topic modelling using LDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tqdm\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from gensim import corpora,models\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "from gensim.parsing.porter import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
    "from nltk.stem.porter import *\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/abcnews-date-text.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1186018, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>publish_date</th>\n",
       "      <th>headline_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20030219</td>\n",
       "      <td>aba decides against community broadcasting lic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20030219</td>\n",
       "      <td>act fire witnesses must be aware of defamation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20030219</td>\n",
       "      <td>a g calls for infrastructure protection summit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20030219</td>\n",
       "      <td>air nz staff in aust strike for pay rise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20030219</td>\n",
       "      <td>air nz strike to affect australian travellers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publish_date                                      headline_text\n",
       "0      20030219  aba decides against community broadcasting lic...\n",
       "1      20030219     act fire witnesses must be aware of defamation\n",
       "2      20030219     a g calls for infrastructure protection summit\n",
       "3      20030219           air nz staff in aust strike for pay rise\n",
       "4      20030219      air nz strike to affect australian travellers"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date'] = data.publish_date.apply(lambda x:pd.to_datetime(str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.publish_date.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2003-02-19\n",
       "1   2003-02-19\n",
       "2   2003-02-19\n",
       "3   2003-02-19\n",
       "4   2003-02-19\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_freq = data['date'].apply(lambda x : x.year)\n",
    "year_freq = year_freq.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    91928\n",
       "2012    89008\n",
       "2014    81531\n",
