YelpReviews.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('yelp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   business_id  10000 non-null  object\n",
      " 1   date         10000 non-null  object\n",
      " 2   review_id    10000 non-null  object\n",
      " 3   stars        10000 non-null  int64 \n",
      " 4   text         10000 non-null  object\n",
      " 5   type         10000 non-null  object\n",
      " 6   user_id      10000 non-null  object\n",
      " 7   cool         10000 non-null  int64 \n",
      " 8   useful       10000 non-null  int64 \n",
      " 9   funny        10000 non-null  int64 \n",
      "dtypes: int64(4), object(6)\n",
      "memory usage: 781.4+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\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>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>type</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife took me here on my birthday for breakf...</td>\n",
       "      <td>review</td>\n",
       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
       "      <td>review</td>\n",
       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
       "      <td>2012-06-14</td>\n",
       "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
       "      <td>4</td>\n",
       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
       "      <td>review</td>\n",
       "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
       "      <td>2010-05-27</td>\n",
       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-yxfBYGB6SEqszmxJxd97A</td>\n",
       "      <td>2007-12-13</td>\n",
       "      <td>m2CKSsepBCoRYWxiRUsxAg</td>\n",
       "      <td>4</td>\n",
       "      <td>Quiessence is, simply put, beautiful.  Full wi...</td>\n",
       "      <td>review</td>\n",
       "      <td>sqYN3lNgvPbPCTRsMFu27g</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>zp713qNhx8d9KCJJnrw1xA</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>riFQ3vxNpP4rWLk_CSri2A</td>\n",
       "      <td>5</td>\n",
       "      <td>Drop what you're doing and drive here. After I...</td>\n",
       "      <td>review</td>\n",
       "      <td>wFweIWhv2fREZV_dYkz_1g</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hW0Ne_HTHEAgGF1rAdmR-g</td>\n",
       "      <td>2012-07-12</td>\n",
       "      <td>JL7GXJ9u4YMx7Rzs05NfiQ</td>\n",
       "      <td>4</td>\n",
       "      <td>Luckily, I didn't have to travel far to make m...</td>\n",
       "      <td>review</td>\n",
       "      <td>1ieuYcKS7zeAv_U15AB13A</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>wNUea3IXZWD63bbOQaOH-g</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>XtnfnYmnJYi71yIuGsXIUA</td>\n",
       "      <td>4</td>\n",
       "      <td>Definitely come for Happy hour! Prices are ama...</td>\n",
       "      <td>review</td>\n",
       "      <td>Vh_DlizgGhSqQh4qfZ2h6A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nMHhuYan8e3cONo3PornJA</td>\n",
       "      <td>2010-08-11</td>\n",
       "      <td>jJAIXA46pU1swYyRCdfXtQ</td>\n",
       "      <td>5</td>\n",
       "      <td>Nobuo shows his unique talents with everything...</td>\n",
       "      <td>review</td>\n",
       "      <td>sUNkXg8-KFtCMQDV6zRzQg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AsSCv0q_BWqIe3mX2JqsOQ</td>\n",
       "      <td>2010-06-16</td>\n",
       "      <td>E11jzpKz9Kw5K7fuARWfRw</td>\n",
       "      <td>5</td>\n",
       "      <td>The oldish man who owns the store is as sweet ...</td>\n",
       "      <td>review</td>\n",
       "      <td>-OMlS6yWkYjVldNhC31wYg</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>e9nN4XxjdHj4qtKCOPq_vg</td>\n",
       "      <td>2011-10-21</td>\n",
       "      <td>3rPt0LxF7rgmEUrznoH22w</td>\n",
       "      <td>5</td>\n",
       "      <td>Wonderful Vietnamese sandwich shoppe. Their ba...</td>\n",
       "      <td>review</td>\n",
       "      <td>C1rHp3dmepNea7XiouwB6Q</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>h53YuCiIDfEFSJCQpk8v1g</td>\n",
       "      <td>2010-01-11</td>\n",
       "      <td>cGnKNX3I9rthE0-TH24-qA</td>\n",
       "      <td>5</td>\n",
       "      <td>They have a limited time thing going on right ...</td>\n",
       "      <td>review</td>\n",
       "      <td>UPtysDF6cUDUxq2KY-6Dcg</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WGNIYMeXPyoWav1APUq7jA</td>\n",
       "      <td>2011-12-23</td>\n",
       "      <td>FvEEw1_OsrYdvwLV5Hrliw</td>\n",
       "      <td>4</td>\n",
       "      <td>Good tattoo shop. Clean space, multiple artist...</td>\n",
       "      <td>review</td>\n",
       "      <td>Xm8HXE1JHqscXe5BKf0GFQ</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>yc5AH9H71xJidA_J2mChLA</td>\n",
       "      <td>2010-05-20</td>\n",
       "      <td>pfUwBKYYmUXeiwrhDluQcw</td>\n",
       "      <td>4</td>\n",
       "      <td>I'm 2 weeks new to Phoenix. I looked up Irish ...</td>\n",
       "      <td>review</td>\n",
       "      <td>JOG-4G4e8ae3lx_szHtR8g</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               business_id        date               review_id  stars  \\\n",
       "0   9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
       "1   ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
       "2   6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
       "3   _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
       "4   6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
       "5   -yxfBYGB6SEqszmxJxd97A  2007-12-13  m2CKSsepBCoRYWxiRUsxAg      4   \n",
       "6   zp713qNhx8d9KCJJnrw1xA  2010-02-12  riFQ3vxNpP4rWLk_CSri2A      5   \n",
       "7   hW0Ne_HTHEAgGF1rAdmR-g  2012-07-12  JL7GXJ9u4YMx7Rzs05NfiQ      4   \n",
       "8   wNUea3IXZWD63bbOQaOH-g  2012-08-17  XtnfnYmnJYi71yIuGsXIUA      4   \n",
       "9   nMHhuYan8e3cONo3PornJA  2010-08-11  jJAIXA46pU1swYyRCdfXtQ      5   \n",
       "10  AsSCv0q_BWqIe3mX2JqsOQ  2010-06-16  E11jzpKz9Kw5K7fuARWfRw      5   \n",
       "11  e9nN4XxjdHj4qtKCOPq_vg  2011-10-21  3rPt0LxF7rgmEUrznoH22w      5   \n",
       "12  h53YuCiIDfEFSJCQpk8v1g  2010-01-11  cGnKNX3I9rthE0-TH24-qA      5   \n",
       "13  WGNIYMeXPyoWav1APUq7jA  2011-12-23  FvEEw1_OsrYdvwLV5Hrliw      4   \n",
       "14  yc5AH9H71xJidA_J2mChLA  2010-05-20  pfUwBKYYmUXeiwrhDluQcw      4   \n",
       "\n",
       "                                                 text    type  \\\n",
       "0   My wife took me here on my birthday for breakf...  review   \n",
       "1   I have no idea why some people give bad review...  review   \n",
       "2   love the gyro plate. Rice is so good and I als...  review   \n",
       "3   Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
       "4   General Manager Scott Petello is a good egg!!!...  review   \n",
       "5   Quiessence is, simply put, beautiful.  Full wi...  review   \n",
       "6   Drop what you're doing and drive here. After I...  review   \n",
       "7   Luckily, I didn't have to travel far to make m...  review   \n",
       "8   Definitely come for Happy hour! Prices are ama...  review   \n",
       "9   Nobuo shows his unique talents with everything...  review   \n",
       "10  The oldish man who owns the store is as sweet ...  review   \n",
       "11  Wonderful Vietnamese sandwich shoppe. Their ba...  review   \n",
       "12  They have a limited time thing going on right ...  review   \n",
       "13  Good tattoo shop. Clean space, multiple artist...  review   \n",
       "14  I'm 2 weeks new to Phoenix. I looked up Irish ...  review   \n",
       "\n",
       "                   user_id  cool  useful  funny  \n",
       "0   rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0  \n",
       "1   0a2KyEL0d3Yb1V6aivbIuQ     0       0      0  \n",
       "2   0hT2KtfLiobPvh6cDC8JQg     0       1      0  \n",
       "3   uZetl9T0NcROGOyFfughhg     1       2      0  \n",
       "4   vYmM4KTsC8ZfQBg-j5MWkw     0       0      0  \n",
       "5   sqYN3lNgvPbPCTRsMFu27g     4       3      1  \n",
       "6   wFweIWhv2fREZV_dYkz_1g     7       7      4  \n",
       "7   1ieuYcKS7zeAv_U15AB13A     0       1      0  \n",
       "8   Vh_DlizgGhSqQh4qfZ2h6A     0       0      0  \n",
       "9   sUNkXg8-KFtCMQDV6zRzQg     0       1      0  \n",
       "10  -OMlS6yWkYjVldNhC31wYg     1       3      1  \n",
       "11  C1rHp3dmepNea7XiouwB6Q     1       1      0  \n",
       "12  UPtysDF6cUDUxq2KY-6Dcg     1       2      0  \n",
       "13  Xm8HXE1JHqscXe5BKf0GFQ     1       2      0  \n",
