{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.probability import FreqDist\n",
    "import matplotlib.pyplot as plt\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Kendra', 'loves', 'cats', '.', 'In', 'fact', ',', 'she', 'has', 'TEN', 'cats', '.', 'If', 'she', 'did', \"n't\", 'have', 'a', 'house', ',', 'a', 'husband', 'and', 'a', 'graduate', 'degree', 'in', 'data', 'science', ',', 'she', \"'d\", 'be', 'a', 'cat', 'lady', '!']\n"
     ]
    }
   ],
   "source": [
    "#=================================\n",
    "# TOKENIZATION\n",
    "#=================================\n",
    "# Intro to tokenization\n",
    "# Tokenization is simply breaking text down into \"tokens\" which, in this case, is words!\n",
    "# Here is a sample sentence. It can have punctuation, CAPS!, etc.\n",
    "\n",
    "example_text = \"Kendra loves cats. In fact, she has TEN cats. If she didn't have a house, a husband and a graduate degree in data science, she'd be a cat lady!\"\n",
    "tokenized_example = word_tokenize(example_text)\n",
    "print(tokenized_example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_items([('Kendra', 1), ('loves', 1), ('cats', 2), ('.', 2), ('In', 1), ('fact', 1), (',', 3), ('she', 3), ('has', 1), ('TEN', 1), ('If', 1), ('did', 1), (\"n't\", 1), ('have', 1), ('a', 4), ('house', 1), ('husband', 1), ('and', 1), ('graduate', 1), ('degree', 1), ('in', 1), ('data', 1), ('science', 1), (\"'d\", 1), ('be', 1), ('cat', 1), ('lady', 1), ('!', 1)])\n"
     ]
    }
   ],
   "source": [
    "#=================================\n",
    "# FREQUENCY DISTRIBUTIONS\n",
    "#=================================\n",
    "# After turning the words into tokens, we can start to treat them a data\n",
    "# For example, we can use a FREQUENCY DISTRIBUTION to count the words\n",
    "fdist = FreqDist(tokenized_example)\n",
    "print(fdist.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('a', 4)]\n"
     ]
    }
   ],
   "source": [
    "# We can use the same Frequency Distribution to find the most common word\n",
    "print(fdist.most_common(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02702702702702703\n"
     ]
    }
   ],
   "source": [
