xq9XpaWlkqS4uDh5vV7DFQGwA0bIPkVNTQ033AIIK7fbLY/HI5fLJY/Ho/79+5suCYAN9LgRsvz8/JBHsaZNmyZJWrp0aSRLAtDDeL1eVVVVMToGIKjHBTIAMM3tdqukpMR0GQBshClLAAAAwwhkAADSB68JAAAbpElEQVQAhjFlCQD4VN15Ol3iCXXgbBDIAABhdfLp9FADGQACGQCgC915Ol3iCXXgbBDIAEQFizIDQOcIZABshykvAD0NgQxAVLAoMwB0jmUvAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEsewEAYcDCtwDOBYEMAKKMhW8BfBKBDADCgIVvAZwL7iEDAAAwjEAGAABgGIEMAADAMAIZAACAYVG9qf+xxx7T5s2bFQgEdNttt2nEiBG699575XK5NHToUM2fP19xcWREAADQs0Qt/ezcuVNvvPGG/vCHP2jlypU6fPiwFi9erOnTp+vpp5+WZVnatGlTtMoBAACwjaiNkG3btk3Z2dmaOnWqGhoa9JOf/ERr167ViBEjJEljxozR9u3blZubG62SEGMiufCmxOKbAABzohbI6urqdOjQIT366KM6ePCg7rrrLlmWJZfLJUlKTU1VfX19l+00NzeroqIi0uVKkpqamiQpat8XbbHWv0OHDgVr7kp1dbUkqVevXt1qP1b+LJwu1n42u4v+xS4n902ifyZFLZBlZGRo8ODBSkpK0uDBg5WcnKzDhw8H329sbFSfPn26bCc5OVk5OTmRLDUoJSVFkqL2fdEWa/3LycnRlClTQvosC2/Gtlj72ewu+he7nNw3if5FQqjhL2r3kF111VV6+eWXZVmWjhw5omPHjumaa67Rzp07JUlbt27V8OHDo1UOAACAbURthOz666/Xa6+9pltuuUWWZWnevHm6+OKLNXfuXC1ZskSDBw9WXl5etMoBAACwjague/GTn/zktNdWrVoVzRIAAABsh83FAZuI5FOkPEEKAPZGIANiUE1NjaTuLesBALAvAhlgE/n5+SGPYvEUKQA4C/sUAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACGEcgAAAAMSzBdAAAA4bZ06VJVVlaGvd333ntPkjRt2rSwty1JQ4YMiVjbsDcCGQDAcSorK/X2228rLS0trO1aliVJqqqqCmu7ktTQ0BD2NhE7CGQAAEdKS0vTlVdeabqMkO3evdt0CTCIe8gAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGBYgukCAADRt3TpUlVWVkak7ffee0+SNG3atLC3PWTIkIi0C5hGIAMiKFIXvUhe8CQuej1BZWWl/u/tCl2Sfn7Y2063ekmSmg7UhbXdf9YfDmt7gJ0QyIAIqqys1L4331S4L3m9//XPo2++GeaWJS55Pccl6edr5ohvmy4jZI+8+oTpEoCIIZABEXa+pO/IZbqMkP23LNMlAECPw039AAAAhhHIAAAADCOQAQAAGMY9ZAAAxBie4HYeAhkAADGmsrJS77zzhtznhfchnMSkE//8qHp3WNuVJH917DzcZAKBDACAGOQ+z9L48S2mywjZunVJpkuwNUcEslgcumXYFgCAM+uJ1/WoB7KamhqNHz9ejz/+uBISEnTvvffK5XJp6NChmj9/vuLiuv+cQWVlpd546x21p2SGtVZX24k/nl37w7tUZlxTbVjbAwDASSorK/XGOxVqO+8/wtquKylZkvR6dXivw/HVR865jagGskAgoHnz5qlXrxPbaixevFjTp0/X1VdfrXnz5mnTpk3Kzc09q7bbUzJ1/LIbw1luxPR65/mQP8t+cwCAnqjtvP9Q44RC02WEJPWZlefcRlQD2UMPPaTJkydr2bJlkqS9e/dqxIgRkqQxY8Zo+/btZx3InKqyslLvvr1bWWltYW+7j3XiBsvjVa+Ftd33G+LD2h7sqydOKwBAJEQtkK1bt06ZmZkaPXp0MJBZliWX60QoSE1NVX19fZftNDc3q6KiosNrTU1N4S84wpqamk7rR2efy0pr05zhDVGoKjyKX08LuX+RcvJnwmQNp9YRa0I9f+Xl5frH/gPKSBkQ1u+PazsxrfDB/pqwtnu06SPjP5uSPX4+nf6zSf/sycn9O9ffLVELZM8884xcLpf+9re/qaKiQrNmzVJt7b/ncBsbG9WnT58u20lOTlZOTk6H11JSUiR9HO6SIyolJeW0fnT2ueNRqCfcQu1fJL9fktEaTtYRO89A/Vt3fj4zUgbo+s9NjkJV527LvtXGfzYle/x8pqSkqEnNxr7/bHXnZzMWdad/DY1RKCjMunX+GmPr6tdZ30INaVELZE899VTw3wsLC7VgwQI9/PDD2rlzp66++mpt3bpVI0eOjFY5ANAlpmQBRIvRZS9mzZqluXPnasmSJRo8eLDy8vJMlgMAHVRWVurtPXuUnhTeX5VWW7sk6UDF3rC2W9/SGtb2AESPkUC2cuW/n0ZYtWqViRIAICTpSQka8R/9TJcRkleP1JkuAcBZcsTCsIhdsTglJDEtBAAILwIZjKqsrNQbe9+QMsLc8L/WF37jgzfC3LCko+FvEgDQsxHIYF6G1H5du+kqQhb31+7vJgEAwKchkAEAHKe2tlb19fXavXu36VJCVl9f32E5KPQs/FUfAADAMEbIAACOk5mZqY8//lhXXnml6VJCtnv3bmVmZpouA4YwQgYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAAACG8ZQlEEG1tbU6Ium/ZZkuJWQfSmpnLSQAiCpGyAAAAAxjhAxG1dbWSkdjbDuio1Jt79BGkDIzMxX3/vv6jlwRLip8/luWMlgLCQCiKoauggAAAM7ECBmMyszM1IFjB2Juc3FW0wYAhBMjZAAAAIYRyAAAAAwjkAEAABjmiHvIamtrFddUo17vPG+6lJDENdWotjbJdBkAAMAmGCEDAAAwzBEjZJmZmfpHXYuOX3aj6VJC0uud53lKDwAABDkikAEwo7a2VkebPtKWfatNlxKSo00fqXdt7CzSC6DnYMoSAADAMEbIAJy1zMxMHauzdP3nJpsuJSRb9q3mdgEAtkQgA4BO1NbWqr6lVa8eqTNdSkjqW1pP7A8LIOYQyACgB6qtrVV1/RE98uoTpksJ2T/rD+u8Wst0GbZQW1srf7VL69bFzhJK/mqXEuL5C0NnCGQA0InMzEzVH/lQI/6jn+lSQvLqkTqmZIEYRSADgB4oMzNTvepdmjni26ZLCdkjrz6hlMzYCMeRlpmZqda2Ko0f32K6lJCtW5fEXxg+BU9ZAgAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGE8ZQkAAGyltrZW8dVHlPrMStOlhCS++ohq48+tDUbIAAAADGOEDAAA2EpmZqb+3iY1Tig0XUpIUp9Zec5rrBHIbO7E9ibxKn49zXQpITtQH6/z2E8PAICQMWUJAABgGCNkNpeZmamUj/drzvAG06WErPj1NPViewwAAELGCBkAAIBhBDIAAADDCGQAAACGEcgAAAAMI5ABAAAYRiADAAAwjEAGAABgGIEMAADAMBaGBQA4UkNDg3bv3h3WNltaWiRJSUlJYW1XOlEvei4CGRBhhyX9t6ywtnny13Ykdjg9LCkjAu0C0TRkyJCItPvee+9JkgYNGhSR9iNVN+yPQAZEUKR+uVb/66Jw8dChYW87Q1wUEPumTZsW0XaXLl0akfbRcxHIgAjiogAACAWBDMA5Odr0kbbsWx3WNo8HGiVJvRJTw9ru0aaPdJH6d+u/qW9p1atH6sJaR3NbuyQpOT68z1XVt7SGtT0A0RO1QBYIBHTffffpgw8+UEtLi+666y4NGTJE9957r1wul4YOHar58+crLo4HP4FYEbn7dGolSRd9Jius7V6k/t2qOdL3IQ2MwJQz081AbIpaIHv22WeVkZGhhx9+WHV1dfrGN76hz33uc5o+fbquvvpqzZs3T5s2bVJubm60SgJwjpw+Jev0/gGwj6gNR+Xn5+tHP/pR8Dg+Pl579+7ViBEjJEljxozRK6+8Eq1yAAAAbCNqI2SpqSfuBWloaNC0adM0ffp0PfTQQ3K5XMH36+vru2ynublZFRUVHV5ramoKf8ER1tTUdFo/OvtcLE7idqd/OirF/TXMvTz+r3/2Cm+zkqSjUlO/0PoXKSd/5k3WEEn0L3o1xJpQf7dE8vsl8z+bTj9/sdi/c/3ZjOpN/R9++KGmTp2qgoIC3XTTTXr44YeD7zU2NqpPnz5dtpGcnKycnJwOr6WkpEj6ONzlRlRKSspp/ejsc8e7/JT9hNq/YcOG/ev8hdfJe3SGXhT+e3R00Yn7dELpX6Sc/DMzWUMk0b/o1NCkZmPff7ZC/d0Sye+XzP9spqSkqKHRaAlnpTvXPjXG1tWvs76FGtKiFsj8fr+mTJmiefPm6ZprrpEkXXbZZdq5c6euvvpqbd26VSNHjoxWObAJ7tEBgLPjr3Zp3brw7hhwcmAqAn9Plr/apQHnhb9dp4haIHv00Uf18ccf67e//a1++9vfSpLuv/9+FRcXa8mSJRo8eLDy8vKiVQ4A9Hj/rD+sR159Iuztftx8Yi+JPsnh3Uvin/WH9Vn1C2ubsSrSTwAPGBj+2YUB5/EU8KeJWiCbM2eO5syZc9rrq1atCkv7cU216vXO82Fp6yRX4JgkyUrsHdZ245pqJZ0f1jYBoDsieWH84D2/JOn8gZeEtd3Pqh8X9H9hdsF5HLEwbKT/pjD0M+EOT+fzSwWAUZG6oJ/aNhd1IHSOCGT8TQEAAMSyWFxRAQAAwFEcMUIGAACcJb76iFKfWRnWNl1NJx44sVLC+8BJfPUR6bzMc2qDQAYAAGwlcveG10iShg4M7z65Oi/znGsmkAEAAFvpifeGE8hiwPsN8Sp+PbzDq5L0/1pObFvVN8kKa7vvN8QrO6wtAgDgbAQym4vk8hj//NeyHv8xKLwLAGaLxf8AAOgOApnNsVYQAADOx7IXAAAAhhHIAAAADCOQAQAAGEYgAwAAMIxABgAAYBiBDAAAwDACGQAAgGEEMgAAAMMIZAAAAIYRyAAAAAwjkAEAABhGIAMAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDEkwXAKBn2LBhg9avXx/SZ9977z1J0rRp00L6/Lhx45Sfn3/WtQGAaQQyALbTv39/0yUAQFQRyABERX5+PqNYANAJApmDdGdKSGJaCEBo+N0CRB6BrAdjWghAJPC7Beg+ApmDMCUEIBL43QJEHoEMANCjRfIJYIkpWYSGQAYAQIiYjkWkEMgAAD0aU7KwAwIZYgbTCgAApyKQwZGYVgAAxBICGWIG0woAAKcikAFAGLBXJ4BzQSADgChjSh3AJxHIACAMmFIHcC7iTBcAAADQ0xHIAAAADCOQAQAAGOayLMsyXUR3VFRUKCcn56z/+7N5Emro0KEhfZ4noXAu+NkEEAlO/91i9/6Fmlu4qf9T8CQU7IqfTQCR4PTfLXbuX48bIQMAAIiWUHML95ABAAAYRiADAAAwjEAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhlfGLa9vV0LFizQ//3f/ykpKUnFxcUaOHCg6bIAAACixvgI2V/+8he1tLRozZo1mjlzph588EHTJQEAAESV8UC2a9cujR49WpJ0+eWX6+233zZcEQAAQHQZn7JsaGhQWlpa8Dg+Pl6tra1KSDhzac3NzaqoqIhWeQAAABFnPJClpaWpsbExeNze3t5pGJOk5ORk9rIEAAAxIdRBJONTlldeeaW2bt0qSXrzzTeVnZ1tuCIAAIDoMj5Clpubq+3bt2vy5MmyLEsPPPCA6ZIAAACiynggi4uL08KFC02XAQAAYIzxKUsAAICejkAGAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADCOQAQAAGGZ8YdjuYnNxAAAQK5qbm0P6nMuyLCvCtQAAAOBTMGUJAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHITrFnzx4VFhae9vrmzZs1YcIETZo0SWvXrjVQ2bkJBAL68Y9/rIKCAt1yyy3atGlTh/djvX9tbW2aPXu2Jk+erG9+85t6//33O7wf6/2TpJqaGn3lK1/R/v37O7zuhL5J0te//nUVFhaqsLBQs2fP7vDe2rVrNX78eE2cOFFbtmwxVOHZe+yxxzRp0iSNHz9ef/zjHzu8F+vnb926dcHzNnHiRH3xi1/Uxx9/HHw/1s9dIBDQzJkzNXnyZBUUFDju/38tLS2aOXOmJk6cqClTpqiqqqrD+7F6/k69lh84cEC33XabCgoKNH/+fLW3t3f47PHjx1VUVKSCggLdcccdqq2tNVHyCRYsy7KsZcuWWTfeeKN16623dni9paXFuuGGG6yjR49azc3N1