       "14  JOG-4G4e8ae3lx_szHtR8g     1       1      0  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have no idea why some people give bad reviews about this place. It goes to show you, you can please everyone. They are probably griping about something that their own fault...there are many people like that.\n",
      "\n",
      "In any case, my friend and I arrived at about 5:50 PM this past Sunday. It was pretty crowded, more than I thought for a Sunday evening and thought we would have to wait forever to get a seat but they said we'll be seated when the girl comes back from seating someone else. We were seated at 5:52 and the waiter came and got our drink orders. Everyone was very pleasant from the host that seated us to the waiter to the server. The prices were very good as well. We placed our orders once we decided what we wanted at 6:02. We shared the baked spaghetti calzone and the small \"Here's The Beef\" pizza so we can both try them. The calzone was huge and we got the smallest one (personal) and got the small 11\" pizza. Both were awesome! My friend liked the pizza better and I liked the calzone better. The calzone does have a sweetish sauce but that's how I like my sauce!\n",
      "\n",
      "We had to box part of the pizza to take it home and we were out the door by 6:42. So, everything was great and not like these bad reviewers. That goes to show you that  you have to try these things yourself because all these bad reviewers have some serious issues.\n"
     ]
    }
   ],
   "source": [
    "print(dataset['text'][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'My nephew just moved to Scottsdale recently so me and a bunch of friends brought him here to show him a local bar that he and the girlfriend could come shoot pool, watch football, play volleyball etc...well....we weren\\'t there 5minutes and 2 kids were running around the pool tables, messing up our games and screaming. I couldn\\'t believe the staff was allowing this to happen. They were hitting the pool sticks on everything and crying when their mom attempted (in vain) to quiet them. You\\'d think the mom would leave at this point with her  kids....um...no....the staff did seem annoyed as well but said nothing. Then....it happened...she said \" you guys better behave or mommy will be fired\"!! Holy shit....she works there!! Even worse! Shame on that owner for allowing this to happen. And that employee needs to recognize this...YOU WORK AT A BAR.....not a daycare....A BAR!!!'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['text'][len(dataset)-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['length'] = dataset['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2483132f320>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dataset['length'].plot(bins = 100 , kind ='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxlength = max(dataset['length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In our continuing quest to identify cool, locally owned places to eat and/or drink, Caroline and I auditioned Vintage 95 last night. \\n\\nBefore I go further, understand that whenever I go out for eats or drinks, I have  in  mind a Platonic Ideal of the Bar/Pub/Eatery I most want to frequent. I\\'m on a constant quest to find that  Ideal expressed in Real Life. \\n\\nVintage 95 isn\\'t quite there, but it\\'s damn close. If I ever give something Five Stars, you\\'ll know it has pegged my Platonic Ideal. Anyway...\\n\\nThe plan last night was for drinks. No plans for food, just Adult Beverages and warm conversation. But it turned into more.\\n\\nThe location in downtown Chandler is terrific for us. The owners have created a very fine visual experience - leather, stone, dark woods, good lighting. And they don\\'t have the music turned up so loud that you CANNOT HAVE A CONVERSATION. This is one of my pet peeves. If I want to stare at people move their mouths while enduring an aural assault, I\\'ll stand on the deck of an aircraft carrier. When I go out with friends, I want to enjoy their company AND their conversation. Is that concept so difficult to grasp? [/rant off]\\n\\nThe atmosphere at Vintage 95 is very close to my Ideal. I\\'d go back just to sit on the leather couches in front of the fireplace, and then go back another time to sit on the leather stools at the bar, and then go back about fourteen more times to sit out on the patio. Seriously - go check out the patio. It is EXACTLY what a Patio Hangout Bar should be. EXACTLY.\\n\\nCaroline and I told the hostesses we were only there for drinks, so we were seated in the bar area in some fabulous leather club chairs. It wasn\\'t initmate, but we weren\\'t looking for intimate. And speaking of the bar, even though V95 advertises itself as a wine bar, they DO have booze. I\\'m not much of a wine drinker and was very pleased to see that they carried a pretty good selection of single malt scotches. Not an overwhelming selection, but well beyond the normal Glenfiddich /Glenlivit /GlenMorangie trio to which most places are limited. I had a couple of drums of Ardbeg, which is one of my new favorites and very reasonably priced at retail. (Scotch is never reasonably priced in restaurants, but I was celebrating so I didn\\'t care.) Caroline had her normal \"vodka martini extra dirty extra cold\" which she judged to have \"perfect dirtiness\", (no wonder I love her!), perfect amount of olives and very cold. \\n\\nThe limited Happy Hour menu had some very interesting choices. We settled on the bruschetta and the smoked tomato bisque. The bruschetta was VERY nice and quite unusual. You get to select four of eight choices for your bruschetta platter; we picked: (1) white bean and pancetta, (2) gravlax, caper goat cheese and pickled onions, (3) fig chutney, ricotta and prosciutto, (4) brie, pear and onion jam. They were all served cold, in nice sized portions and the flavors were all nicely balanced and very interesting. Caroline would have preferred the bread to not be so crispy, but I really liked it. The tomato bisque  was creamy, smoky and had well-balanced flavor. Caroline said it was unique and I say it was just darn delicious. \\n\\nThings being as they are, drinks and appetizers turned into food. A friend had told us \"you have to try the Vintage burger\", so we did. It came served with a mixture of regular and sweet potato fries, all nicely cooked and nicely seasoned. Recommended. The burger was VERY tasty. They obviously use good beef, the bun was fresh, the fixin\\'s were tasty. HIGHLY recommended.\\n\\nIn for a dime, in for a dollar, right? So we ordered dessert. Again, the dessert menu is short, but I\\'m okay with that as long as they do it well. Chocolate torte with hazelnut gelato, apple pie with carmel sauce and creme fraiche gelato, and something else we couldn\\'t remember. I\\'m allergic to hazelnut and don\\'t like sweet desserts, so we decided to try the apple pie.\\n\\nLike everything else we had sampled, the apple pie was unusual - you wouldn\\'t find it anywhere else. It was served on a freshly baked puff pastry, cubed apples served on top and inside - tender but not mushy -  with lots of cinnamon and sugar, plate was swirled with salted dolce la leche. It was tasty, but instead of the expected creme fraiche gelato, we were served hazelnut gelato. I didn\\'t realize it was hazelnut until I\\'d had a couple of bites and my throat started to swell up.\\n\\nAt this point that the night could have turned into a disaster, but to their credit - it didn\\'t. We told the waiter who told the manager, (Gavin - one of the owners), who immediately came and asked if I needed emergency assistance. I didn\\'t, I\\'m not THAT allergic.)  Frankly, their response was EXACTLY the kind of customer service you want to see. Anyone can make a mistake, so no harm, no foul. But I must give BIG Kudos to Gavin for his kindness, attention to detail and outstanding customer service.\\n\\nWe will DEFINTELY be back and I strongly recommend you put it on your list too.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['length'] == maxlength]['text'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "minlength = min(dataset['length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['length'] == minlength]['text'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24831768470>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WYF0PWaVVseB1qAnLn3p1z+0P0v2OdCd9OnJqm59NLe/Gv4Y1Qha8DjXXAi9P8niYfMcn8C9MzmIK8AfAV7vlbcC53fJLmHxDz3J2MflaPGlwzjp0SKmqO5K8C7ghyW4mZ/97A/CRJJcB/wO8utv8CuDqJDcz+Y/hJzPsYhPwpSQ7qur8A/8MpNn5NklJapSHaCSpURa8JDXKgpekRlnwktQoC16SGmXBS1KjLHhJatT/A2Udzp+wR5RQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = 'stars' , data = dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x248317d9fd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg = sns.FacetGrid(data = dataset , col = 'stars' , col_wrap = 2)\n",