    "# We can use the Frequency Distribution to find the frequency of specific words\n",
    "print(fdist.freq(\"cat\")) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IvDAeuhn4UdbBjQWDNQKtOSQiTWq4PoJvAB8H8LBO0GUAZvaseOzVmUY3BgxtW6kagYg0p+Gahua4+92ld8b75mcS0RhT2JfgL+s2sWPnQIOjERHZ3XCJYEaZY1ozoQIdE8Yxb68Odux0Hl6/udHhiIjsZrhE0GNm7yy908zeQdicRiqwMNYKlql5SESa0HB9BB8EfmFmpzD0xd8NTAD+KcvAxpKFc/bgmvvWhm0rD210NCIiuyqbCNx9DfB8MzsGOCTe/St3vzbzyMaQoW0rNXJIRJpPpfsR/A74XcaxjFlac0hEmplmB4+CA/aeQnub8fDjm9m6Y2ejwxER2YUSwSiYOK6d+TM7GHBYsVbNQyLSXJQIRslQP4Gah0SkuSgRjJKhfgLVCESkuSgRjJJF6jAWkSalRDBKtBy1iDQrJYJRst9eHUxob+Oxp7awZYfWHBKR5qFEMErGtbdxYNybYOXG/gZHIyIyRIlgFC2Kaw49okQgIk1EiWAUFfoJVm5QIhCR5pFZIjCzfc3sd2b2ZzO718w+kFDGzOw/zWyFmd1lZodnFU8zWDg7JIJHlAhEpIlUtNZQjfqBj7j7HWa2B7DUzK529z8XlXkFsCBejgDOj/+OSYVJZWoaEpFmklkicPdVwKp4vdfM7gP2AYoTwWuAi93dgVvMbIaZdcbHjjn7zJhMx4R2ntq6k3OvWc64dhv2MR0T2jmwXaOMRCQ7WdYIBpnZfOA5wK0lh/YBVhbdfjTeNyYTQVub8czOaSz965N8/ZoHKn7c6w6eyouOzDAwEWlpFn6MZ/gEZlOB64HPu/tlJceWAF9y9xvj7d8CZ7p7T0m504DTADo7O7sWL15cUyx9fX10dHRkUr7Ssn/dsIPr/tJL+/jxw5Zdvamfmx/dxuFzxvGJF82qaxxZlm2WOPIYc7PEoZjzH0ep7u7upe7enXjQ3TO7AOOBq4APpxz/NvCGotvLgM5y5+zq6vJa9fT0ZFY+i7L3r9ro+525xJ93zpUNjaPass0SRx5jbpY4FHP+4ygF9HjK92qWo4YMuAi4z93/I6XY5cBb4uihI4ENPkb7B2qx/6wpjGsz1m7eSd92dTCLSDay7CN4AfBm4G4zuzPe92/APAB3vwC4AnglsALoA07NMJ7cmTCujQP2nsIDazaxYu0mDn36jEaHJCJjUJajhm4Eyg6LidWV92QVw1iwYM4ePLBmE8tW9yoRiEgmNLO4yWn5ahHJmhJBk9OGNiKSNSWCJqctLkUka0oETW7eXh1MaINVG7ayYcuORocjImOQEkGTa28z9pkW+vSXq1YgIhlQIsiBedNDIlimRCAiGVAiyIF5gzUCdRiLSP0pEeTAvtPDukTa+F5EsqBEkAOFpiGNHBKRLCgR5MCsyW1MnTiOxzdvZ/2mbY0OR0TGGCWCHDAzFsSN7x9Q85CI1JkSQU5oqQkRyYoSQU4UlppYppFDIlJnSgQ5oaUmRCQrSgQ5Mbj43Orewm5uIiJ1oUSQE7OmTmDPjvH0butn9catjQ5HRMYQJYKcMLOhfgKNHBKROlIiyBH1E4hIFpQIcmSoRqCRQyJSP0oEOaIagYhkQYkgRxbODolg+dpeBgY0ckhE6kOJIEemd4xnzrSJbN0xwMon+xodjoiMEUoEOaORQyJSb0oEOaM1h0Sk3jJLBGb232a21szuSTl+tJltMLM74+WsrGIZSxbO1ZpDIlJf4zI89/eA84CLy5T5vbsfn2EMY06haUgb2YtIvWRWI3D3G4Ansjp/q1owO+xL8Jd1m9ixc6DB0YjIWGBZLmBmZvOBJe5+SMKxo4GfA48CfwPOcPd7U85zGnAaQGdnZ9fixYtriqevr4+Ojo5MymdVNqn8u69Yx9rNO/nGy2ex77RxZcs2S8zNXlZx1F62WeLIY8xZn7tYd3f3UnfvTjzo7pldgPnAPSnHpgFT4/VXAssrOWdXV5fXqqenJ7PyWZVNKv/2793m+525xBf/6bFRiyOP710eY26WOBRz/uMoBfR4yvdqw0YNuftGd98Ur18BjDezWY2KJ0+Kl6QWERmphiUCM5trZhavPy/G8nij4smTwUSgkUMiUgeZjRoys