1. Foundations of NLP.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>NLP with Deep Learning for Everyone</h1>\n",
    "<h1>Foundations of NLP</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import gzip\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords  \n",
    "from nltk.text import TextCollection\n",
    "from nltk.collocations import BigramCollocationFinder\n",
    "from nltk.metrics.association import BigramAssocMeasures\n",
    "\n",
    "import sklearn\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.5\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 23.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 3022f3d8aeb2c24ebf2d47d4e9999181a7ad777d\n",
      "\n",
      "pandas    : 2.0.3\n",
      "nltk      : 3.8.1\n",
      "watermark : 2.4.3\n",
      "sklearn   : 1.3.0\n",
      "numpy     : 1.23.5\n",
      "json      : 2.0.9\n",
      "matplotlib: 3.7.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One-Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"Mary had a little lamb, little lamb,\n",
    "    little lamb. 'Mary' had a little lamb\n",
    "    whose fleece was white as snow.\n",
    "    And everywhere that Mary went\n",
    "    Mary went, MARY went. Everywhere\n",
    "    that mary went,\n",
    "    The lamb was sure to go\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to tokenize the text. NLTK provides us with a convenient tokenizer function we can use. It is also smart enough to be able to handle different languages. We start by making sure that we have the punkt add-on downloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/bgoncalves/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then move on to using it to tokenize our text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = nltk.word_tokenize(text, 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mary',\n",
      " 'had',\n",
      " 'a',\n",
      " 'little',\n",
      " 'lamb',\n",
      " ',',\n",
      " 'little',\n",
      " 'lamb',\n",
      " ',',\n",
      " 'little',\n",
      " 'lamb',\n",
      " '.',\n",
      " \"'Mary\",\n",
      " \"'\",\n",
      " 'had',\n",
      " 'a',\n",
      " 'little',\n",
      " 'lamb',\n",
      " 'whose',\n",
      " 'fleece',\n",
      " 'was',\n",
      " 'white',\n",
      " 'as',\n",
      " 'snow',\n",
      " '.',\n",
      " 'And',\n",
      " 'everywhere',\n",
      " 'that',\n",
      " 'Mary',\n",
      " 'went',\n",
      " 'Mary',\n",
      " 'went',\n",
      " ',',\n",
      " 'MARY',\n",
      " 'went',\n",
      " '.',\n",
      " 'Everywhere',\n",
      " 'that',\n",
      " 'mary',\n",
      " 'went',\n",
      " ',',\n",
      " 'The',\n",
      " 'lamb',\n",
      " 'was',\n",
      " 'sure',\n",
      " 'to',\n",
      " 'go']\n"
     ]
    }
   ],
   "source": [
    "pprint(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll note that NLTK includes apostrophes at the beginning of words and returns all punctuation markings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",\n",
      "'Mary\n",
      "'\n"
     ]
    }
   ],
   "source": [
    "print(tokens[5])\n",
    "print(tokens[12])\n",
    "print(tokens[13])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wrap it into a utility function to handle these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(text, preserve_case=True):\n",
    "    punctuation = set(string.punctuation)\n",
    "    text_words = []\n",
    "    \n",
    "    for word in nltk.word_tokenize(text, 'english'):\n",
    "        # Remove any punctuation characters present in the beginning of the word\n",
    "        while len(word) > 0 and  word[0] in punctuation:\n",
    "            word = word[1:]\n",
    "\n",
    "        # Remove any punctuation characters present in the end of the word\n",
    "        while len(word) > 0 and word[-1] in punctuation:\n",
    "            word = word[:-1]\n",
    "        \n",
    "        if len(word) > 0:\n",
    "            if preserve_case:\n",
    "                text_words.append(word)\n",
    "            else:\n",
    "                text_words.append(word.lower())\n",
    "        \n",
    "    return text_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_words = tokenize(text, preserve_case=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mary',\n",
       " 'had',\n",
       " 'a',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'mary',\n",
       " 'had',\n",
       " 'a',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'whose',\n",
       " 'fleece',\n",
       " 'was',\n",
       " 'white',\n",
       " 'as',\n",
       " 'snow',\n",
       " 'and',\n",
       " 'everywhere',\n",
       " 'that',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'everywhere',\n",
       " 'that',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'the',\n",
       " 'lamb',\n",
       " 'was',\n",
       " 'sure',\n",
       " 'to',\n",
       " 'go']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get a quick one-hot encoded version using pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot = pd.get_dummies(text_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which provides us with a DataFrame where each column corresponds to an individual unique word and each row to a word in our text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>11</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        a    and     as everywhere fleece     go    had   lamb little   mary  \\\n",