    "fg.map(plt.hist , 'length' , color = 'yellow' , bins = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "onestar_reviews = dataset[dataset['stars'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>type</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>IJ0o6b8bJFAbG6MjGfBebQ</td>\n",
       "      <td>2010-09-05</td>\n",
       "      <td>Dx9sfFU6Zn0GYOckijom-g</td>\n",
       "      <td>1</td>\n",
       "      <td>U can go there n check the car out. If u wanna...</td>\n",
       "      <td>review</td>\n",
       "      <td>zRlQEDYd_HKp0VS3hnAffA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>vvA3fbps4F9nGlAEYKk_sA</td>\n",
       "      <td>2012-05-04</td>\n",
       "      <td>S9OVpXat8k5YwWCn6FAgXg</td>\n",
       "      <td>1</td>\n",
       "      <td>Disgusting!  Had a Groupon so my daughter and ...</td>\n",
       "      <td>review</td>\n",
       "      <td>8AMn6644NmBf96xGO3w6OA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>o1GIYYZJjM6nM03fQs_uEQ</td>\n",
       "      <td>2011-11-30</td>\n",
       "      <td>ApKbwpYJdnhhgP4NbjQw2Q</td>\n",
       "      <td>1</td>\n",
       "      <td>I've eaten here many times, but none as bad as...</td>\n",
       "      <td>review</td>\n",
       "      <td>iwUN95LIaEr75TZE_JC6bg</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>l4vBbCL9QbGiwLuLKwD_bA</td>\n",
       "      <td>2011-11-22</td>\n",
       "      <td>DJVxOfj2Rw9zklC9tU3i1w</td>\n",
       "      <td>1</td>\n",
       "      <td>I have always been a fan of Burlington's deals...</td>\n",
       "      <td>review</td>\n",
       "      <td>EPROVap0M19Y6_4uf3eCmQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>CEswyP-9SsXRNLR9fFGKKw</td>\n",
       "      <td>2012-05-19</td>\n",
       "      <td>GXj4PNAi095-q9ynPYH3kg</td>\n",
       "      <td>1</td>\n",
       "      <td>Another night meeting friends here.  I have to...</td>\n",
       "      <td>review</td>\n",
       "      <td>MjLAe48XNfYlTeFYca5gMw</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9924</th>\n",
       "      <td>mhQCxOiqp03qnhGRTtPduw</td>\n",
       "      <td>2012-07-07</td>\n",
       "      <td>PCjnn__E8AzNa2kXFXxTHQ</td>\n",
       "      <td>1</td>\n",
       "      <td>Horrible service.</td>\n",
       "      <td>review</td>\n",
       "      <td>rvaJcf5OIY2So_pkoqDHLQ</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9931</th>\n",
       "      <td>C_eWAEOvkHZ_IZYGwjtpmg</td>\n",
       "      <td>2011-04-25</td>\n",
       "      <td>QfdoE-XqEeCsttJH8g0ugQ</td>\n",
       "      <td>1</td>\n",
       "      <td>I love Saddle Ranch for its casual night time ...</td>\n",
       "      <td>review</td>\n",
       "      <td>yhGfcKunPjcy-v-1ppr6oQ</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9953</th>\n",
       "      <td>QCxXYA13PtkD3wec47_r8Q</td>\n",
       "      <td>2012-03-14</td>\n",
       "      <td>V2N2TCqnaqMYfg7zXGG-Rg</td>\n",
       "      <td>1</td>\n",
       "      <td>\"Hipster,Trendy\" ????-I think NOT !!!! Very di...</td>\n",
       "      <td>review</td>\n",
       "      <td>JEQ6el2-tLtKJU6k_SpE-w</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>wepFVY82q_tuDzG6lQjHWw</td>\n",
       "      <td>2012-02-12</td>\n",
       "      <td>spusZYROtBKw_5tv3gYm4Q</td>\n",
       "      <td>1</td>\n",
       "      <td>Went last night to Whore Foods to get basics t...</td>\n",
       "      <td>review</td>\n",
       "      <td>W7zmm1uzlyUkEqpSG7PlBw</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>r-a-Cn9hxdEnYTtVTB5bMQ</td>\n",
       "      <td>2012-04-07</td>\n",
       "      <td>j9HwZZoBBmJgOlqDSuJcxg</td>\n",
       "      <td>1</td>\n",
       "      <td>The food is delicious.  The service:  discrimi...</td>\n",
       "      <td>review</td>\n",
       "      <td>toPtsUtYoRB-5-ThrOy2Fg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>749 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 business_id        date               review_id  stars  \\\n",
       "23    IJ0o6b8bJFAbG6MjGfBebQ  2010-09-05  Dx9sfFU6Zn0GYOckijom-g      1   \n",
       "31    vvA3fbps4F9nGlAEYKk_sA  2012-05-04  S9OVpXat8k5YwWCn6FAgXg      1   \n",
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       "61    l4vBbCL9QbGiwLuLKwD_bA  2011-11-22  DJVxOfj2Rw9zklC9tU3i1w      1   \n",
       "64    CEswyP-9SsXRNLR9fFGKKw  2012-05-19  GXj4PNAi095-q9ynPYH3kg      1   \n",
       "...                      ...         ...                     ...    ...   \n",
       "9924  mhQCxOiqp03qnhGRTtPduw  2012-07-07  PCjnn__E8AzNa2kXFXxTHQ      1   \n",
       "9931  C_eWAEOvkHZ_IZYGwjtpmg  2011-04-25  QfdoE-XqEeCsttJH8g0ugQ      1   \n",
       "9953  QCxXYA13PtkD3wec47_r8Q  2012-03-14  V2N2TCqnaqMYfg7zXGG-Rg      1   \n",
       "9984  wepFVY82q_tuDzG6lQjHWw  2012-02-12  spusZYROtBKw_5tv3gYm4Q      1   \n",
       "9987  r-a-Cn9hxdEnYTtVTB5bMQ  2012-04-07  j9HwZZoBBmJgOlqDSuJcxg      1   \n",
       "\n",
       "                                                   text    type  \\\n",
       "23    U can go there n check the car out. If u wanna...  review   \n",
       "31    Disgusting!  Had a Groupon so my daughter and ...  review   \n",
       "35    I've eaten here many times, but none as bad as...  review   \n",
       "61    I have always been a fan of Burlington's deals...  review   \n",
       "64    Another night meeting friends here.  I have to...  review   \n",
       "...                                                 ...     ...   \n",
       "9924                                  Horrible service.  review   \n",
       "9931  I love Saddle Ranch for its casual night time ...  review   \n",
       "9953  \"Hipster,Trendy\" ????-I think NOT !!!! Very di...  review   \n",
       "9984  Went last night to Whore Foods to get basics t...  review   \n",
       "9987  The food is delicious.  The service:  discrimi...  review   \n",
       "\n",
       "                     user_id  cool  useful  funny  length  \n",
       "23    zRlQEDYd_HKp0VS3hnAffA     0       1      1     594  \n",
       "31    8AMn6644NmBf96xGO3w6OA     0       1      0     361  \n",
       "35    iwUN95LIaEr75TZE_JC6bg     0       4      3    1198  \n",
       "61    EPROVap0M19Y6_4uf3eCmQ     0       0      0     569  \n",
       "64    MjLAe48XNfYlTeFYca5gMw     0       1      2     498  \n",
       "...                      ...   ...     ...    ...     ...  \n",
       "9924  rvaJcf5OIY2So_pkoqDHLQ     0       1      0      17  \n",
       "9931  yhGfcKunPjcy-v-1ppr6oQ     0       2      0     995  \n",
       "9953  JEQ6el2-tLtKJU6k_SpE-w     0       2      0     296  \n",
       "9984  W7zmm1uzlyUkEqpSG7PlBw     0       1      2    1673  \n",
       "9987  toPtsUtYoRB-5-ThrOy2Fg     0       0      0     200  \n",
       "\n",
       "[749 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onestar_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fivestar_reviews = dataset[dataset['stars'] == 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
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       "</table>\n",
       "<p>3337 rows × 11 columns</p>\n",
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       "                 business_id        date               review_id  stars  \\\n",
       "0     9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
       "1     ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
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       "4     6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
       "6     zp713qNhx8d9KCJJnrw1xA  2010-02-12  riFQ3vxNpP4rWLk_CSri2A      5   \n",
       "...                      ...         ...                     ...    ...   \n",
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       "9999  pF7uRzygyZsltbmVpjIyvw  2010-10-16  vWSmOhg2ID1MNZHaWapGbA      5   \n",
       "\n",
       "                                                   text    type  \\\n",
       "0     My wife took me here on my birthday for breakf...  review   \n",
       "1     I have no idea why some people give bad review...  review   \n",
       "3     Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
       "4     General Manager Scott Petello is a good egg!!!...  review   \n",
       "6     Drop what you're doing and drive here. After I...  review   \n",
       "...                                                 ...     ...   \n",
       "9990  Yes I do rock the hipster joints.  I dig this ...  review   \n",