58ARwOzzOxR4NPAeAB3vwA4CXi3mfUDW4DXx+qLDGOh5hKISB1llgjc/Q3DHD+PMLxUqnTA3lNobzMefnwzW3fsZNL49kaHJCI5ppnFOTRpfDvzZ3Yw4LBirZqHRGRklAhyqrAk9fK1ah4SkZFRIsipBbO1SY2I1IcSQU5pkxoRqRclgpzSctQiUi9KBDk1f2YHE9rbeOypLWza1t/ocEQkx5QIcmpcexsHxgXotBKpiIyEEkGOLZoTEoH6CURkJJQIcmzBHI0cEpGRUyLIMW1bKSL1oESQY4sGt61UIhCR2ikR5Ng+MybTMaGddb3beHLz9kaHIyI5pUSQY21tNthPoOYhEamVEkHOLZytkUMiMjJKBDmnfgIRGSklgpwb2rZSQ0hFpDZKBDk3uPjc2l60wZuI1EKJIOdm7zGR6ZPH81TfDtb1bmt0OCKSQ0oEOWdmLIxLTaifQERqoUQwBmhJahEZCSWCMWBw28o16jAWkeopEYwBgzUCNQ2JSA2UCMaAQiJYvqaXAY0cEpEqKRGMAXtNmcCsqRPZvH0n6/t2NjocEcmZzBKBmf23ma01s3tSjpuZ/aeZrTCzu8zs8KxiaQWL5oaRQ49s0LaVIlKdLGsE3wOOK3P8FcCCeDkNOD/DWMa8QvPQyo1KBCJSnXFZndjdbzCz+WWKvAa42MN02FvMbIaZdbr7qqxiGssKm9Rc/9etfOr/Eithu1m3biP/t7L+ZbM891iPuVniUMzNGQd9m+nqqvjUFbMslyWIiWCJux+ScGwJ8CV3vzHe/i1wprv3JJQ9jVBroLOzs2vx4sU1xdPX10dHR0cm5bMqW2n5h57awRlXP17xOUUkfw6c0c5XXrZ3TY/t7u5e6u7dSccyqxHUk7t/B/gOQHd3t3fVmBKXLl1KNY+tpnxWZSst3wXs+bQ13HzXMubNm1fReR955JFMymZ57rEec7PEoZibM46Nax+r6rujUo1MBI8B+xbdfnq8T2r0kmfOYUbfo3R1za+o/NIJj2dSNstzj/WYmyUOxdykcSzNptbfyOGjlwNviaOHjgQ2qH9ARGT0ZVYjMLOfAEcDs8zsUeDTwHgAd78AuAJ4JbAC6ANOzSoWERFJl+WooTcMc9yB92T1/CIiUhnNLBYRaXFKBCIiLU6JQESkxSkRiIi0uExnFmfBzNYBf63x4bOA9RmVz6pss8ShmFsrDsWc/zhK7efuydOS3b1lLkBPVuWzKtsscSjm1opDMec/jmouahoSEWlxSgQiIi2u1RLBdzIsn1XZZolDMbdWHIo5/3FULHedxSIiUl+tViMQEZESSgQiIi1OiUBEpMXlYoeykTKzPYEFwKTCfe5+QwWP6wSecPdtZcpMBua5+7Iq49nX3e+q9DFjWdL7YWa/A9I6sNzdX1Jyji+7+5lmdrK7/zTDcKXJ1PI3mEEML3D3Pwx3X7Ma853FZvYO4AOEHdDuBI4Ebnb3Yyt47DXAgcDP3f2MhOOvBr4KTHD3/c3sMOAcdz8hoex1wAmE5LsUWAv8wd0/XMVrmevuqxPu3xs4EziYXZPdbq/RzF4A3Onum83sTcDhwLnunjhb28yeD8yn6EeDu1+cUO63CV/Ou91XdOw6yrwfZpa0H9+RwMeAte7+3JLz3Q0cCix198OTnrOo7GLSkwxJ/39pryftNZrZQuB8YI67H2JmhwInuPvnEsoacApwgLufY2bzgLnufltJubKvy93vKHd8OFXG3AF8hPAF/E4zWwAscvclZc5f6WdpEvB24O/Y9fP8LwllK/obNLMTy712d78s4dxzgC8AT3P3V5jZwcBR7n5RQtk7Sj93SfcVHVsK/DfwY3d/slxso6EVagQfAJ4L3OLux5jZMwj/ucNy95fGP9KDU4p8BngecF0sf6eZ7Z9Sdrq7b4yJ6WJ3/7SZVVsjuAh4VcL9PwIuicf+FXgrsC7lHOcDzzazZxP+kL8LXAy8uLSgmf2AkAjvBHbGuz2WL5SZBHQQNiDaE7B4aBqwT5nXUvb9cPelRc/xYuBThC+Ff3X3KxPO92vgSWCqmW0sfhnhdD6t6L6vxn9PBOYCP4y33wCsKT1xja/xQuCjwLfj67nLzH4M7PalCnwLGACOBc4BeoGfEz63xb4W/50EdAN/irEcCvQARxXF3Ev5ZDct4e5qYv4fQgIvPOdjwE+BxERQyWepyA+A+4GXE96PU4D7Ul7KZ6jsb/DV8d/ZwPOBa+PtY4