       "0   False  False  False      False  False  False  False  False  False   True   \n",
       "1   False  False  False      False  False  False   True  False  False  False   \n",
       "2    True  False  False      False  False  False  False  False  False  False   \n",
       "3   False  False  False      False  False  False  False  False   True  False   \n",
       "4   False  False  False      False  False  False  False   True  False  False   \n",
       "5   False  False  False      False  False  False  False  False   True  False   \n",
       "6   False  False  False      False  False  False  False   True  False  False   \n",
       "7   False  False  False      False  False  False  False  False   True  False   \n",
       "8   False  False  False      False  False  False  False   True  False  False   \n",
       "9   False  False  False      False  False  False  False  False  False   True   \n",
       "10  False  False  False      False  False  False   True  False  False  False   \n",
       "11   True  False  False      False  False  False  False  False  False  False   \n",
       "12  False  False  False      False  False  False  False  False   True  False   \n",
       "13  False  False  False      False  False  False  False   True  False  False   \n",
       "14  False  False  False      False  False  False  False  False  False  False   \n",
       "15  False  False  False      False   True  False  False  False  False  False   \n",
       "16  False  False  False      False  False  False  False  False  False  False   \n",
       "17  False  False  False      False  False  False  False  False  False  False   \n",
       "18  False  False   True      False  False  False  False  False  False  False   \n",
       "19  False  False  False      False  False  False  False  False  False  False   \n",
       "20  False   True  False      False  False  False  False  False  False  False   \n",
       "21  False  False  False       True  False  False  False  False  False  False   \n",
       "22  False  False  False      False  False  False  False  False  False  False   \n",
       "23  False  False  False      False  False  False  False  False  False   True   \n",
       "24  False  False  False      False  False  False  False  False  False  False   \n",
       "25  False  False  False      False  False  False  False  False  False   True   \n",
       "26  False  False  False      False  False  False  False  False  False  False   \n",
       "27  False  False  False      False  False  False  False  False  False   True   \n",
       "28  False  False  False      False  False  False  False  False  False  False   \n",
       "29  False  False  False       True  False  False  False  False  False  False   \n",
       "30  False  False  False      False  False  False  False  False  False  False   \n",
       "31  False  False  False      False  False  False  False  False  False   True   \n",
       "32  False  False  False      False  False  False  False  False  False  False   \n",
       "33  False  False  False      False  False  False  False  False  False  False   \n",
       "34  False  False  False      False  False  False  False   True  False  False   \n",
       "35  False  False  False      False  False  False  False  False  False  False   \n",
       "36  False  False  False      False  False  False  False  False  False  False   \n",
       "37  False  False  False      False  False  False  False  False  False  False   \n",
       "38  False  False  False      False  False   True  False  False  False  False   \n",
       "\n",
       "     snow   sure   that    the     to    was   went  white  whose  \n",
       "0   False  False  False  False  False  False  False  False  False  \n",
       "1   False  False  False  False  False  False  False  False  False  \n",
       "2   False  False  False  False  False  False  False  False  False  \n",
       "3   False  False  False  False  False  False  False  False  False  \n",
       "4   False  False  False  False  False  False  False  False  False  \n",
       "5   False  False  False  False  False  False  False  False  False  \n",
       "6   False  False  False  False  False  False  False  False  False  \n",
       "7   False  False  False  False  False  False  False  False  False  \n",
       "8   False  False  False  False  False  False  False  False  False  \n",
       "9   False  False  False  False  False  False  False  False  False  \n",
       "10  False  False  False  False  False  False  False  False  False  \n",
       "11  False  False  False  False  False  False  False  False  False  \n",
       "12  False  False  False  False  False  False  False  False  False  \n",
       "13  False  False  False  False  False  False  False  False  False  \n",
       "14  False  False  False  False  False  False  False  False   True  \n",
       "15  False  False  False  False  False  False  False  False  False  \n",
       "16  False  False  False  False  False   True  False  False  False  \n",
       "17  False  False  False  False  False  False  False   True  False  \n",
       "18  False  False  False  False  False  False  False  False  False  \n",
       "19   True  False  False  False  False  False  False  False  False  \n",
       "20  False  False  False  False  False  False  False  False  False  \n",
       "21  False  False  False  False  False  False  False  False  False  \n",