       "9991  Only 4 stars? \\n\\n(A few notes: The folks that...  review   \n",
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       "9994  Let's see...what is there NOT to like about Su...  review   \n",
       "9999  4-5 locations.. all 4.5 star average.. I think...  review   \n",
       "\n",
       "                     user_id  cool  useful  funny  length  \n",
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       "6     wFweIWhv2fREZV_dYkz_1g     7       7      4    1565  \n",
       "...                      ...   ...     ...    ...     ...  \n",
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       "9991  hTau-iNZFwoNsPCaiIUTEA     1       1      0     908  \n",
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       "9999  KSBFytcdjPKZgXKQnYQdkA     0       0      0     461  \n",
       "\n",
       "[3337 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fivestar_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "extreme_reviews = pd.concat([onestar_reviews,fivestar_reviews])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>WJ5mq4EiWYAA4Vif0xDfdg</td>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>EuHX-39FR7tyyG1ElvN1Jw</td>\n",
       "      <td>5</td>\n",
       "      <td>Only 4 stars? \\n\\n(A few notes: The folks that...</td>\n",
       "      <td>review</td>\n",
       "      <td>hTau-iNZFwoNsPCaiIUTEA</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>f96lWMIAUhYIYy9gOktivQ</td>\n",
       "      <td>2009-03-10</td>\n",
       "      <td>YF17z7HWlMj6aezZc-pVEw</td>\n",
       "      <td>5</td>\n",
       "      <td>I'm not normally one to jump at reviewing a ch...</td>\n",
       "      <td>review</td>\n",
       "      <td>W_QXYA7A0IhMrvbckz7eVg</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>L3BSpFvxcNf3T_teitgt6A</td>\n",
       "      <td>2012-03-19</td>\n",
       "      <td>0nxb1gIGFgk3WbC5zwhKZg</td>\n",
       "      <td>5</td>\n",
       "      <td>Let's see...what is there NOT to like about Su...</td>\n",
       "      <td>review</td>\n",
       "      <td>OzOZv-Knlw3oz9K5Kh5S6A</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>pF7uRzygyZsltbmVpjIyvw</td>\n",
       "      <td>2010-10-16</td>\n",
       "      <td>vWSmOhg2ID1MNZHaWapGbA</td>\n",
       "      <td>5</td>\n",
       "      <td>4-5 locations.. all 4.5 star average.. I think...</td>\n",
       "      <td>review</td>\n",
       "      <td>KSBFytcdjPKZgXKQnYQdkA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4086 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 business_id        date               review_id  stars  \\\n",
       "23    IJ0o6b8bJFAbG6MjGfBebQ  2010-09-05  Dx9sfFU6Zn0GYOckijom-g      1   \n",
       "31    vvA3fbps4F9nGlAEYKk_sA  2012-05-04  S9OVpXat8k5YwWCn6FAgXg      1   \n",
       "35    o1GIYYZJjM6nM03fQs_uEQ  2011-11-30  ApKbwpYJdnhhgP4NbjQw2Q      1   \n",
       "61    l4vBbCL9QbGiwLuLKwD_bA  2011-11-22  DJVxOfj2Rw9zklC9tU3i1w      1   \n",
       "64    CEswyP-9SsXRNLR9fFGKKw  2012-05-19  GXj4PNAi095-q9ynPYH3kg      1   \n",
       "...                      ...         ...                     ...    ...   \n",
       "9990  R8VwdLyvsp9iybNqRvm94g  2011-10-03  pcEeHdAJPoFNF23es0kKWg      5   \n",
       "9991  WJ5mq4EiWYAA4Vif0xDfdg  2011-12-05  EuHX-39FR7tyyG1ElvN1Jw      5   \n",
       "9992  f96lWMIAUhYIYy9gOktivQ  2009-03-10  YF17z7HWlMj6aezZc-pVEw      5   \n",
       "9994  L3BSpFvxcNf3T_teitgt6A  2012-03-19  0nxb1gIGFgk3WbC5zwhKZg      5   \n",
       "9999  pF7uRzygyZsltbmVpjIyvw  2010-10-16  vWSmOhg2ID1MNZHaWapGbA      5   \n",
       "\n",
       "                                                   text    type  \\\n",
       "23    U can go there n check the car out. If u wanna...  review   \n",
       "31    Disgusting!  Had a Groupon so my daughter and ...  review   \n",
       "35    I've eaten here many times, but none as bad as...  review   \n",
       "61    I have always been a fan of Burlington's deals...  review   \n",
       "64    Another night meeting friends here.  I have to...  review   \n",
       "...                                                 ...     ...   \n",
       "9990  Yes I do rock the hipster joints.  I dig this ...  review   \n",
       "9991  Only 4 stars? \\n\\n(A few notes: The folks that...  review   \n",
       "9992  I'm not normally one to jump at reviewing a ch...  review   \n",
       "9994  Let's see...what is there NOT to like about Su...  review   \n",
       "9999  4-5 locations.. all 4.5 star average.. I think...  review   \n",
       "\n",
       "                     user_id  cool  useful  funny  length  \n",
       "23    zRlQEDYd_HKp0VS3hnAffA     0       1      1     594  \n",
       "31    8AMn6644NmBf96xGO3w6OA     0       1      0     361  \n",
       "35    iwUN95LIaEr75TZE_JC6bg     0       4      3    1198  \n",
       "61    EPROVap0M19Y6_4uf3eCmQ     0       0      0     569  \n",
       "64    MjLAe48XNfYlTeFYca5gMw     0       1      2     498  \n",
       "...                      ...   ...     ...    ...     ...  \n",
       "9990  b92Y3tyWTQQZ5FLifex62Q     1       1      1     263  \n",
       "9991  hTau-iNZFwoNsPCaiIUTEA     1       1      0     908  \n",
       "9992  W_QXYA7A0IhMrvbckz7eVg     2       3      2    1326  \n",
       "9994  OzOZv-Knlw3oz9K5Kh5S6A     1       2      1    1968  \n",
       "9999  KSBFytcdjPKZgXKQnYQdkA     0       0      0     461  \n",
       "\n",
       "[4086 rows x 11 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extreme_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24839a42898>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATh0lEQVR4nO3dcayd9X3f8fcHQwlNYAFxYcY2w8rcaYamzriz2LJNSRMVJ11n0i2RI6VYLZIzRKZEyjZBJzXpImudShqVNCARhWKnWajXlOG10JVa6aJsNO51SjA2YbECA8cedpKmMZ3m1c53f5yflTP72L8L+Ln3mvt+SY/Oc77n+T33e6Qrf/w8v985N1WFJElnct58NyBJWvgMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdQ0WFklek2Rnkq8l2ZPkl1v9o0m+leTxtr1zbMwdSfYleTrJjWP165Psbq/dlSRD9S1JOlWG+pxF+wf9tVX1YpILgC8DHwTWAS9W1Z0nHb8a+DywFrgK+CPgx6rqeJKdbeyfAA8Dd1XVI4M0Lkk6xWBXFjXyYnt6QdvOlEzrgQeq6mhVPQPsA9YmWQpcUlWP1SjZtgI3DdW3JOlU5w958iRLgF3A3wQ+VVVfSfIO4ANJbgZmgA9X1Z8DyxhdOZywv9X+qu2fXD+jyy+/vK655pqz8j4kabHYtWvXt6tq6uT6oGFRVceBNUleDzyY5DrgHuBjjK4yPgZ8HPgFYNI8RJ2hfookm4BNAFdffTUzMzOv+D1I0mKS5H9Oqs/Jaqiq+h7wx8C6qnqhqo5X1Q+ATzOao4DRFcOKsWHLgQOtvnxCfdLPubeqpqtqemrqlGCUJL1MQ66GmmpXFCS5CHg78PU2B3HCu4An2/52YEOSC5OsBFYBO6vqIHAkyQ1t0vxm4KGh+pYknWrI21BLgS1t3uI8YFtV/V6SzyZZw+hW0rPA+wGqak+SbcBe4BhwW7uNBXArcD9wEfBI2yRJc2SwpbPzbXp6upyzkKSXJsmuqpo+ue4nuCVJXYaFJKnLsJAkdRkWkqQuw0KS1DXoJ7glDeO5f/vj892CFqCrf2n3YOf2ykKS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpK7BwiLJa5LsTPK1JHuS/HKrX5bk0STfaI+Xjo25I8m+JE8nuXGsfn2S3e21u5JkqL4lSaca8sriKPCTVfUTwBpgXZIbgNuBHVW1CtjRnpNkNbABuBZYB9ydZEk71z3AJmBV29YN2Lck6SSDhUWNvNieXtC2AtYDW1p9C3BT218PPFBVR6vqGWAfsDbJUuCSqnqsqgrYOjZGkjQHBp2zSLIkyePAIeDRqvoKcGVVHQRoj1e0w5cBz48N399qy9r+yXVJ0hwZNCyq6nhVrQGWM7pKuO4Mh0+ah6gz1E89QbIpyUySmcOHD7/0hiVJE83Jaqiq+h7wx4zmGl5ot5Zoj4faYfuBFWPDlgMHWn35hPqkn3NvVU1X1fTU1NRZfQ+StJgNuRpqKsnr2/5FwNuBrwPbgY3tsI3AQ21/O7AhyYVJVjKayN7ZblUdSXJDWwV189gYSdIcOH/Acy8FtrQVTecB26rq95I8BmxLcgvwHPBugKrak2QbsBc4BtxWVcfbuW4F7gcuAh5pmyRpjgwWFlX1BPCmCfXvAG87zZjNwOYJ9RngTPMdkqQB+QluSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkrsHCIsmKJF9M8lSSPUk+2OofTfKtJI+37Z1jY+5Isi/J00luHKtfn2R3e+2uJBmqb0nSqc4f8NzHgA9X1VeTXAzsSvJoe+0TVXXn+MFJVgMbgGuBq4A/SvJjVXUcuAfYBPwJ8DCwDnhkwN4lSWMGu7KoqoNV9dW2fwR4Clh2hiHrgQeq6mhVPQPsA9YmWQpcUlWPVVUBW4GbhupbknSqOZmzSHIN8CbgK630gSRPJLkvyaWttgx4fmzY/lZb1vZPrkuS5sjgYZHkdcAXgA9V1fcZ3VJ6A7AGOAh8/MShE4bXGeqTftamJDNJZg4fPvyKe5ckjQwaFkkuYBQUn6uq3wWoqheq6nhV/QD4NLC2Hb4fWDE2fDlwoNWXT6ifoqrurarpqpqempo6u29GkhaxIVdDBfgM8FRV/dpYfenYYe8Cnmz724ENSS5MshJYBeysqoPAkSQ3tHPeDDw0VN+SpFMNuRrqzcDPAbuTPN5qvwi8N8kaRreSngXeD1BVe5JsA/YyWkl1W1sJBXArcD9wEaNVUK6EkqQ5NFhYVNWXmTzf8PAZxmwGNk+ozwDXnb3uJEkvhZ/gliR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6hosLJKsSPLFJE8l2ZPkg61+WZJHk3yjPV46NuaOJPuSPJ3kxrH69Ul2t9fuSpKh+pYknWrIK4tjwIer6m8DNwC3JVkN3A7sqKpVwI72nPbaBuBaYB1wd5Il7Vz3AJuAVW1bN2DfkqSTDBYWVXWwqr7a9o8ATwHLgPXAlnbYFuCmtr8eeKCqjlbVM8A+YG2SpcAlVfVYVRWwdWyMJGkOzMmcRZJrgDcBXwGurKqDMAoU4Ip22DLg+bFh+1ttWds/uS5JmiODh0WS1wFfAD5UVd8/06ETanWG+qSftSnJTJKZw4cPv/RmJUkTDRoWSS5gFBSfq6rfbeUX2q0l2uOhVt8PrBgbvhw40OrLJ9RPUVX3VtV0VU1PTU2dvTciSYvckKuhAnwGeKqqfm3spe3Axra/EXhorL4hyYVJVjKayN7ZblUdSXJDO+fNY2MkSXPg/AHP/Wbg54DdSR5vtV8EfgXYluQW4Dng3QBVtSfJNmAvo5VUt1XV8TbuVuB+4CLgkbZJkubIrMIiyY6qeluvNq6qvszk+QaAieOqajOweUJ9BrhuNr1Kks6+M4ZFktcAPwpc3j48d+If/0uAqwbuTZK0QPSuLN4PfIhRMOzih2HxfeBTA/YlSVpAzhgWVfXrwK8n+RdV9ck56kmStMDMas6iqj6Z5O8D14yPqaqtA/UlSVpAZjvB/VngDcDjwIkVSie+ekOS9Co326Wz08Dq9t