vjx462PPvrIUJVn509/+pNVXFxsWZZl1dbWWl/5yleC7zmhf2VlZda9995rWZZl7dixw/r+978ffM8J/WtpabF+8IMfWGPHjrUqKys7vB7rfbMsyzp+/Lh18803n/G9jz76yLrxxhut5uZm6+OPPw7+e6zYsWOHdeedd1ptbW1WQ0ODtXTp0uB7Tjl/Jy1YsMBavXp18DjWz51lnfjdMm3aNMuyLGvbtm3WD3/4w+B7Tjh/K1eutObMmWNZlmXt37/fmjJlSvC9WD1/n7yW33nnndaOHTssy7KsuXPnWhs3buzw+ccffzz4/8vnn3/e+tnPfhbdgk/BCNm/ZGVlqaSk5LTX9+/fr6ysLPXt21dJSUm66qqr9Prrrxuo8Ozl5+frRz/6UfA4Pj4++O9O6N8NN9ygn/3sZ5KkQ4cOye12B99zQv8eeughTZ48WQMGDOjwuhP6Jkn79u3TsWPHNGXKFN1+++168803g++Vl5friiuuUFJSktLT05WVlaV9+/YZrLZ7tm3bpuzsbE2dOlXf//73dd111wXfc8r5k6S33npLlZWVmjRpUvC1WD93knTppZeqra1N7e3tamhoUELCv9dSd8L5q6ys1JgxYyRJgwcP7jACGKvn75PX8r1792rEiBGSpDFjxuiVV17p8Pldu3Zp9OjRwff/9re/Ra/YT4i5lfojJS8vTwcPHjzt9YaGBqWnpwePU1NT1dDQEM3SzllqaqqkE32ZNm2apk+fHnzPCf2TpISEBM2aNUtlZWVaunRp8PVY79+6deuUmZmp0aNHa9myZR3ei/W+ndSrVy995zvf0a233qqqqirdcccd2rBhgxISEmK+j3V1dTp06JAeffRRHTx4UHfddZc2bNggl8sV83071WOPPaapU6d2eM0J/UtJSdEHH3wgj8ejuro6Pfroo8H3nNC/nJwcbdmyRTfccIP27NmjI0eOqK2tTfHx8THbv09eyy3LksvlknSiD/X19R0+f2o/z/R+NDFC1oW0tDQ1NjYGjxsbGzv8kMaKDz/8ULfffrtuvvlm3XTTTcHXndI/6cRI0osvvqi5c+eqqalJUuz375lnntErr7yiwsJCVVRUaNasWaqurpYU+3076dJLL9V//ud/yuVy6dJLL1VGRoZj+piRkaFRo0YpKSlJgwcPVnJycvAelVjv20kff/yx/v73v2vkyJEdXndC/1asWKFRo0bpxRdf1J///Gfde++9wW1wnNC/CRMmKC0tTbfffru2bNmiz3/+88EZFCf0T5Li4v4dcxobG9WnT58O75/azzO9H00Esi585jOf0YEDB3T06FG1tLTo9ddf1xVXXGG6rG7x+/2aMmWKfvzjH+uWW27p8J4T+ve///u/euyxxyRJvXv3lsvlCv5SifX+PfXUU1q1apVWrlypnJwcPfTQQzrvvPMkxX7fTvrTn/6kBx98UJJ05MgRNTQ0BPs4bNgw7dq1S83Nzaqvr9f+/fuVnZ1tstxuueqqq/Tyyy/LsiwdOXJEx44dU0ZGhiTnnL/XXntNX/7yl097PdbPnST16dMnGEL69u2r1tZWtbW1SXLG+Xvrrbd01VVXaeXKlbrhhht0ySWXBN9zwvmTpMsuu0w7d+6UJG3dulXDhw/v8P6VV16pl156Kfj+VVddFfUaT2Ivy1McPHhQM2bM0Nq1a/Xcc8+pqalJkyZN0ubNm/Vf//VfsixLEyZM0De/+U3TpXZLcXGxSktLNXjw4OBrt956q44dO+aI/jU1NWn27Nny+/1qbW3VHXfcoWPHjjnm/J1UWFioBQsW6J133nFU31paWjR79mwdOnRILpdL99xzj/bs2aOsrCx97Wtf09q1a7VmzRpZlqU777xTeXl5pkvulp///OfauXOnLMvS3XffraNHjzrq/P3+979XQkKCvvWtb0mSnnjiCcecu8bGRt13332qrq5WIBDQ7bffLkmOOX+1tbWaMWOGjh07pvT0dC1atEjr16+P+fN36rX8H//4h+bOnatAIKDBgweruLhY8fHxmjJlih599FG1tbUFZx4SExP1yCOPBP9CGG0EMgAAAMOYsgQAADCMQAYAAGAYgQwAAMAwAhkAAIBhBDIAAADDCGQAHGXnzp265pprgptejx8/XtOmTVNLS8sZP3/o0CFt3rxZkrRo0SIdOnQomuUCgCQCGQAHGjlypFauXKmVK1dq3bp1SkxMDIauT9qxY4d2794tSbr//vt14YUXRrNUAJDEXpYAHK6lpUUfffSR+vbtq/vvv1+HDx9WXV2dxowZo6KiIi1btkzHjx/XFVdcoRUrVmjBggVav369Dh48qJqaGh06dEizZ8/W6NGjtWXLFi1dulRpaWnq27evPvvZz6qoqMh0FwE4AIEMgOPs2LFDhYWFqqmpUVxcnCZOnKhLLrlEl19+uW699VY1NzdrzJgxmj59ur73ve/p73//u772ta9pxYoVwTaSkpL0+9//Xtu3b9fjjz+uL3/5yyouLtaaNWvkdrs1c+ZMcx0E4DgEMgCOM3LkSP3yl79UXV2dpkyZoosvvlgZGRl66623tGPHDqWlpXV6T9lJOTk5kqTzzz9fLS0tqq2tVVpamtxutyRp+PDh8vv9Ee8LgJ6Be8gAOFa/fv308MMPa86cOVqxYoXS09P1yCOPaMqUKTp+/Lgsy1JcXJza29tP+29dLleH4/79+6uxsVG1tbWSpD179kSlDwB6BkbIADjakCFDVFhYqIqKCv3jH//Qrl271Lt3bw0cOFAfffSRsrOz9bvf/U6f//znP7WduLg4zZ07V3fccYfS09PV3t6ugQMHRqkXAJyOzcUBIESPPfaYvv3tbyspKUn33HOPRo0apa9//eumywLgAIyQAUCIUlNTNXHiRPXq1UsXXXSRxo0bZ7okAA7BCBkAAIBh3NQPAABgGIEMAADAMAIZAACAYQQyAAAAwwhkAAAAhhHIAAAADPv/kxlDwqdqnCEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let us see if there is a relationship between length of a review and rating \n",
    "test_df['Review']=test_df['Review'].astype(str)\n",