       "22  False  False   True  False  False  False  False  False  False  \n",
       "23  False  False  False  False  False  False  False  False  False  \n",
       "24  False  False  False  False  False  False   True  False  False  \n",
       "25  False  False  False  False  False  False  False  False  False  \n",
       "26  False  False  False  False  False  False   True  False  False  \n",
       "27  False  False  False  False  False  False  False  False  False  \n",
       "28  False  False  False  False  False  False   True  False  False  \n",
       "29  False  False  False  False  False  False  False  False  False  \n",
       "30  False  False   True  False  False  False  False  False  False  \n",
       "31  False  False  False  False  False  False  False  False  False  \n",
       "32  False  False  False  False  False  False   True  False  False  \n",
       "33  False  False  False   True  False  False  False  False  False  \n",
       "34  False  False  False  False  False  False  False  False  False  \n",
       "35  False  False  False  False  False   True  False  False  False  \n",
       "36  False   True  False  False  False  False  False  False  False  \n",
       "37  False  False  False  False   True  False  False  False  False  \n",
       "38  False  False  False  False  False  False  False  False  False  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = one_hot.astype('str')\n",
    "temp[temp=='0'] = \"\"\n",
    "temp = pd.DataFrame(temp)\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here can easily generate a mapping between the words and their numerical id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 0,\n",
       " 'and': 1,\n",
       " 'as': 2,\n",
       " 'everywhere': 3,\n",
       " 'fleece': 4,\n",
       " 'go': 5,\n",
       " 'had': 6,\n",
       " 'lamb': 7,\n",
       " 'little': 8,\n",
       " 'mary': 9,\n",
       " 'snow': 10,\n",
       " 'sure': 11,\n",
       " 'that': 12,\n",
       " 'the': 13,\n",
       " 'to': 14,\n",
       " 'was': 15,\n",
       " 'went': 16,\n",
       " 'white': 17,\n",
       " 'whose': 18}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_dict = dict(zip(one_hot.columns, np.arange(one_hot.shape[1])))\n",
    "word_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Allowing us to easily transform from words to ids and back"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bag of Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the one-hot encoded representation we can easily obtain the bag of words version of our document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>as</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>everywhere</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fleece</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mary</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snow</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sure</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>that</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whose</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count\n",
       "a               2\n",
       "and             1\n",
       "as              1\n",
       "everywhere      2\n",
       "fleece          1\n",
       "go              1\n",
       "had             2\n",
       "lamb            5\n",
       "little          4\n",
       "mary            6\n",
       "snow            1\n",
       "sure            1\n",
       "that            2\n",
       "the             1\n",
       "to              1\n",
       "was             2\n",
       "went            4\n",
       "white           1\n",
       "whose           1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(one_hot.sum(), columns=['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'mary': 6,\n",
       "         'had': 2,\n",
       "         'a': 2,\n",
       "         'little': 4,\n",
       "         'lamb': 5,\n",
       "         'whose': 1,\n",
       "         'fleece': 1,\n",
       "         'was': 2,\n",
       "         'white': 1,\n",
       "         'as': 1,\n",
       "         'snow': 1,\n",
       "         'and': 1,\n",
       "         'everywhere': 2,\n",
       "         'that': 2,\n",
       "         'went': 4,\n",
       "         'the': 1,\n",
       "         'sure': 1,\n",
       "         'to': 1,\n",
       "         'go': 1})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(text_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more general representation would be to use a dictionary mapping each word to the number of times it occurs. This has the added advantage of being a dense representation that doesn't waste any space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = ['playing', 'loved', 'ran', 'river', 'friendships', \n",
    "         'misunderstanding', 'trouble', 'troubling']\n",
    "\n",
    "stemmers = { \n",
    "    'LancasterStemmer' : nltk.stem.LancasterStemmer(),\n",
    "    'PorterStemmer' : nltk.stem.PorterStemmer(),\n",
    "    'RegexpStemmer' : nltk.stem.RegexpStemmer('ing$|s$|e$|able$'),\n",
    "    'SnowballStemmer' : nltk.stem.SnowballStemmer('english')\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = []\n",
    "\n",
    "for word in words:\n",
    "    row = []\n",
    "    for stemmer in stemmers:\n",
    "        stem = stemmers[stemmer]\n",
    "        row.append(stem.stem(word))\n",
    "        \n",