1MkqRFZrYfynsS+OtDNiJJWrhme2VxObA3yU7g6IliVf2TQbqSJC0osw2Ljw7ZhCRpYZvtaqj/OnQjkqSFa7aroY7ww68F/xHgAuAvq+qSoRqTJC0cs72yuHj8eZKbgLWDdCRJWnBe1leUV9V/An7yLPciSVqgZnsb6mfHnp7H6HMXfuZCkhaJ2a6G+pmx/WPAs8D6s96NJGlBmu2cxc8P3YgkaeGa1ZxFkuVJHkxyKMkLSb6QZHl/pCTp1WC2E9y/yehvZF8FLAP+c6tJkhaB2YbFVFX9ZlUda9v9wNSAfUmSFpDZhsW3k7wvyZK2vQ/4zpCNSZIWjtmGxS8A7wH+F3AQ+GeAk96StEjMdunsx4CNVfXnAEkuA+5kFCKSpFe52V5ZvPFEUABU1XeBN51pQJL72uqpJ8dqH03yrSSPt+2dY6/dkWRfkqeT3DhWvz7J7vbaXUky+7cnSTobZhsW5yW59MSTdmXRuyq5H1g3of6JqlrTtofb+VYDG4Br25i7kyxpx98DbAJWtW3SOSVJA5rtbaiPA/89ye8w+pqP9wCbzzSgqr6U5JpZnn898EBVHQWeSbIPWJvkWeCSqnoMIMlW4CbgkVmeV5J0FszqyqKqtgL/FHgBOAz8bFV99mX+zA8keaLdpjpxtbIMeH7smP2ttqztn1yXJM2hWX/rbFXtrarfqKpPVtXel/nz7gHeAKxhtKrq460+aR6izlCfKMmmJDNJZg4fPvwyW5QknexlfUX5y1VVL1TV8ar6AfBpfvg3MfYDK8YOXQ4caPXlE+qnO/+9VTVdVdNTU35mUJLOljkNiyRLx56+CzixUmo7sCHJhUlWMprI3llVB4EjSW5oq6BuBh6ay54lSbOf4H7JknweeAtweZL9wEeAtyRZw+hW0rPA+wGqak+SbcBeRl+BfltVHW+nupXRyqqLGE1sO7ktSXNssLCoqvdOKH/mDMdvZsIKq6qaAa47i61Jkl6iOb0NJUk6NxkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoaLCyS3JfkUJInx2qXJXk0yTfa46Vjr92RZF+Sp5PcOFa/Psnu9tpdSTJUz5KkyYa8srgfWHdS7XZgR1WtAna05yRZDWwArm1j7k6ypI25B9gErGrbyeeUJA1ssLCoqi8B3z2pvB7Y0va3ADeN1R+oqqNV9QywD1ibZClwSVU9VlUFbB0bI0maI3M9Z3FlVR0EaI9XtPoy4Pmx4/a32rK2f3JdkjSHFsoE96R5iDpDffJJkk1JZpLMHD58+Kw1J0mL3VyHxQvt1hLt8VCr7wdWjB23HDjQ6ssn1Ceqqnurarqqpqemps5q45K0mM11WGwHNrb9jcBDY/UNSS5MspLRRPbOdqvqSJIb2iqom8fGSJLmyPlDnTjJ54G3AJcn2Q98BPgVYFuSW4DngHcDVNWeJNuAvcAx4LaqOt5OdSujlVUXAY+0TZI0hwYLi6p672leettpjt8MbJ5QnwGuO4utSZJeooUywS1JWsAMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUtdgX/dxrrv+X22d7xa0AO361ZvnuwVpXnhlIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqWtewiLJs0l2J3k8yUyrXZbk0STfaI+Xjh1/R5J9SZ5OcuN89CxJi9l8Xlm8tarWVNV0e347sKOqVgE72nOSrAY2ANcC64C7kyyZj4YlabFaSLeh1gNb2v4W4Kax+gNVdbSqngH2AWvnoT9JWrTmKywK+MMku5JsarUrq+ogQHu8otWXAc+Pjd3fapKkOTJff8/izVV1IMkVwKNJvn6GYzOhVhMPHAXPJoCrr776lXcpSQLm6cqiqg60x0PAg4xuK72QZClAezzUDt8PrBgbvhw4cJrz3ltV01U1PTU1NVT7krTozHlYJHltkotP7AM/BTwJbAc2tsM2Ag+1/e3AhiQXJlkJrAJ2zm3XkrS4zcdtqCuBB5Oc+Pn/oar+IMmfAtuS3AI8B7wboKr2JNkG7AWOAbdV1fF56FuSFq05D4uq+ibwExPq3wHedpoxm4HNA7cmSTqNhbR0VpK0QBkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeo6Z8IiybokTyfZl+T2+e5HkhaTcyIskiwBPgW8A1gNvDfJ6vntSpIWj3MiLIC1wL6q+mZV/V/gAWD9PPckSYvGuRIWy4Dnx57vbzVJ0hw4f74bmKVMqNUpByWbgE3t6YtJnh60q8XjcuDb893EQpA7N853CzqVv58nfGTSP5Uv2d+YVDxXwmI/sGLs+XLgwMkHVdW9wL1z1dRikWSmqqbnuw9pEn8/58a5chvqT4FVSVYm+RFgA7B9nnuSpEXjnLiyqKpjST4A/BdgCXBfVe2Z57YkadE4J8ICoKoeBh6e7z4WKW/taSHz93MOpOqUeWJJkv4/58qchSRpHp0zt6E095LcB/xj4FBVXTff/UjjkjwLHAGOA8dcETUsb0PptJL8I+BFYKthoYWmhcV0VfkZizngbSidVlV9CfjufPchaf4ZFpLOVQX8YZJd7dsbNCDnLCSdq95cVQeSXAE8muTr7WpYA/DKQtI5qaoOtMdDwIOMvp1aAzEsJJ1zkrw2ycUn9oGfAp6c365e3QwLnVaSzwOPAX8ryf4kt8x3T1JzJfDlJF8DdgK/X1V/MM89vaq5dFaS1OWVhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLaQBJPpTkR+e7D+lscemsNICX842oSZZU1fHhupJePr8bSnqF2ieItwHLGf2N+P8IXAV8Mcm3q+qtSe4B/i5wEfA7VfWRNvZZ4D5Gn0D+jfY9R/8cOAbsraoNc/1+pEkMC+mVWwccqKqfBkjy14CfB946dmXxb6rqu0mWADuSvLGqnmiv/Z+q+gdt7AFgZVUdTfL6OX4f0mk5ZyG9cruBtyf590n+YVX9xYRj3pPkq8CfAdcCq8de++2x/SeAzyV5H6OrC2lBMCykV6iq/gdwPaPQ+HdJfmn89SQrgX8JvK2q3gj8PvCasUP+cmz/p4FPtfPtSuLVvxYEw0J6hZJcBfzvqvot4E7g7zD629AXt0MuYRQIf5HkSuAdpznPecCKqvoi8K+B1wOvG7h9aVb8X4v0yv048KtJfgD8FXAr8PeAR5IcbBPcfwbsAb4J/LfTnGcJ8FttziPAJ6rqe8O3L/W5dFaS1OVtKElSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6/h+Zn3WA0jMj0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(extreme_reviews['stars'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n",
      "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n"
     ]
    }
   ],
   "source": [
    "punctuations = string.punctuation\n",
    "english_stopwords = stopwords.words('english')\n",
    "print(english_stopwords)\n",
    "print(punctuations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removepunctuations(text):\n",
    "    punctuations = string.punctuation\n",
    "    text = [ch for ch in text if ch not in punctuations]\n",
    "    return ''.join(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string = \" Hey ! This is Nikhil Kumar Ghanghor !! Hope you all are doing good . Would like to see you soon after this pandemic ends.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string = removepunctuations(test_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Hey  This is Nikhil Kumar Ghanghor  Hope you all are doing good  Would like to see you soon after this pandemic ends'"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removestopwords(text,stopwords):\n",
    "    text = [word for word in text.split() if word.lower() not in stopwords]\n",
    "    return ' '.join(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string = removestopwords(test_string,english_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hey Nikhil Kumar Ghanghor Hope good Would like see soon pandemic ends'"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_arr = [\n",
    "            'Hey!how r u? hope everything is fine. text me when u get free.',\n",
    "            'Hi , I am missing you a lot these days . Would like to talk to you if you ever have time please do text.'\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(test_arr)):\n",
    "    test_arr[i] = removestopwords( removepunctuations( test_arr[i] ) , english_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer()\n",
    "X = vectorizer.fit_transform(test_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['days', 'ever', 'everything', 'fine', 'free', 'get', 'heyhow', 'hi', 'hope', 'like', 'lot', 'missing', 'please', 'talk', 'text', 'time', 'would']\n"
     ]
    }
   ],
   "source": [
    "print(vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0]\n",
      " [1 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(X.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_cleansing(text):\n",
    "    text = ''.join([ ch for ch in text if ch not in punctuations])\n",
    "    text = ' '.join([ word for word in text.split() if word.lower() not in english_stopwords])\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heyhow r u hope everything fine text u get freeHi missing lot days Would like talk ever time please text\n"
     ]
    }
   ],
   "source": [
    "print(text_cleansing(test_arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_preprocessed = extreme_reviews['text'].apply(text_cleansing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23      U go n check car u wanna buy 1 Thats wrong mov...\n",
       "31      Disgusting Groupon daughter tried outdated gau...\n",
       "35      Ive eaten many times none bad last night Servi...\n",
       "61      always fan Burlingtons deals however shopping ...\n",
       "64      Another night meeting friends laugh Waited ano...\n",
       "                              ...                        \n",
       "9990    Yes rock hipster joints dig place little bit s...\n",
       "9991    4 stars notes folks rated place low must isola...\n",
       "9992    Im normally one jump reviewing chain restauran...\n",
       "9994    Lets seewhat like Surprise Stadium Well 950 ta...\n",
       "9999    45 locations 45 star average think Arizona rea...\n",
       "Name: text, Length: 4086, dtype: object"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer()\n",
    "X = vectorizer.fit_transform(dataset_preprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['00s', '01042012', '022010', '03342', '05', '050', '06', '07092008', '075', '09', '090', '095', '10', '100', '1000', '10000', '1000am', '1000pm', '1000x', '1001', '100150', '1007', '100s', '100th', '101', '1011', '1012', '101412', '1015', '102', '1025', '1030', '103011', '1030pm', '10312012', '105', '1070', '108', '109', '1095', '1099', '10am', '10am4pm', '10close', '10each', '10gallon', '10ish', '10min', '10mins', '10minutes', '10person', '10pm', '10pm12am', '10shrimp', '10th', '10thand', '10x', '10yard', '10yo', '11', '110', '1100', '1100am', '1103', '111', '1113', '1115', '1118', '111807', '111809', '111909', '111pm', '111th', '112', '112312', '1125', '1130', '1130am', '1135', '1139', '114', '1141', '1145', '115', '1150', '1152', '1154', '1155', '115th', '116', '118', '1182010', '1199', '11991079', '11a3p', '11am', '11ish', '11person', '11pm', '12', '120', '1200', '12000', '1205', '1207', '120year', '121007', '121107', '121207', '1213', '121407', '1216', '122111', '122112', '1230', '1230p', '123110', '1232', '123200', '1240', '1244', '125', '1250', '12712', '128i', '129', '1295', '12am', '12maybe', '12oz', '12pm', '12th', '12year', '12yr', '13', '130', '1300', '131415', '13331', '1361', '13are', '13dont', '13person', '13th', '13years