CbgN0SAfA9wuv8RLz9AOHvbDARmNlR8Xx7m1nxj7ppQHtKzACvI2zGdbuZ9cTn+Y0X/TIvOR/x9f1HPPYmd/9h6fFatUIi2OruW80MM5vo7veb2aJKHxz/Y+5NObzD3TeEXDH0kJSy42JT02sZ+mBVxd2TkgDATHe/yMw+4O7XA9eb2e0pZfvd3c3sNcB58XFvTynbDRxc/OFM8C7gg8DTCF8MhTdjI3BemccN+36Y2cuBTwLbgM+7++/STubuHwU+ama/dPfXlHle4nuEmX3N3buLDi2Of5SlanmNHe5+W8lnoz+l7BHufriZ/THG96SZTUiI+5gY92XA4e5+d7x9COELsbjsHvHYZ4FVhC/XQs2jsw4xH+jurzOzN8Tn67OSB5ao5LNUcJC7n2xmr3H378dk9PuUshX9Dbr7qQBm9psYx6p4u5PwhZ9klrtfamYfj+foN7OdJWUmAFMJ36V7FN2/ETgp7QW6+wrgE2b2KeB4Qu1gp5n9D6GG/kTJ+UpNKXOsaq2QCB41sxnA/wFXm9mT1L5oXal7zeyNQHusGr+f8OsiydnAVcCN7n67mR0ALK9THDviv6vM7FXA34C9Usr2xg/2m4G/N7M24haiCe4h/GJO3Uva3c8FzjWz97n7N6uI+RzKvB8xke0N/Dtwc7xvsJqd1gwyXBIoMcXMDnD3B+P59yfhD6zG17jezA4kfimZ2Umkv487zKy9qOzehBpCmkWFJBDju8fMnplS9gR3f3bR7fPN7E/AWSOMeXtsmy+UPZCQsNMM+1kqUvg8PxWT3GrCL/kk1fwNQuiLKo5hDTAvpexmM5vJ0Gs8EthQXKDoh9f30ppX08Smt1MJW/b+nFCzfyGhtnKYu5+d9lh3/3Y1zzUsz2ABo2a9EJo/TiC0J9bjfB3A54HbCVXzzwOTUsq+oJL7aozjeGA6cAjwO8Kv1hNSys4FPgz8fbw9D3hLStnfEZpbrgIuL1xSyr4HmFF0e0/g9BG8puvi8yddrk0o30v4FbYxXu8tur4x5TmOAx6Jz3U98DDw8oRyL4qXI6uI/wDgGsJ+3I8BNxJWf0wqe0p8bx+Nn6FlwMllzv0TQpPe0fFyIfCTlLI3xfO3E0YJngLcVIeYXxbfs3WEL7CHgaPLxFzNZ+kd8fPzIuBBQv/Ruyr4G7yd0IyV+DcYy58XY3hbvFwJfDOl7OHAH4Cn4r8PAIemlC38aLmC8EV+bdLntKj8UuC3wBuBiSXHLqv176bWy5jvLG4W1XYmVXHeduD97v71Kh6zH7DA3a+JnX7t7t6bUG63fgMYalopKXunux9Wct8f3f05KTFU3CGYJTObCDwj3rzfE0aIxeo6wFPu/qEKz9vu7jvNbArQlvT+lpR/BvASQvPNb909rU288N69m/BFCXADcL67b00oOx84F3gB4ZftH4APuvvDCWX3d/eHimMu3JcSx0xCB74R+uBSl0eu8rO023OWi6NaZvZPFL137v6LlHKTgPcS+ip6CTXTb6a8z78h9B+cQVE/nbufmXLuwZpoM1AiGAELoyzOYPeREMcWlSl0Jn0QKP6yngb8k+9aba81jtvc/XkVln0ncBqwl7sfGKvTF3jK6J4qYrib8GupUI1uB+5y979LKf9TQofgGynqEHT3D8TjH3P3r8TruwwJNbMvuPu/jSDWY939WksZSeIJI0ji4yYROpjns+v/9zkJZR8hdGBfQvhlmPqHFptVHnX3bWZ2NKHz92J3f6riF1UHKT9Wlrr7biO4Yn/AsCOdRiGOqwm1p6fi7T2B/3X3l1z/hhQAABDhSURBVNchjksJtcofxbveSKj1npxQdqm7d5nZXe5+aLzvdt99dFvZUYIeO4NHWyv0EWTpp8AFhGp6aSdSQU2dSVX6g5mdR/jS2Vy405Pb0d9DGGVxayyz3Mx2aX+19BEnSSNwCn4NXGJmhbbLd8X70gzXIfh64Cvx+scJ73XBcUDNiYDwa/BawkiS4tdp8XZiIiD0M20gVOvLtYdDqGUcT3i/LzKzJYQvqBsTyv4c6Dazgwgjdi4HfkxoO96NhSHAnwH2Y9eEdEBC2b2Bd7J78vqXojLPINTMppckx2kU1dZKVDTSycxudPcXJnymdvss1RjHrOKE6aGjfbf+BDN7KD7/Onc/IuVcpQ5x9+IRg78zsz+nlK20n67wHbCI8F5dHm+/GhhxEq2VEsHI9Lv7+eUK+Ag6k6pQaJIp/mXqhD/