    "test_df['Review Length']=test_df['Review'].apply(len)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.boxplot(x='Rating', y='Review Length', data=test_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that Reviews with ratings 1 and and 8 are the longest in length!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us try to use NLP and machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing NLP Libraries\n",
    "from collections import Counter\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from stop_words import get_stop_words\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import sent_tokenize, word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Akshay\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us try to extract all the words and try to perform analysis on it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = test_df['Review'].str.lower().str.cat(sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removes punctuation,numbers and returns list of words\n",
    "b = re.sub('[^A-Za-z]+', ' ', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove all the stopwords from the text\n",
    "stop_words = list(get_stop_words('en'))         \n",
    "nltk_words = list(stopwords.words('english'))   \n",
    "stop_words.extend(nltk_words)\n",
    "\n",
    "newStopWords = ['game','thrones', 'bran', 'stark', 'dragons']\n",
    "stop_words.extend(newStopWords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_tokens = word_tokenize(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have extracted all the words in the reviews. Let us find the total length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6030"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will now remove the stop words from the reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_sentence = []\n",
    "for w in word_tokens:\n",
    "    if w not in stop_words:\n",
    "        filtered_sentence.append(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3599"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filtered_sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have removed all the stop words and reduced the size by half!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove characters which have length less than 2  \n",
    "without_single_chr = [word for word in filtered_sentence if len(word) > 2]\n",
    "\n",
    "# Remove numbers\n",
    "cleaned_data_title = [word for word in without_single_chr if not word.isnumeric()]   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us look at the top 10 most used words in a review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"notebook\", font_scale=1.3)\n",
    "top_N = 100\n",
    "word_dist = nltk.FreqDist(cleaned_data_title)\n",
    "rslt = pd.DataFrame(word_dist.most_common(top_N),\n",
    "                    columns=['Words', 'Frequency'])\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.barplot(x=\"Words\",y=\"Frequency\", data=rslt.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see that the most common words are positive indicating how great Game of thrones is! <br> Let us visualize this is a word cloud "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wc(data,bgcolor,title):\n",
    "    plt.figure(figsize = (100,100))\n",
    "    wc = WordCloud(background_color = bgcolor, max_words = 1000,  max_font_size = 50)\n",