    "    matrix.append(row)\n",
    "\n",
    "comparison = pd.DataFrame(matrix, index=words, columns=stemmers.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>LancasterStemmer</th>\n",
       "      <th>PorterStemmer</th>\n",
       "      <th>RegexpStemmer</th>\n",
       "      <th>SnowballStemmer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>playing</th>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loved</th>\n",
       "      <td>lov</td>\n",
       "      <td>love</td>\n",
       "      <td>loved</td>\n",
       "      <td>love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>river</th>\n",
       "      <td>riv</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friendships</th>\n",
       "      <td>friend</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>misunderstanding</th>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trouble</th>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>troubling</th>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 LancasterStemmer  PorterStemmer  RegexpStemmer  \\\n",
       "playing                      play           play           play   \n",
       "loved                         lov           love          loved   \n",
       "ran                           ran            ran            ran   \n",
       "river                         riv          river          river   \n",
       "friendships                friend     friendship     friendship   \n",
       "misunderstanding    misunderstand  misunderstand  misunderstand   \n",
       "trouble                    troubl         troubl         troubl   \n",
       "troubling                  troubl         troubl         troubl   \n",
       "\n",
       "                 SnowballStemmer  \n",
       "playing                     play  \n",
       "loved                       love  \n",
       "ran                          ran  \n",
       "river                      river  \n",
       "friendships           friendship  \n",
       "misunderstanding   misunderstand  \n",
       "trouble                   troubl  \n",
       "troubling                 troubl  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordnet = nltk.stem.WordNetLemmatizer()\n",
    "\n",
    "results_n = [wordnet.lemmatize(word, 'n') for word in words]\n",
    "results_v = [wordnet.lemmatize(word, 'v') for word in words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison['WordNetLemmatizer Noun'] = results_n\n",
    "comparison['WordNetLemmatizer Verb'] = results_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LancasterStemmer</th>\n",
       "      <th>PorterStemmer</th>\n",
       "      <th>RegexpStemmer</th>\n",
       "      <th>SnowballStemmer</th>\n",
       "      <th>WordNetLemmatizer Noun</th>\n",
       "      <th>WordNetLemmatizer Verb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>playing</th>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>playing</td>\n",
       "      <td>play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loved</th>\n",
       "      <td>lov</td>\n",
       "      <td>love</td>\n",
       "      <td>loved</td>\n",
       "      <td>love</td>\n",
       "      <td>loved</td>\n",
       "      <td>love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>river</th>\n",
       "      <td>riv</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friendships</th>\n",
       "      <td>friend</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendships</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>misunderstanding</th>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstanding</td>\n",
       "      <td>misunderstand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trouble</th>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>trouble</td>\n",
       "      <td>trouble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>troubling</th>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubling</td>\n",
       "      <td>trouble</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 LancasterStemmer  PorterStemmer  RegexpStemmer  \\\n",
       "playing                      play           play           play   \n",
       "loved                         lov           love          loved   \n",
       "ran                           ran            ran            ran   \n",
       "river                         riv          river          river   \n",
       "friendships                friend     friendship     friendship   \n",
       "misunderstanding    misunderstand  misunderstand  misunderstand   \n",
       "trouble                    troubl         troubl         troubl   \n",
       "troubling                  troubl         troubl         troubl   \n",
       "\n",
       "                 SnowballStemmer WordNetLemmatizer Noun WordNetLemmatizer Verb  \n",
       "playing                     play                playing                   play  \n",
       "loved                       love                  loved                   love  \n",
       "ran                          ran                    ran                    run  \n",
       "river                      river                  river                  river  \n",
       "friendships           friendship             friendship            friendships  \n",
       "misunderstanding   misunderstand       misunderstanding          misunderstand  \n",
       "trouble                   troubl                trouble                trouble  \n",