SUtvcfXthlIWZjaPkS9/jiJMqnUn48n93vH01IUGmGa5D0FKuJ92uVm/8ZXYP4bUXzjdc9fjp7n5cJU/g7n3ApcCl8VfquYQ29aQhhQMeRqScSBjJ9U2LI4hSXAR8iJCQ0n6AFPySkGCvKVN2ESFpzWBomCWEL/d3pjym0pFOL4z/VvKZqiWOATOb5+6PwGCzZ9KoobRh3eXcYWZHuvst8dxHEPoCk3zOzKYThmR/k5C8PpgQx9nxXDcQRn71xtufAX5VQ4x1oURQAzMrZPrFZnY68AuKfiF6GPpVqs/M/p3d28SHndg2HI/DCit0vZn9GzDZzF4GnA4srkMMA4Q5CmUTY5HvxC/ITxJ+FU0lzBUYPGXK9aTb1Zoa/y38KvslIRkM96vsJjN7lheN2Ckntou/jlCD6SEMlU2yw8IwzLcw9AWYNpILYIMnz6VI0pHWTl3g7r8EfmlmR7n7zRWet9qRTsOqMY5PADea2fWE/8O/JzR91iw2czrh/+Cm2MznhBrY/SkPO5kwAu4e4Jj4HfFV0v+25gDbi25vj/c1hPoIalBUzSz+ZTr4RqZU0avqTKowjqrbGy0MF3078A+E+K8CvluuDbvCWBYAX2T32c27vRex/ETgnwlNFuOHiof2djMbADbFGCcTRrIQb09y93JflJXGfAPwqqJfZXsAv3L3F6WU/zNwEPAQIfEXmjcOTSj7MPBHQq3gcnffXFqmqOzBhM/Eze7+EwvDWF/r7l9OKf8lQs3iMnb9AbJbU6CZfY4wSuiKtOcvKlvNjN5TCEnucOD7hGbOT3odlveodiCBmc0idFrDMJ3WFT7/fuWOJ9XsLWFgRNJ9Rcc+QfhhUOio/kfgUnevaLJrvSkRjICZvRb4tYcZsp8i/FF8NuUPsqLOpCqf/9PxamJ7o7u/KeExJxK+7IZr4642lhuBTxM6xF9NGB/d5u5J49Uxs18z1N4+2GTh7l+Lx1P/iOoY8zJCB/e2eHsioYM7ccJh2hdEyhfDNHffmFR+pCysw5QQRuKSIr2EuRHbCM1xqf08w3XgJ5SveKRTNWqIYx927y8Zdi2xerIwN+Noj8tFxBrB9e7+rDKP6SLMG4Aweqlcc2CmlAhGoPClbmYvBD5LqAqeldQZZWa3uPuRZnYV8J+EzqSfufuBdYij4l+2FoZCHksYcngJIZGlzR6tJoZCoru78OFPG+kRj93j7oeUOd+Ih9YOJ+VX2SXu/sU6nLuaX9eFGuYu0mpTNcSyF7svupg0bPOP7v6cos/1eOD37n5kSbl24F53f0bpOeoUb0VxxLJfJtRM7mWoaco9Zb2orJjZWwgDGAo1opMJs+F/MMzjZrPr/8sjmQVZhvoIRqbwS/ZVwIXu/qtYFU9SUWdSjSpub3T3U+Mf1isIa+v8l5ld7e7vGGEM22Kz03Izey9hQtLUMuWHa2+fXa7pK6nZq1ru/nkzu5LQrgxwah1/lVWzXk7xMheTCF8iaTPDAYgjU0qTTNIw1qRFF28i/JIvVdGMXg/zI5YVd9LWWTUzi/+RMNO6rjXcarn7xRaWJynUyk5097QRRpjZCYS1o55GmDA3j/B5SRxunTUlgpF5zMJwyZcBX45NC2l7PFTbmVSNi4HbzKz4l+330wq7+474BeiE9vd/JMzmrJqZ/cDd30wYWtlBmOL/WcIfxFsTyhc64sYBp5rZgyS3t7cTEslIRwiVFZvxRrRqZ4qK18tx98dL7vqGhdUp05rVLiC818cQRmadRHondzWLLhY68D/FUAd+YgyEmb/3mtlt7DpkuR6/xIcbSFDsQUIfU0MTAUD84k/98i/xWUJSvibWfo4BdmvKHS1qGhoBC7NyjwPu9jAevxN4lrv/JqFsVZ1JNcRSUXujmb2CUJU+mrC0wqWEVQ9rah6KHagvJUzVP5qSL+7SEVSVdsSNRtNQlixO8ovNdqcTftXeljKQoPh1thFqCO/2lMmGRU0mhX+nAle6+98nlL3d3Z9rZncShnxuM7N7PWWiXxWvr+KZwlWcM6kGODi0N2Xww8+BZxOWayjuOH9/rXGMBjPrcffu2LfwHHcfMLM/pf2fZ001ghHwMFb8sqLbq0hfVKvNzPYs6Uyq5/t/Z3zucfH8adX2txD6Bt5Vp+r0BYQ/wgMYWpnTi/7d5YsvqWM1RaY1gVFQza/arxVd7yes25M21BRgS/y3z8yeBjxO+oqiFS+6aGaJv/6TmpxG8oVfRi2