', '14', '140', '1400', '1417', '1418', '147', '149', '1495', '1499', '1499lb', '14half', '14lbs', '15', '150', '1500', '15000', '150200', '150mm', '1520', '1520min', '1525', '1530', '1564', '157', '15plate', '15th', '15yard', '16', '160', '1600', '162', '165', '1650', '169', '1699', '16th', '17', '1700', '1719', '175', '17th', '18', '180', '1800', '1800s', '185', '1850', '1895', '1895s', '1899', '18lb', '18th', '19', '1900', '1900s', '1913', '1928', '1929', '1930s', '1940s', '195', '1952', '1953', '1955', '1956', '1960s', '1961', '1968', '1969', '1970', '1978', '1980', '1980s', '1987', '199', '1990', '1990s', '1992', '1995', '1996', '1997', '1998', '1999', '199not', '19th', '1cent', '1full', '1i', '1k', '1perscoop', '1pm', '1st', '1to1', '20', '200', '2000', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '200lbs', '2010', '2011', '2012', '2012they', '202', '2025', '2030', '2038', '205', '20mbs', '20miles', '20min', '20minutes', '20s', '20something', '20th', '20xs', '21', '2100', '214', '215', '21but', '21st', '21we', '22', '220', '22312', '2240', '225', '2253', '2295', '22oz', '23', '230', '235', '24', '241', '2412', '247', '249', '249449', '2499', '24and', '24hrs', '24st', '24th', '25', '250', '2500', '25000', '250400', '2540', '25min', '25person', '25th', '26', '260', '2600', '2608', '26th', '26whole', '27', '27243', '275', '27th', '28', '285', '29', '295', '299', '2995', '2998', '2999', '29th', '2am', '2fresh', '2i', '2mbps', '2nd', '2pm', '2pound', '2rd', '2top', '2topping', '2wice', '2x', '30', '300', '3000', '300degree', '3035', '3040', '3045', '3080', '30extra', '30s', '30somethings', '30th', '30years', '311', '3122669', '315', '316', '32', '3200', '32010because', '325', '328', '32s', '33', '330', '330pm', '33rd', '34', '3400', '34000', '345pm', '349', '3499', '34th', '35', '350', '350ib', '3540', '35c', '35th', '36', '369', '36pm', '37', '370', '375', '3755', '379', '38', '389', '38th', '39', '3900', '394', '398', '399', '39th', '3also', '3am', '3bean', '3d', '3egg', '3g', '3i', '3k', '3lbs', '3p6p', '3pm', '3rd', '3stars', '3x', '3xwk', '3xyear', '3year', '40', '400', '4000', '400a', '400s', '4050', '40ish', '40lm', '40min', '40th', '40yearold', '40years', '41', '411', '415', '41711', '42', '4200', '420000', '425', '43', '430', '430530', '430pm', '435', '4499', '44th', '45', '450', '450600', '450ish', '455', '45min', '46', '46th', '47', '4730pm', '475', '48', '480', '48th', '49', '490', '491', '495', '499', '4b', '4hr', '4little', '4pack', '4pm', '4pm7pm', '4s', '4star', '4th', '4that', '4x6', '50', '500', '5000', '50000', '500pm', '5060', '5070', '50cents', '50lm', '50s', '50thats', '51', '510', '515', '517', '517p', '52', '520', '52010', '5231', '525', '528', '53', '530', '530p', '530pm', '54', '545', '549', '55', '550', '552', '56', '565', '57', '575', '58', '595', '599', '5995', '59th', '5k', '5make', '5min', '5p', '5pm', '5star', '5stars', '5th', '5yo', '60', '600', '6000', '60000', '600am', '600pm', '601', '602', '6080', '60miles', '60s', '610', '612', '615pm', '618', '61st', '620', '62010', '620pm', '623', '6235474655', '6239774458', '625', '63', '630', '630am', '64', '642', '64ish', '64th', '65', '650', '653pm', '65person', '66', '6600', '67', '67000', '68', '69', '699', '6am', '6pack', '6pm', '6th', '70', '700', '700am', '70lb', '70s', '70th', '71', '710', '711', '711ish', '715', '719', '71st', '725', '730', '730ish', '730pm', '74th', '75', '750', '7500', '76', '775', '78', '7807', '79', '7am', '7am6pm', '7am9pm', '7pm', '7th', '80', '800', '8000hp', '800am', '8020', '80s', '81', '810', '811', '812', '8132010', '814', '815', '815am', '816', '819', '820', '82012', '83', '830', '830pm', '832we', '83rd', '845', '849', '85', '850', '85340', '85year', '86', '875', '88', '89', '895', '899', '8am', '8amagain', '8close', '8pm', '8pm10pm', '8th', '8v', '8x10', '8yo', '90', '900', '9000', '90lower', '90minute', '90s', '91', '911', '915', '925', '92nd', '93', '930', '930am', '945', '945am', '95', '950', '950ea', '951pm', '96', '975', '98', '99', '995', '9975', '999', '99999', '999for', '99cent', '9am', '9oz', '9p11p', '9pm', '9th', '9year', '9yo', 'a1', 'a2', 'aa', 'aaa', 'aaaamazing', 'aaammmazzing', 'aaron', 'ab', 'aback', 'abandoned', 'abandoning', 'abbazabba', 'abbreviate', 'abbreviated', 'abby', 'abc', 'abdomen', 'abhor', 'abiding', 'abilities', 'ability', 'abilty', 'able', 'abodoba', 'abou', 'abound', 'aboutseriously', 'abovehaving', 'abrasion', 'abroad', 'abrupt', 'absence', 'absent', 'absinthe', 'absoloutely', 'absolut', 'absolute', 'absolutely', 'absolutley', 'absolutly', 'absorb', 'absorption', 'abstain', 'abstained', 'abstract', 'absurd', 'absurd8', 'abuelos', 'abuelosit', 'abundance', 'abundant', 'abuse', 'abused', 'abusive', 'abysmal', 'ac', 'acacia', 'academy', 'acai', 'acapulco', 'accent', 'accented', 'accents', 'accept', 'acceptable', 'accepted', 'accepting', 'accepts', 'access', 'accessed', 'accessible', 'accessories', 'accessorize', 'accessory', 'accident', 'accidental', 'accidentally', 'accidently', 'acclaimed', 'accolades', 'accommodate', 'accommodated', 'accommodates', 'accommodating', 'accommodation', 'accommodations', 'accomodate', 'accomodated', 'accomodating', 'accompanied', 'accompanies', 'accompaniment', 'accompany', 'accompanying', 'accomplish', 'accomplished', 'accomplishment', 'according', 'accordingly', 'account', 'accountable', 'accounts', 'accoutrements', 'accredited', 'accross', 'accumulated', 'accurate', 'accurately', 'accusation', 'accused', 'accustom', 'accustomed', 'accutemp', 'ace', 'ache', 'acheat', 'aches', 'achieve', 'achieved', 'achievement', 'acid', 'acidic', 'acknowledge', 'acknowledged', 'acknowledgement', 'acknowledging', 'ackward', 'acne', 'acoustic', 'acoustics', 'acquaintance', 'acquaintence', 'acquired', 'acres', 'acrid', 'across', 'acrylics', 'act', 'acted', 'acting', 'action', 'actions', 'activation', 'active', 'activehealthy', 'actively', 'activism', 'activities', 'activitiessuch', 'activity', 'actor', 'actors', 'acts', 'actual', 'actuality', 'actually', 'actualy', 'actully', 'acute', 'acy', 'ad', 'adage', 'adam', 'adamant', 'adams', 'adapter', 'add', 'addd', 'added', 'addendum', 'addict', 'addicted', 'addicting', 'addictingly', 'addiction', 'addictionand', 'addictive', 'addicts', 'adding', 'addition', 'additional', 'additionally', 'additions', 'addon', 'addperhaps', 'address', 'addressed', 'addressing', 'addressweb', 'adds', 'adelman', 'adequate', 'adequately', 'adios', 'adjacent', 'adjoining', 'adjust', 'adjusted', 'adjustment', 'adjustments', 'administrative', 'admire', 'admired', 'admiring', 'admission', 'admit', 'admits', 'admitted', 'admittedly', 'admitting', 'admonishment', 'ado', 'adobada', 'adobe', 'adobo', 'adolescence', 'adopt', 'adopted', 'adoption', 'adoptions', 'adorable', 'adoration', 'adore', 'adorned', 'adorning', 'adovada', 'adquate', 'adrian', 'adrianne', 'adrienne', 'ads', 'adult', 'adulthood', 'adults', 'advance', 'advanced', 'advancing', 'advantage', 'advantages', 'adventure', 'adventures', 'adventurous', 'adversity', 'advertise', 'advertised', 'advertisement', 'advertisements', 'advertising', 'adverts', 'advice', 'advicedo', 'advise', 'advised', 'advising', 'advisor', 'advisors', 'advocate', 'advocated', 'aeg', 'aerators', 'aerobic', 'aerobics', 'aeropress', 'aerosol', 'aesthetically', 'aesthetics', 'affair', 'affect', 'affected', 'affects', 'afficianados', 'affiliated', 'affiliates', 'affiliation', 'affliction', 'affluent', 'afforadable', 'afford', 'affordable', 'aficionado', 'aficionados', 'afloat', 'aforementioned', 'afoul', 'afraid', 'africa', 'afterall', 'afterbar', 'afterdark', 'afterglow', 'afternoon', 'afternoons', 'aftertaste', 'afterthought', 'afterward', 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'weird', 'weirded', 'weirdest', 'weirdly', 'weirdo', 'weirdos', 'weirdthe', 'weis', 'wekopa', 'welcome', 'welcomeand', 'welcomed', 'welcomes', 'welcoming', 'well', 'wellalso', 'wellbehaved', 'wellchilled', 'welldecorated', 'welldone', 'wellestablished', 'welleven', 'wellhard', 'wellintentioned', 'wellit', 'wellkept', 'wellknown', 'welllit', 'wellloved', 'wellmade', 'wellmaintained', 'wellmarinated', 'wellness', 'wellno', 'wellnot', 'wellof', 'wellorganized', 'wellpriced', 'wellqualified', 'wellrespected', 'wellreviewed', 'wells', 'wellseasoned', 'wellstocked', 'wellsuited', 'wellversed', 'wellwait', 'wellwritten', 'wellyes', 'wendys', 'went', 'wentmy', 'wentsome', 'werent', 'wernet', 'wes', 'west', 'western', 'westgate', 'westin', 'westminster', 'westside', 'westward', 'wet', 'wetall', 'weve', 'wfireplace', 'wfor', 'wginger', 'wguacamole', 'wha', 'whaat', 'whack', 'whacked', 