    "    wc.generate(' '.join(data))\n",
    "    plt.imshow(wc)\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x7200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let us visualise the most common words\n",
    "wc(cleaned_data_title,'black','Most Used Words')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us try to perform analysis on the entire review rather than all the words. <br> For this we make use of the TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "bloblist_desc = list()\n",
    "df_review_str=test_df['Review'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in df_review_str:\n",
    "    blob = TextBlob(row)\n",
    "    bloblist_desc.append((row,blob.sentiment.polarity, blob.sentiment.subjectivity))\n",
    "    df_polarity_desc = pd.DataFrame(bloblist_desc, columns = ['Review','sentiment','polarity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will try to perform sentimental analysis and try to classify whether a review is a positive or negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Review</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finally, a Show that stays true to it's writt...</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Excellent adaptation.</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>One of the most stunning shows on television</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lord of the Rings - I think not!</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fantasy has never seemed so real</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  sentiment  polarity\n",
       "0   Finally, a Show that stays true to it's writt...      0.175     0.825\n",
       "1                              Excellent adaptation.      1.000     1.000\n",
       "2       One of the most stunning shows on television      0.500     0.750\n",
       "3                   Lord of the Rings - I think not!      0.000     0.000\n",
       "4                   Fantasy has never seemed so real      0.200     0.300"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_polarity_desc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see the sentiments of the reviews, if it is close to 1 it means it is a positive review and by looking at the dataset we can set the values like I've set below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(df_polarity_desc):\n",
    "    if df_polarity_desc['sentiment'] >= 0:\n",
    "        val = \"Positive Review\"\n",
    "    elif df_polarity_desc['sentiment'] >= -0.09:\n",
    "        val = \"Neutral Review\"\n",
    "    else:\n",
    "        val = \"Negative Review\"\n",
    "    return val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is a personal preference which you can change according to your choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_polarity_desc['Sentiment_Type'] = df_polarity_desc.apply(f, axis=1)\n",
    "plt.figure(figsize=(9,4))\n",
    "ax = sns.countplot(x=\"Sentiment_Type\", data=df_polarity_desc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that we have an unbalanced dataset with mostly positive reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Review</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>polarity</th>\n",