       "troubling                 troubl              troubling                trouble  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK provides stopwords for 23 different languages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dutch',\n",
       " 'german',\n",
       " 'slovene',\n",
       " 'hungarian',\n",
       " 'romanian',\n",
       " 'kazakh',\n",
       " 'turkish',\n",
       " 'russian',\n",
       " 'README',\n",
       " 'italian',\n",
       " 'english',\n",
       " 'greek',\n",
       " 'tajik',\n",
       " 'norwegian',\n",
       " 'portuguese',\n",
       " 'finnish',\n",
       " 'danish',\n",
       " 'french',\n",
       " 'swedish',\n",
       " 'azerbaijani',\n",
       " 'spanish',\n",
       " 'indonesian',\n",
       " 'arabic',\n",
       " 'nepali']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('/Users/bgoncalves/nltk_data/corpora/stopwords/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can easily use them to filter out meaningless words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Filtered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Original</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whose</th>\n",
       "      <td>whose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fleece</th>\n",
       "      <td>fleece</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>as</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snow</th>\n",
       "      <td>snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>And</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>everywhere</th>\n",
       "      <td>everywhere</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>that</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>went</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>went</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARY</th>\n",
       "      <td>MARY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>went</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Everywhere</th>\n",
       "      <td>Everywhere</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>that</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mary</th>\n",
       "      <td>mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>went</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sure</th>\n",
       "      <td>sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>go</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Filtered\n",
       "Original              \n",
       "Mary              Mary\n",
       "had                   \n",
       "a                     \n",
       "little          little\n",
       "lamb              lamb\n",
       "little          little\n",
       "lamb              lamb\n",
       "little          little\n",
       "lamb              lamb\n",
       "Mary              Mary\n",
       "had                   \n",
       "a                     \n",
       "little          little\n",
       "lamb              lamb\n",
       "whose            whose\n",
       "fleece          fleece\n",
       "was                   \n",
       "white            white\n",
       "as                    \n",
       "snow              snow\n",
       "And                   \n",
       "everywhere  everywhere\n",
       "that                  \n",
       "Mary              Mary\n",
       "went              went\n",
       "Mary              Mary\n",
       "went              went\n",
       "MARY              MARY\n",
       "went              went\n",
       "Everywhere  Everywhere\n",
       "that                  \n",
       "mary              mary\n",
       "went              went\n",
       "The                   \n",
       "lamb              lamb\n",
       "was                   \n",
       "sure              sure\n",
       "to                    \n",
       "go                  go"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))  \n",
    "  \n",
    "tokens = tokenize(text)  \n",
    "  \n",
    "filtered_sentence = [word if word.lower() not in stop_words else \"\" for word in tokens]  \n",
    "  \n",
    "pd.DataFrame((zip(tokens, filtered_sentence)), \n",
    "             columns=['Original', 'Filtered']).set_index('Original')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# N-Grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ngrams(text, length):\n",
    "    from nltk.util import ngrams\n",
    "    \n",
    "    n_grams = ngrams(tokenize(text), length)\n",
    "    return [' '.join(grams) for grams in n_grams]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mary had',\n",
       " 'had a',\n",
       " 'a little',\n",
       " 'little lamb',\n",
       " 'lamb little',\n",
       " 'little lamb',\n",
       " 'lamb little',\n",
       " 'little lamb',\n",
       " 'lamb mary',\n",
       " 'mary had',\n",
       " 'had a',\n",
       " 'a little',\n",
       " 'little lamb',\n",
       " 'lamb whose',\n",
       " 'whose fleece',\n",
       " 'fleece was',\n",
       " 'was white',\n",
       " 'white as',\n",
       " 'as snow',\n",
       " 'snow and',\n",
       " 'and everywhere',\n",
       " 'everywhere that',\n",
       " 'that mary',\n",
       " 'mary went',\n",
       " 'went mary',\n",
       " 'mary went',\n",
       " 'went mary',\n",
       " 'mary went',\n",
       " 'went everywhere',\n",
       " 'everywhere that',\n",
       " 'that mary',\n",
       " 'mary went',\n",
       " 'went the',\n",