'whales', 'whatever', 'whatnot', 'whats', 'whatsleftoflastseasonscollection', 'whatsoever', 'whatsosever', 'whatsover', 'wheat', 'wheatfree', 'wheel', 'wheeling', 'wheels', 'wheezed', 'whenever', 'whenharrymetsally', 'whenwhere', 'whereas', 'whered', 'wheres', 'whereupon', 'wherever', 'whether', 'whew', 'whiff', 'whileas', 'whilst', 'whim', 'whimi', 'whimper', 'whimsical', 'whined', 'whines', 'whinin', 'whining', 'whinnying', 'whip', 'whipmmmmfood', 'whipped', 'whips', 'whirl', 'whirlpool', 'whirlpools', 'whisked', 'whiskey', 'whiskeys8', 'whisper', 'whispered', 'whispers', 'whistle', 'whistlinggotta', 'white', 'whiteboard', 'whitedark', 'whiten', 'whites', 'whitesands', 'whitetrash', 'whiteys', 'whitney', 'whiz', 'whoa', 'whoah', 'whod', 'whoever', 'whole', 'wholeheartedly', 'wholesome', 'wholly', 'whoop', 'whooping', 'whopper', 'whopping', 'whore', 'whoreish', 'whorestyle', 'whoring', 'whos', 'whose', 'whyd', 'whys', 'whysee', 'wich', 'wicked', 'wicker', 'wide', 'widening', 'wider', 'widerice', 'widow', 'width', 'wiener', 'wierd', 'wife', 'wifelolsince', 'wifes', 'wifey', 'wifi', 'wig', 'wiggle', 'wigs', 'wigwam', 'wih', 'wii', 'wikipedia', 'wil', 'wild', 'wildcat', 'wilderness', 'wildest', 'wildfish', 'wildflower', 'wildflowers', 'wildhorse', 'wildlife', 'wildly', 'willamette', 'willfully', 'willie', 'willing', 'willingfully', 'willow', 'willpower', 'wills', 'willy', 'willys', 'wilma', 'wilted', 'wimp', 'wimper', 'win', 'wind', 'windex', 'winding', 'window', 'windowcan', 'windowpanes', 'windows', 'winds', 'windshield', 'windshields', 'windsor', 'windy', 'wine', 'winebeer', 'wines', 'wineyou', 'wing', 'wings', 'wining', 'wink', 'winkie', 'winkies', 'winking', 'winky', 'winner', 'winners', 'winning', 'winningsorry', 'wino', 'winos', 'wins', 'winter', 'winters', 'winwin', 'wipe', 'wiped', 'wiper', 'wipes', 'wipesorry', 'wiping', 'wire', 'wired', 'wireless', 'wires', 'wiring', 'wisconsin', 'wisconsinite', 'wisdom', 'wise', 'wiseguy', 'wisely', 'wiser', 'wish', 'wished', 'wishes', 'wishful', 'wishing', 'witches', 'withaspoon', 'withbut', 'withdrawal', 'withdrawn', 'withe', 'withheld', 'withhis', 'within', 'without', 'withstand', 'withstanding', 'witness', 'witnessed', 'witnesses', 'witty', 'wives', 'wiz', 'wizard', 'wks', 'wll', 'wm', 'wmushroom', 'wo', 'woah', 'wobblesee', 'wobbly', 'woefilled', 'woes', 'wok', 'woke', 'woken', 'wolfed', 'wolfpack', 'wolverine', 'woman', 'women', 'womens', 'wonder', 'wondered', 'wonderful', 'wonderfuland', 'wonderfully', 'wonderfulness', 'wonderfulthe', 'wondering', 'wonderland', 'wonderous', 'wonders', 'wondrous', 'wongs', 'wont', 'wonton', 'wontons', 'woo', 'wood', 'woodcarving', 'wooden', 'woodfired', 'woodlands', 'woodpaneled', 'woods', 'woodworker', 'wooed', 'woohoo', 'wool', 'wooooooooo', 'word', 'wordexcellent', 'wordis', 'wordly', 'words', 'wordsit', 'wore', 'work', 'workappropriate', 'worked', 'worker', 'workers', 'working', 'workmanship', 'workout', 'workouts', 'works', 'workschool', 'workshops', 'workstudyread', 'world', 'worldand', 'worldloli', 'worlds', 'worldtyson', 'worldwide', 'worm', 'worn', 'worried', 'worries', 'worrisome', 'worry', 'worrying', 'worse', 'worship', 'worst', 'worstershire', 'worth', 'worthless', 'worthwhile', 'worthy', 'wott', 'woudnt', 'would', 'woulda', 'wouldnt', 'wouldve', 'wound', 'wounds', 'wow', 'wowd', 'wowed', 'wowexciting', 'wowner', 'wowzer', 'wrap', 'wrapped', 'wrapper', 'wrappers', 'wrapping', 'wraps', 'wrath', 'wreck', 'wrestling', 'wrice', 'wright', 'wrigley', 'wrigleyvillechicago', 'wrinkled', 'wrinkles', 'wrinkling', 'wrist', 'wristbands', 'write', 'writer', 'writers', 'writes', 'writing', 'written', 'wrkout', 'wrong', 'wrongthings', 'wrote', 'ws', 'wt', 'wtf', 'wth', 'wthe', 'wthin', 'wthis', 'wtih', 'wussies', 'wussy', 'wwe', 'wwine', 'wwwaddictionovercomecom', 'wwwdogparkriotcom', 'wwwphoenixrodandgun', 'würze', 'x2', 'x5', 'xanax', 'xavier', 'xbox', 'xcp', 'xeriscape', 'xl', 'xlv', 'xmas', 'xrays', 'xylophone', 'xyz', 'xèo', 'ya', 'yaaaaay', 'yack', 'yah', 'yahoo', 'yahummee', 'yakaguru', 'yaki', 'yakisoba', 'yakitorithe', 'yall', 'yam', 'yang', 'yankin', 'yanks', 'yard', 'yards', 'yardsale', 'yasu', 'yasus', 'yawning', 'yay', 'ycs', 'yds', 'yea', 'yeah', 'yeahim', 'yeahlike', 'yeahnotsomuch', 'yeahplease', 'year', 'yearly', 'yearning', 'yearnings', 'years', 'yearshow', 'yearsif', 'yearsso', 'yearssuper', 'yearsthe', 'yearsuntil', 'yearthough', 'yeast', 'yeh', 'yelena', 'yell', 'yelled', 'yelling', 'yellow', 'yellowfin', 'yellowish', 'yellowtail', 'yells', 'yelp', 'yelpadvertised', 'yelpcom', 'yelped', 'yelper', 'yelpers', 'yelpi', 'yelping', 'yelps', 'yen', 'yep', 'yeppie', 'yer', 'yerba', 'yes', 'yesssss', 'yesterday', 'yet', 'yetd', 'yetthe', 'yettobeadded', 'yew', 'ygl', 'yielding', 'yikes', 'yikesi', 'yin', 'yippee', 'yknow', 'ymca', 'yo', 'yodeling', 'yodels', 'yoga', 'yogalates', 'yoghund', 'yogis', 'yogurt', 'yogurtcucumbermint', 'yogurtini', 'yogurtland', 'yogurtology', 'yogurtopia', 'yogurts', 'yolis', 'yolk', 'yon', 'yoohoo', 'york', 'yorker', 'yorkers', 'yorkie', 'youd', 'youfeed', 'youki', 'youll', 'young', 'younger', 'youngest', 'youngggg', 'youngtown', 'younight', 'youre', 'yourselfdoes', 'youth', 'youthful', 'youtube', 'youve', 'youwhen', 'yow', 'yowza', 'yr', 'yraguis', 'yrs', 'yu', 'yuck', 'yucky', 'yuk', 'yukgejang', 'yukon', 'yum', 'yuma', 'yumgasms', 'yumjust', 'yumm', 'yummie', 'yummier', 'yumminess', 'yummm', 'yummmayyyy', 'yummmm', 'yummmmm', 'yummmmmm', 'yummmmmmers', 'yummmmmmmm', 'yummo', 'yummy', 'yummyalthough', 'yummydont', 'yummythat', 'yummywe', 'yumness', 'yumo', 'yung', 'yup', 'yupha', 'yuphas', 'yuppies', 'yur', 'yuris', 'yusefs', 'yuukk', 'yuuuummmmae', 'yuuuuummmmmyyy', 'yuuuuuuum', 'yuyuyummy', 'yuzu', 'yyyeeaahhhhsooooothe', 'z11', 'zach', 'zam', 'zanella', 'zankou', 'zappos', 'zatsiki', 'zen', 'zenlike', 'zenroad', 'zero', 'zerostar', 'zest', 'zesty', 'zexperience', 'zgrill', 'zha', 'zhou', 'zia', 'zichini', 'zilch', 'zillion', 'zin', 'zinburger', 'zinburgergeist', 'zinc', 'zinfandel', 'zing', 'zip', 'zipcar', 'ziploc', 'zipper', 'zippers', 'zipps', 'ziti', 'zoe', 'zoes', 'zombi', 'zombies', 'zone', 'zoners', 'zones', 'zoning', 'zoo', 'zoom', 'zoyo', 'zucca', 'zucchini', 'zuccini', 'zuchinni', 'zumba', 'zupa', 'zupas', 'zuzu', 'zuzus', 'zwiebelkräuter', 'éclairs', 'école', 'ém']\n"
     ]
    }
   ],
   "source": [
    "print(vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4086, 21882)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "multi_nb_classifier = MultinomialNB()\n",
    "labels = extreme_reviews['stars'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_nb_classifier.fit(X,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string = [\"Voila that was awesome\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string_vectorised = vectorizer.transform(test_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_string_vectorised.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predict = multi_nb_classifier.predict(test_string_vectorised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5], dtype=int64)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "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>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>type</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife took me here on my birthday for breakf...</td>\n",
       "      <td>review</td>\n",
       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
       "      <td>review</td>\n",
       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
       "      <td>2012-06-14</td>\n",
       "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
       "      <td>4</td>\n",
       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
       "      <td>review</td>\n",
       "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
       "      <td>2010-05-27</td>\n",
       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>VY_tvNUCCXGXQeSvJl757Q</td>\n",
       "      <td>2012-07-28</td>\n",
       "      <td>Ubyfp2RSDYW0g7Mbr8N3iA</td>\n",
       "      <td>3</td>\n",
       "      <td>First visit...Had lunch here today - used my G...</td>\n",
       "      <td>review</td>\n",
       "      <td>_eqQoPtQ3e3UxLE4faT6ow</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>EKzMHI1tip8rC1-ZAy64yg</td>\n",
       "      <td>2012-01-18</td>\n",
       "      <td>2XyIOQKbVFb6uXQdJ0RzlQ</td>\n",
       "      <td>4</td>\n",
       "      <td>Should be called house of deliciousness!\\n\\nI ...</td>\n",
       "      <td>review</td>\n",
       "      <td>ROru4uk5SaYc3rg8IU7SQw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>53YGfwmbW73JhFiemNeyzQ</td>\n",