TrS4vKpcnT8UEfgPwIzNbS1HNarSpRjBKau1MqvDc7yOM2FlD6LdIHdYYy89haAbobe6+tg4xnO/u7x6+ZMXn26u0NpEnNszw2BGe+1OEfqaXAP9FSLgXpo3QKnrciwmLE/7a3bcnHP9I0c1JhMld96V0cCfNPt9AmC/xER/BOjrVDH6Ixxu+Q1m1LKzntBUGlwafDvwooZlwdOJRIhg9FsaLFzqTri3XmVTleVcQqv3DfojM7GRC38R1DE3A+ai7/6wesUhgwwyPLSk7nZDIC1901xN22dpQWjbhsRMJ8yqGLVuteO6r3P3ohGOfJayW+mPC5+j1hI1n7iA0a+32mCqet+JhvVbFLoGSTolgDLAwrvxllbTzxzbJlxVqARZmhF7TqLbJsWq44bElZX9OWO6i0MH/ZuDZ7p64KF4cmno6oU/ICctFJ64+OhKxaet2dz8o4dhu7dkWV6AdaVt3NcN6Y6f6scB1hf62at770ZZSk4Iy8ztGg/oIxoYHgevM7Ffs2mGWNMSyraQp6HHSRzpJ7apZjuJAd//nottnx87dNBcT1t8pbJLzRsJw1d1WxaxG0YguCKO29mbX9auK9VmYUFmoSZ5EaOqA5C+6ilU5rDdph7IRLXWRJa9tHa/MKRGMDY/Ey4R4KefXFia1/STefh1hMw2pgyqGxxbbYmYv9LgyqYXVRbcklCuoZlXMahxfdL0fWFOmlnkKYSG9bxFe7y3Am2J7/XtHGkgVw3qr3aFMEqhpaAwxs444kmm4cv9M2KgEwozNxI05pHqVDo8tecxhhGah6fGuJ4G3uvtdKc/xQ8IqpcWrYr7H3d9SY8xlJ681c6d9HML9CXZdO+uz9W4mG+uUCMYAC5vfXARMdfd5ZvZswvDQ0xscmlQgdoaeROhsnUHoZN5thJHtuirmIkItEOKs1JJaQjXPX7yI4jxCIrIYyyOesISzhU2ZzgfmuPshFvbfPcHd02bWSxNTIhgDzOxWwhfJ5WkdZs3aSSWDI4yeIjSFpI4wqqW2UWUcFwK/8LhSaZx8+I/u/q6EstcDHwW+3YhOWjP7hrt/0MwWk7xOk0YNVUF9BGOEu68s6TDbWXK8KTupBKhww5uiWdeJW1vWIY4j3X1wAxh3v9LMvpJStsPdbyv5zI148cIqFObffHUUn3PMUiIYG1aa2fMBt7Cg3AdIX+BMmk9VG96w69aW3yFsrJO6tWUV/mZmnwR+GG+fQlglN8n6mJAKG9OcRPqs+rpz96Xxag+wxcPGSIWVUSeOVhxjhZqGxgALG3OcS1jzx4DfAO9v5k4+2W2E0QLCMODhRhgNrsNkZh8jfAl+0+qwblXsNC6e2HYDcHbS58jMDiAkoecT+hQeAk4ZafNUtczsFuCl7r4p3p5KWDvr+aMZR94pEeSYme3r7itTjh3v7ktGOyapXK1t/rFP6BuE0TKvdveHRqt9PmFhuMmEeSiboT7Lg1cZz53ufthw90l5ahrKt6vN7Dh3f7j4TjM7lbDYmRJBExvBr+dTCVtbfj4mgf0ZajOvWZxl/jHK76tdujBcYb/nN1N+v+esbDazw+O8A8ysm/JzMCSBagQ5ZmavJPwyfJW7L4/3fZww0/QV7v5oI+OTfLEq9tWudmG4rMQv/ksY6svoBF5X1IcgFVCNIMfc/Qoz2wZcaWaFzWWeB7zI43LXMvZYdltbznT3i8zsAx6Wmb7ezG5PKVvxrngZ2x94DmH+w4nAEYxwiYtWpESQc+7+29gUdB1hav2xmlU55lW9tWWFCltErrKwFebfypw3aVe879Uhhmp9yt1/amHPhWMIw0nPJyQEqZCahnKsaJKYEYbM7WDX/Qg0SaxFmNlSd+8a4TmOJ2ynuS9D+2qf7e6JG79Y2FmtsDBc6q54WbKhje6/SNgp8Mf1GEHValQjyDFNEmtNlry15Yj+luP4+wVxpNkGwq/rsqpYGC5L1ewbLilUIxDJmbj/ROEPt7C15Vfd/YERnvc2d3/eCMMbVVbFvuGSTolAJGfixjSFbTALNYHdFqmr4bxfJyxodwlF++cWhmbK2KWmIZH8+T+GFqmr58CAwiSss+O/Rqh5HJtcXMYKJQKR/KlokboaLGFo8AHx+kYzO8zdy+2YJjmnThWR/LnJzJ6VwXm7CBPJOoGnAe8CXg5cGNc1kjFKfQQiOVHrInVVnP8G4JUlC7j9itAZu7TWjW+k+alpSCQ/jh++yIjMJiSWgh2EHci2xBnsMkYpEYjkxCgs8fwj4FYz+2W8/Wrgx2Y2Bfhzxs8tDaSmIREZFBdxe0G8+Qd372lkPDI6lAhERFqcRg2JiLQ4JQIRkRanRCAtzcw+YWb3mtldZnanmWW2fLGZXRfb4EWaikYNScsys6MIQzIPd/dtZjYLmNDgsERGnWoE0so6gfXuvg3A3de7+9/M7Cwzu93M7jGz75iZweAv+q+bWY+Z3WdmzzWzy8xsuZl9LpaZb2b3m9mPYpmfxRUyd2Fm/2BmN5vZHWb20zh5CzP7kpn9OdZQvjqK74W0MCUCaWW/AfY1swfM7Ftm9uJ4/3nu/lx3PwSYzK4Tuba7ezdwAWHj9vcAhwBvM7OZscwi4Fvu/kxgI3B68ZPGmscngZe6++FAD/Dh+Ph/Av4uzhL+XAavWWQ3SgTSsuJSCl3AacA64BIzextwjJndGpd0OBb4u6KHFXbruhu4191XxRrFg4SdvQBWuvsf4vUfAi8seeojgYOBP5jZnYRN4vcjbAizFbjIzE4E+ur2YkXKUB+BtDR330nY7/m6+MX/LuBQoNvdV5rZZwj7AhcUlloYYNflGAYo2hug9GlKbhtwtbu/oTQeM3se8BLgJOC9aAloGQWqEUjLMrNFZrag6K7DgGXx+vrYbn9SDaeeFzuiAd4I3Fhy/BbgBWZ2UIxjipktjM833d2vAD4EPLuG5xapmmoE0sqmAt80sxmELR9XEJqJngLuAVYDt9dw3mXAe8zsvwlr9JxffNDd18UmqJ/EPXYh9Bn0Ar+MO5AZ8OEanlukalpiQqSOzGw+sCR2NIvkgpqGRERanGoEIiItTjUCEZEWp0QgItLilAhERFqcEoGISItTIhARaXFKBCIiLe7/A1RoLwwvUBY7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And we can use the Frequency Distribution to graph our sentence by word freqency\n",
    "fdist.N()\n",
    "fdist.plot(30,cumulative=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# But... the word \"a\" doesn't really tell us much about that sentence, does it?\n",
    "# \"a\" is what is considered to be a STOPWORD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"haven't\", 'why', 'so', \"mightn't\", 'your', \"won't\", 'm', 'for', \"isn't\", 'between', 'this', 'didn', \"you're\", 'ain', 'not', \"it's\", 'itself', 'what', 'some', 'now', 'll', 'by', 'but', 'nor', 'ourselves', 'those', 'yours', 'of', 'she', 'to', 'doesn', 'until', \"needn't\", 'herself', 'is', 'our', 'does', 'then', 'from', 'haven', \"hadn't\", 'or', \"she's\", 'over', \"didn't\", 'are', 'will', \"shan't\", 'very', 'too', 'had', 'off', 'through', 'he', 're', 'under', 'myself', 'when', 'don', 'each', 'be', 'ours', 'most', 'o', 'y', 'am', 'hers', 'which', 'can', 'yourselves', 'wasn', 'd', 'there', \"shouldn't\", 'wouldn', 'whom', 'into', 'an', 'in', 'a', 'just', 'you', 'them', \"that'll\", 'theirs', \"weren't\", 'they', 'it', 'been', 'having', 'that', 'themselves', 'and', 'about', 'further', \"wouldn't\", \"you'll\", 'as', 'my', \"wasn't\", 'needn', \"doesn't\", 'its', 'won', 'again', 'where', 