       "      <th>Sentiment_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Heads up for GAME OF THRONES</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Negative Review</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>As expected, some will love it, some will hat...</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>Negative Review</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Possibly the worst storyline I will ever enco...</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Negative Review</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>On finding Game of Thrones on VHS</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>Negative Review</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Unbelievably awful</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Negative Review</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Review  sentiment  polarity  \\\n",
       "15                       Heads up for GAME OF THRONES  -0.400000  0.400000   \n",
       "22   As expected, some will love it, some will hat...  -0.133333  0.633333   \n",
       "36   Possibly the worst storyline I will ever enco...  -0.500000  1.000000   \n",
       "42                  On finding Game of Thrones on VHS  -0.400000  0.400000   \n",
       "43                                 Unbelievably awful  -1.000000  1.000000   \n",
       "\n",
       "     Sentiment_Type  \n",
       "15  Negative Review  \n",
       "22  Negative Review  \n",
       "36  Negative Review  \n",
       "42  Negative Review  \n",
       "43  Negative Review  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_reviews=df_polarity_desc[df_polarity_desc['Sentiment_Type']=='Positive Review']\n",
    "negative_reviews=df_polarity_desc[df_polarity_desc['Sentiment_Type']=='Negative Review']\n",
    "negative_reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us look at the most used words in all the positive reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x7200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc(positive_reviews['Review'],'black','Most Used Words')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us look at the most used words in all the negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x7200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc(negative_reviews['Review'],'black','Most Used Words')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It is hard to believe such people exist in the world "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us trying to train a model to understand the sentiments and then we can try making a predicting using machine learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "def text_process(review):\n",
    "    nopunc=[word for word in review if word not in string.punctuation]\n",
    "    nopunc=''.join(nopunc)\n",
    "    return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us now train a model <br> We are taking only review with ratings 1 and 10 as most of the reviews are either 1 or 10 and to make the analysis more simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df=test_df.dropna(axis=0,how='any')\n",
    "rating_class = test_df[(test_df['Rating'] == 1) | (test_df['Rating'] == 10)]\n",
    "X_review=rating_class['Review']\n",