       " 'the lamb',\n",
       " 'lamb was',\n",
       " 'was sure',\n",
       " 'sure to',\n",
       " 'to go']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ngrams(text.lower(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collocations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigrams = BigramCollocationFinder.from_words(tokenize(text, False))\n",
    "scored = bigrams.score_ngrams(BigramAssocMeasures.likelihood_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('little', 'lamb'), 20.789064969512467),\n",
       " (('mary', 'went'), 18.154919185356587),\n",
       " (('everywhere', 'that'), 15.777294140200038),\n",
       " (('had', 'a'), 15.777294140200038),\n",
       " (('a', 'little'), 10.232116695720478),\n",
       " (('as', 'snow'), 9.301260258907092),\n",
       " (('snow', 'and'), 9.301260258907092),\n",
       " (('sure', 'to'), 9.301260258907092),\n",
       " (('to', 'go'), 9.301260258907092),\n",
       " (('white', 'as'), 9.301260258907092),\n",
       " (('whose', 'fleece'), 9.301260258907092),\n",
       " (('mary', 'had'), 8.139124120662284),\n",
       " (('that', 'mary'), 8.139124120662284),\n",
       " (('and', 'everywhere'), 6.528671536667327),\n",
       " (('fleece', 'was'), 6.528671536667327),\n",
       " (('was', 'sure'), 6.528671536667327),\n",
       " (('was', 'white'), 6.528671536667327),\n",
       " (('went', 'the'), 4.802579101956642),\n",
       " (('lamb', 'whose'), 4.297236023525216),\n",
       " (('the', 'lamb'), 4.297236023525216),\n",
       " (('lamb', 'little'), 3.850143362325084),\n",
       " (('went', 'mary'), 3.065215528887738),\n",
       " (('went', 'everywhere'), 2.196764352889584),\n",
       " (('lamb', 'was'), 1.7502532877068806),\n",
       " (('lamb', 'mary'), 0.08819306486371048)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TF-IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK has the TextCollection object that allows us to easily compute tf-idf scores from a given corpus. We generate a small corpus by splitting our text by sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = text.split('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 4 documents in our corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK expects the corpus to be tokenized so we do that now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [tokenize(doc, preserve_case=False) for doc in corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['mary', 'had', 'a', 'little', 'lamb', 'little', 'lamb', 'little', 'lamb'],\n",
       " ['mary',\n",
       "  'had',\n",
       "  'a',\n",
       "  'little',\n",
       "  'lamb',\n",
       "  'whose',\n",
       "  'fleece',\n",
       "  'was',\n",
       "  'white',\n",
       "  'as',\n",
       "  'snow'],\n",
       " ['and', 'everywhere', 'that', 'mary', 'went', 'mary', 'went', 'mary', 'went'],\n",
       " ['everywhere',\n",
       "  'that',\n",
       "  'mary',\n",
       "  'went',\n",
       "  'the',\n",
       "  'lamb',\n",
       "  'was',\n",
       "  'sure',\n",
       "  'to',\n",
       "  'go']]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We initialize the TextCollection object with our corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = TextCollection(corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This object provides us with a great deal of functionality, like total frequency counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individual tf/idf scores, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12602676010180824"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.tf_idf('snow', corpus[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the full TF/IDF scores for our corpus, we do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "TFIDF = []\n",
    "\n",
    "for doc in corpus:\n",
    "    current = {}\n",
    "    for token in doc:\n",
    "        current[token] = nlp.tf_idf(token, doc)\n",
    "        \n",
    "    TFIDF.append(current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'mary': 0.0,\n",
       "  'had': 0.07701635339554948,\n",
       "  'a': 0.07701635339554948,\n",
       "  'little': 0.23104906018664842,\n",
       "  'lamb': 0.09589402415059362},\n",
       " {'mary': 0.0,\n",
       "  'had': 0.06301338005090412,\n",
       "  'a': 0.06301338005090412,\n",
       "  'little': 0.06301338005090412,\n",
       "  'lamb': 0.026152915677434625,\n",
       "  'whose': 0.12602676010180824,\n",
       "  'fleece': 0.12602676010180824,\n",
       "  'was': 0.06301338005090412,\n",
       "  'white': 0.12602676010180824,\n",
       "  'as': 0.12602676010180824,\n",
       "  'snow': 0.12602676010180824},\n",
       " {'and': 0.15403270679109896,\n",
       "  'everywhere': 0.07701635339554948,\n",
       "  'that': 0.07701635339554948,\n",
       "  'mary': 0.0,\n",
       "  'went': 0.23104906018664842},\n",
       " {'everywhere': 0.06931471805599453,\n",
       "  'that': 0.06931471805599453,\n",
       "  'mary': 0.0,\n",
       "  'went': 0.06931471805599453,\n",
       "  'the': 0.13862943611198905,\n",
       "  'lamb': 0.028768207245178087,\n",
       "  'was': 0.06931471805599453,\n",
       "  'sure': 0.13862943611198905,\n",
       "  'to': 0.13862943611198905,\n",
       "  'go': 0.13862943611198905}]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TFIDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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