       "      <td>2010-11-16</td>\n",
       "      <td>jyznYkIbpqVmlsZxSDSypA</td>\n",
       "      <td>4</td>\n",
       "      <td>I recently visited Olive and Ivy for business ...</td>\n",
       "      <td>review</td>\n",
       "      <td>gGbN1aKQHMgfQZkqlsuwzg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9SKdOoDHcFoxK5ZtsgHJoA</td>\n",
       "      <td>2012-12-02</td>\n",
       "      <td>5UKq9WQE1qQbJ0DJbc-B6Q</td>\n",
       "      <td>2</td>\n",
       "      <td>My nephew just moved to Scottsdale recently so...</td>\n",
       "      <td>review</td>\n",
       "      <td>0lyVoNazXa20WzUyZPLaQQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>pF7uRzygyZsltbmVpjIyvw</td>\n",
       "      <td>2010-10-16</td>\n",
       "      <td>vWSmOhg2ID1MNZHaWapGbA</td>\n",
       "      <td>5</td>\n",
       "      <td>4-5 locations.. all 4.5 star average.. I think...</td>\n",
       "      <td>review</td>\n",
       "      <td>KSBFytcdjPKZgXKQnYQdkA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 business_id        date               review_id  stars  \\\n",
       "0     9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
       "1     ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
       "2     6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
       "3     _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
       "4     6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
       "...                      ...         ...                     ...    ...   \n",
       "9995  VY_tvNUCCXGXQeSvJl757Q  2012-07-28  Ubyfp2RSDYW0g7Mbr8N3iA      3   \n",
       "9996  EKzMHI1tip8rC1-ZAy64yg  2012-01-18  2XyIOQKbVFb6uXQdJ0RzlQ      4   \n",
       "9997  53YGfwmbW73JhFiemNeyzQ  2010-11-16  jyznYkIbpqVmlsZxSDSypA      4   \n",
       "9998  9SKdOoDHcFoxK5ZtsgHJoA  2012-12-02  5UKq9WQE1qQbJ0DJbc-B6Q      2   \n",
       "9999  pF7uRzygyZsltbmVpjIyvw  2010-10-16  vWSmOhg2ID1MNZHaWapGbA      5   \n",
       "\n",
       "                                                   text    type  \\\n",
       "0     My wife took me here on my birthday for breakf...  review   \n",
       "1     I have no idea why some people give bad review...  review   \n",
       "2     love the gyro plate. Rice is so good and I als...  review   \n",
       "3     Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
       "4     General Manager Scott Petello is a good egg!!!...  review   \n",
       "...                                                 ...     ...   \n",
       "9995  First visit...Had lunch here today - used my G...  review   \n",
       "9996  Should be called house of deliciousness!\\n\\nI ...  review   \n",
       "9997  I recently visited Olive and Ivy for business ...  review   \n",
       "9998  My nephew just moved to Scottsdale recently so...  review   \n",
       "9999  4-5 locations.. all 4.5 star average.. I think...  review   \n",
       "\n",
       "                     user_id  cool  useful  funny  length  \n",
       "0     rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0     889  \n",
       "1     0a2KyEL0d3Yb1V6aivbIuQ     0       0      0    1345  \n",
       "2     0hT2KtfLiobPvh6cDC8JQg     0       1      0      76  \n",
       "3     uZetl9T0NcROGOyFfughhg     1       2      0     419  \n",
       "4     vYmM4KTsC8ZfQBg-j5MWkw     0       0      0     469  \n",
       "...                      ...   ...     ...    ...     ...  \n",
       "9995  _eqQoPtQ3e3UxLE4faT6ow     1       2      0     668  \n",
       "9996  ROru4uk5SaYc3rg8IU7SQw     0       0      0     881  \n",
       "9997  gGbN1aKQHMgfQZkqlsuwzg     0       0      0    1425  \n",
       "9998  0lyVoNazXa20WzUyZPLaQQ     0       0      0     880  \n",
       "9999  KSBFytcdjPKZgXKQnYQdkA     0       0      0     461  \n",
       "\n",
       "[10000 rows x 11 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset['text']\n",
    "y = dataset['stars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       My wife took me here on my birthday for breakf...\n",
       "1       I have no idea why some people give bad review...\n",
       "2       love the gyro plate. Rice is so good and I als...\n",
       "3       Rosie, Dakota, and I LOVE Chaparral Dog Park!!...\n",
       "4       General Manager Scott Petello is a good egg!!!...\n",
       "                              ...                        \n",
       "9995    First visit...Had lunch here today - used my G...\n",
       "9996    Should be called house of deliciousness!\\n\\nI ...\n",
       "9997    I recently visited Olive and Ivy for business ...\n",
       "9998    My nephew just moved to Scottsdale recently so...\n",
       "9999    4-5 locations.. all 4.5 star average.. I think...\n",
       "Name: text, Length: 10000, dtype: object"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       5\n",
       "1       5\n",
       "2       4\n",
       "3       5\n",
       "4       5\n",
       "       ..\n",
       "9995    3\n",
       "9996    4\n",
       "9997    4\n",
       "9998    2\n",
       "9999    5\n",
       "Name: stars, Length: 10000, dtype: int64"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset['text']\n",
    "y = dataset['stars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_cleansing(text):\n",
    "    text = ''.join([ ch for ch in text if ch not in punctuations])\n",
    "    text = ' '.join([ word for word in text.split() if word.lower() not in english_stopwords])\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_preprocessed = X.apply(text_cleansing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       wife took birthday breakfast excellent weather...\n",
       "1       idea people give bad reviews place goes show p...\n",
       "2       love gyro plate Rice good also dig candy selec...\n",
       "3       Rosie Dakota LOVE Chaparral Dog Park convenien...\n",
       "4       General Manager Scott Petello good egg go deta...\n",
       "                              ...                        \n",
       "9995    First visitHad lunch today used Groupon ordere...\n",
       "9996    called house deliciousness could go item item ...\n",
       "9997    recently visited Olive Ivy business last week ...\n",
       "9998    nephew moved Scottsdale recently bunch friends...\n",
       "9999    45 locations 45 star average think Arizona rea...\n",
       "Name: text, Length: 10000, dtype: object"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vectorizer = CountVectorizer()\n",
    "X = vectorizer.fit_transform(X_preprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train , x_test , y_train , y_test = train_test_split(X,y,test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "multi_nb_classifier = MultinomialNB()\n",
    "multi_nb_classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_predict = multi_nb_classifier.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "cm = confusion_matrix(y_train , y_train_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x248662d8128>"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = multi_nb_classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 2, ..., 4, 4, 5], dtype=int64)"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24869ba55c0>"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.65      0.39      0.49       125\n",
      "           2       0.32      0.05      0.09       167\n",
      "           3       0.27      0.05      0.09       316\n",
      "           4       0.44      0.75      0.56       740\n",
      "           5       0.62      0.54      0.58       652\n",
      "\n",
      "    accuracy                           0.49      2000\n",
      "   macro avg       0.46      0.36      0.36      2000\n",
      "weighted avg       0.47      0.49      0.45      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "tfidf = TfidfTransformer()\n",
    "X_tfidf = tfidf.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tfidf.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train , x_test , y_train , y_test = train_test_split(X_tfidf,y,test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "multi_nb_classifier = MultinomialNB()\n",
    "multi_nb_classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_preds = multi_nb_classifier.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_train,train_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24869a38b70>"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = multi_nb_classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24970a6bfd0>"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}