'mustn', 'than', \"you'd\", 'both', \"should've\", 'weren', 'being', 'shouldn', 'me', 'ma', 'were', 'hasn', \"hasn't\", 'more', 'own', 'with', 'yourself', 'no', \"couldn't\", 'shan', 'on', 'hadn', 'should', 's', 'only', \"mustn't\", 'while', 'aren', 't', 'we', 've', 'these', 'doing', 'how', 'same', 'him', 'few', 'i', 'below', 'who', 'her', 'the', 'before', 'have', 'all', 'couldn', 'his', 'do', 'after', 'above', 'at', 'other', 'any', 'if', 'their', 'such', \"aren't\", 'against', 'up', 'because', 'did', \"you've\", 'has', 'was', 'once', 'here', 'out', 'mightn', 'down', 'during', 'himself', 'isn', \"don't\"}\n"
     ]
    }
   ],
   "source": [
    "#=================================\n",
    "# STOPWORDS \n",
    "#=================================\n",
    "stop_words = set(stopwords.words(\"english\"))\n",
    "print(stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenized text: ['Kendra', 'loves', 'cats', '.', 'In', 'fact', ',', 'she', 'has', 'TEN', 'cats', '.', 'If', 'she', 'did', \"n't\", 'have', 'a', 'house', ',', 'a', 'husband', 'and', 'a', 'graduate', 'degree', 'in', 'data', 'science', ',', 'she', \"'d\", 'be', 'a', 'cat', 'lady', '!']\n",
      "Filterd text: ['Kendra', 'loves', 'cats', '.', 'In', 'fact', ',', 'TEN', 'cats', '.', 'If', \"n't\", 'house', ',', 'husband', 'graduate', 'degree', 'data', 'science', ',', \"'d\", 'cat', 'lady', '!']\n"
     ]
    }
   ],
   "source": [
    "#=================================\n",
    "# REMOVING STOPWORDS \n",
    "#=================================\n",
    "filtered_text=[]\n",
    "for w in tokenized_example:\n",
    " #   print(w)\n",
    "    if w not in stop_words:\n",
    "        filtered_text.append(w)\n",
    "print(\"Tokenized text:\",tokenized_example)\n",
    "print(\"Filterd text:\",filtered_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fdist_filtered = FreqDist(filtered_text)\n",
    "# fdist.N()\n",
    "fdist_filtered.plot(30,cumulative=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# But wait, doesn't the word \"cat\" appear more than 2 times? Oh! That's because we have \"cat\" AND \"cats\" \n",
    "# which the computer is counting as two different words!"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}