    "y=rating_class['Rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "bow_transformer=CountVectorizer(analyzer=text_process).fit(X_review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The CountVectorizer provides a simple way to both tokenize a collection of text documents and build a vocabulary of known words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_review = bow_transformer.transform(X_review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_review, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us train a model using Multinomial Naive Bayes as it works great on text as it assumes all features are equally important and avoids overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb = MultinomialNB()\n",
    "nb.fit(X_train, y_train)\n",
    "predict=nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us find the accuracy of our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 81.22 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
    "print('Accuracy:', round(accuracy_score(y_test, predict)* 100,2),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As seen earlier we have an unbalanced dataset and only accuracy cannot depict if our model is good at prediction. <br> Let us look at the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = confusion_matrix(y_test, predict)\n",
    "sns.heatmap(a, annot=True,cmap='Greens', fmt='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that there are a lot of False positive considering the unbalanced dataset with less number of reviews with 1 rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.50      0.35      0.41        40\n",
      "        10.0       0.86      0.92      0.89       173\n",
      "\n",
      "   micro avg       0.81      0.81      0.81       213\n",
      "   macro avg       0.68      0.63      0.65       213\n",
      "weighted avg       0.79      0.81      0.80       213\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = nb.predict_proba(X_test)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba,pos_label=10)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 10 auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have a 81 percent accuracy but a very recall score indicating that movies with ratings 1'sare not classified properly this maybe due to a large ratio of positive review. <br><br>Hence it has a bad recall.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us try SMOTE to handle the problems caused by imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions X_train dataset:  (494, 1058)\n",
      "Number transactions y_train dataset:  (494,)\n",
      "Number transactions X_test dataset:  (213, 1058)\n",
      "Number transactions y_test dataset:  (213,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number transactions X_train dataset: \", X_train.shape)\n",
    "print(\"Number transactions y_train dataset: \", y_train.shape)\n",
    "print(\"Number transactions X_test dataset: \", X_test.shape)\n",
    "print(\"Number transactions y_test dataset: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '10': 439\n",
      "Before OverSampling, counts of label '1': 55 \n",
      "\n",
      "After OverSampling, the shape of train_X: (878, 1058)\n",
      "After OverSampling, the shape of train_y: (878,) \n",
      "\n",
      "After OverSampling, counts of label '10': 439\n",
      "After OverSampling, counts of label '1': 439\n"
     ]
    }
   ],
   "source": [
    "print(\"Before OverSampling, counts of label '10': {}\".format(sum(y_train==10)))\n",
    "print(\"Before OverSampling, counts of label '1': {} \\n\".format(sum(y_train==1)))\n",
    "\n",
    "sm = SMOTE(random_state=2)\n",
    "X_train_res, y_train_res = sm.fit_sample(X_train, y_train.ravel())\n",
    "\n",
    "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))\n",
    "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape))\n",
    "\n",
    "print(\"After OverSampling, counts of label '10': {}\".format(sum(y_train_res==10)))\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res==1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By making using of synthetic minority sampling technique we can have a balanced dataset on which we can perform our predictions, our accuracy might decrease but it will help to improve the classification of reviews with 1 rating! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb.fit(X_train_res, y_train_res)\n",
    "predict_smote=nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us find the accuracy of our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 70.42 %\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy:', round(accuracy_score(y_test, predict_smote)* 100,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = confusion_matrix(y_test, predict_smote)\n",
    "sns.heatmap(a, annot=True,cmap='Greens', fmt='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that there is a reduction in the number of false positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.35      0.68      0.46        40\n",
      "        10.0       0.90      0.71      0.80       173\n",
      "\n",
      "   micro avg       0.70      0.70      0.70       213\n",
      "   macro avg       0.63      0.69      0.63       213\n",
      "weighted avg       0.80      0.70      0.73       213\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predict_smote))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Although, our overall accuracy has decreased. We have increased the recall to 68 percent. <br><br> Hence our model can classify review with 1 rating better. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us test our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' This TV series is magnificent!'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_positive=test_df['Review'][6]\n",
    "rating_positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_postive_transformed = bow_transformer.transform([rating_positive])\n",
    "nb.predict(rating_postive_transformed)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' disgusting, overrated, pointless'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_negative=test_df['Review'][54]\n",
    "rating_negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_negative_transformed = bow_transformer.transform([rating_negative])\n",
    "nb.predict(rating_negative_transformed)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thus we can see the power of natural language processing and how machine learning can be used to perform predictions!"
   ]
  }
 ],
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