       "2008    79991\n",
       "2011    77778\n",
       "2015    77290\n",
       "2007    77174\n",
       "2009    76444\n",
       "2010    74931\n",
       "2005    73124\n",
       "2004    72674\n",
       "2006    66901\n",
       "2003    63997\n",
       "2016    61574\n",
       "2017    47511\n",
       "2018    40102\n",
       "2019    34060\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zbCDKOVfZyrEibcI5x3PLyrnokbnM21zB9y7PY8YXx5KTkcQNI7OZdsdo9h+pY+ofFlC0bZ/XcUPGjKXl9E5rT37Pjl5HCWkfLv3dB1X60jr+KvxHgOFmNg94C/gOcBfwAzNbSPPZ/kzn3FKay3wh8GzLGICvt2KsyDnbU13DHf8o5mvPrCQnI4lX7x7PHeP7fGCv9oI+nXnhrnGkJcVz618W8dRi7WN+JiX7jrB4+36uHdEdM829P1cnlv5Nf1bpS+vYf98qDz/5+fmuuLjY6xgSxJxzPLtsBz/8z1rqGpv4nykD+MzYXh8o+g+rrqnny/9aztxNFXy2sDffuSzvtOMj2cOvb+S3c7aw4Jsfo1uHdl7HCRtLS/bz6ceXkJ4cz5OfK6BLaoLXkSRImNlS51z+yb6mhXckYu0+WMPtf1/CfTNW0r9LMq/ePYHPFvY+Y3mnJMTy+KfzuW1cL/46fzt3/GMJh2p0M9+HNTU1/zJVmJOmsm9jOtOXs6HCl4jjnOOZ4jIueuQdFm7bxwNXDOTpz4+hd1p7n58jJjqKB64YxINXD2be5ko+8cd3Kdt/1I+pQ8+7W/exo+oY1+Vr7r0/qPSltVT4ElF2Vh3jM39bwjdmriKvawqv3T2B28b1Puu13W8Z3ZN/3j6KPdW1XPX7BSzevr+NE4eumUvLSE6I4eKBmV5HCVsqfWkNFb5EBOccTy8pZcojc1m8fT8/uHIQT32ugF6tOKs/lbE5abxw1zg6tIvllr8UMaO4rA0Sh7bqmnpeXbObK4d2IyE22us4Ya259Eep9OWMVPgS9nZUHeNTjy/mm8+uZmC3FGbdM4FPj+3Vpju29U5rz/N3jmN07878z8xV/PSV9TQ2he8NsWfy0spd1DY06XJ+gIzo2VGlL2ekwpew5ZzjycXNZ/VLSw7wo6sG8eTnCsju7J/NW1ITY/nbbSP5ZEFP/jR3G194YimHaxv88lrBbubSMnIzkhja/SOLYYqfqPTlTFT4EpbKDxzlU48v5tvPrWZIViqz7pnAJ8e07Vn9ycRGR/GjqYP54VWDmLNxL9f+8V3KD0TWzXxb9h5mWWmV5t57QKUvp6PCl7DinGP6ohKmPDKXZSUH+PHUwUy/Y3TAt2T91Jhe/O0zI9lRdYypv1/A0pLIuJnPOcff391OdJRx9fAsr+NEJJW+nIoKX8JG2f6j3PKXRXz3+TUMy+7Aa/dM4NaCnn4/qz+VCf3Sef7OcbSPj+Gmxxbx3LJyT3IEyq6Dx/j035YwraiUa87PIiNZi8F4RaUvJ6OV9iTkNTU5pi8u5aevrMeA714+kJtG9Qiay8kHjtTxpelLKdq2nzsn9uW+i/t79kuIPzjnmLG0nB/9Zx0NTY7vXDaAW0Z794uW/NfSkgN8+vHFWpEvgpxupT0VvoS0sv1H+cbMVSzcto/xuWn89JohdO8Y2Mv3vqhvbOL+F9fw5OIypgzK5OHrh9E+PvS3it19sIZvP7eKORsrGNW7E/937VC/3RQpZ+fE0n/hrnGktov1OpL4kZbWlbBUtv8ol/xqLqt3HORn1wzhn7ePCsqyh+ab+X5y9RDu//hA3li3h+seXcjOqmNexzprx3cWvPiE1Qqf8uMMCDl7I3p25NFbR7C98ghvrtvjdRzxkApfQtYTRSXUNDTxn68UcuOo7KC5hH8qZsbthb3562dGUrr/KFf+bgHLSw94HavV9lbX8Ll/Nu8s2C8z+ZxXKxT/G9u3M53bxzF/i3YUj2QqfAlJNfWNPFPcfHm8NWvgB4ML+2fw3J1jaRcXxQ2PFfHiih1eR/KJc44XV+zgokfmMm9zJd+7PI+nvzCmTVYrFP+KijLG5aQxb3Ml4fw2rpyeCl9C0surdlF1tJ5bC3p6HeWs9MtM5sW7ChnWvQN3P7WCh1/fSFMQr8xXcaiWL05byt1PraBventeuXs8d4zvo22BQ8j43DQqD9eyYfchr6OIR0L/riGJSE8UldA3vT1j+nT2OspZ69Q+jml3jOa7z6/mN29tYd2uQ3zxgj6M6NkxaN6ecM7x0qpd3P/iGo7UNfKdywbw2UIVfSgan5sOwPzNleR1TfE4jXhBhS8hZ82Og6woq+KBKwYGTTGerbiYKH5x7Xn075LM/72+kTfX76F7x3ZcNawbU4dlkZuZ7Fm2fYdr+f6La3hl9W6G9ujAQ9edR06Gd3nk3HRJTSAnI4l5Wyr53IQ+XscRD6jwJeRMKyqhXWw01wzv7nWUNmFm3DG+DzeOyub1tbt5YcVO/vj2Vn4/ZyuDuqUwdVgWVw7rRmZK4OZQv7J6F99/YQ2Hahr4xiX9+fz4PsRE6x3AUFeYk8ZTS0qpqW/ULoYRSIUvIeXgsXpeWLGDq8/PCrv5xEnxMVwzvDvXDO9OxaFaXlq1kxeW7+DBV9bzk1fXM6ZPZ6aen8Ulg7uQkuCfY99/pI77X1zDS6t2MSQrlYeuH0o/D68ySNsan5vG3999j2UlBxibk+Z1HAkwFb6ElGeXllNT38Qto0PzZj1fpSfHc9u43tw2rjfbKg7z4oqdvLhiB9+YuYrvvbCGyXkZXDUsi4n904mPaZsztdfW7OZ7L6zm4LF67ru4H1+4oC+xOqsPK6P7dCYmypi3pVKFH4FU+BIynHNMW1TC+dkdGJwVOduu9klP4t6L+nHP5FxWlFXx4oqd/GflTl5ZvZvUdrFcNqQrU4d1Y2SvTmc1F77qaB3/+++1vLBiJ4O6pfDEZ0frpq4wlRQfw/DsjszfXMk3L/E6jQSaCl9CxsKt+9hWcYSHrx/qdRRPmBnnZ3fk/OyOfPfyPOZvqeTF5Tt4YfkOnlxcSlaHdlzZcrNf/y6+XYZ/Y90evvP8ag4cqePeyf2480Kd1Ye7wtw0HnlzE/uP1NGpfZzXcSSAVPgSMp4oKqFjYvMZbaSLjY7iwv4ZXNg/g6N1Dbyxbg/PL9/BY3O38ce3tzKgSzJXn998s1/X1HYf+f6DR+v5wUtreW7ZDgZ0Sebvt41kULfIuWoSyQpz03j4jU0s2FLJFUO7eR1HAkiFLyFh98EaXl+3hzsKe+vu4g9JjIvhqmFZXDUsi8rDtby8ahfPL9/BT1/dwM9e28Do3p2YOiyLS4d0JbVdLHM27OVbz62i8nAdX52Uy5cvzCEuRmf1keK8rFRSEmKYv1mFH2lU+BISnlxcSpNz3Dw62+soQS0tKZ5Pj+3Fp8f24r3KI+/f7Pet51Zz/4trGZSVwvLSKvpnJvOXT41kSHed1UeamOgoxvZNY/6W5mV2Q30tC/GdCl+CXn1jE08tKeWCfun07Kx1233VK609d0/O5auTclhVfpAXVuxg3uZK7rqwL1+dlNtmd/dL6CnMTeO1tbvZXnmEPulJXseRAFHhS9B7c90e9lTX8pOrw3sqnr+YGUN7dGBojw5eR5EgMT63eUre/C2VKvwIojfuJOg9UVRCVod2TOyf4XUUkbDQs3N7enRqx7zN2i43kqjwJaht2XuYd7fu4+bR2dqwRaQNFeakU7R1Hw2NTV5HkQBR4UtQm76ohNho44aRPbyOIhJWxuemcai2gZXlVV5HkQBR4UvQOlrXwMyl5Vw2pCtpSfFexxEJK2P7dsYMXdaPICp8CVr/XrGTQzUN3Fqgm/VE2lqHxDjOy0pV4UcQFb4EJeccTxSVMKBLMvk9O3odRyQsFeamsaKsiuqaeq+jSACo8CUorSirYu3Oam4t6KmFQUT8ZHxuOo1NjqKt+7yOIgGgwpeg9ERRCUnxMUw9P8vrKCJha3h2RxLjopm/RZf1I4EW3glTjU2OysO17D5Yw+7qGvZU1zR/frCGisO13DI6m0sGB+cmNPuP1PHSql3ckN+DpHj9LyriL3ExUYzu3Yn5eh8/IuinaQg6VtfI7uMFXn2M3Qdr/1voLeW+91AtjU3uA98XE2VkpiTQ2OS49+mV9O+SQu+04FuqdkZxGXUNTbpZTyQACnPTmbNxHTuqjpHV4aM7K0r4UOEHmYNH6yk7cLS5wKtr2NNS4rsO/vcsvbqm4SPflxwfQ2ZqAl1TE8jJSKNLSgKZqQl0SUlo+TyetPbxREUZuw/WcPEj73DfjJU884UxQbWgTVOTY/qiUkb17uTznu4icvbeX2Z3cwU3jNTmVOFMhR9EFm7dx6cfX0zdCStfRRmkJ8fTJSWBXp3bU9CnM10+UOTN/2zfikvfXVIT+NHUwdz91Aoem7uNL03s64/DOStzN1dQuv8o903p73UUkYiQm5FEZko88zZXqvDDnAo/SDjneOj1jXROiuOBKwbSJbUdXVISSEuKIya67e+tvHJoN15bs5tH3tjEhQPSGdAlpc1f42xMKyohLSmOSwZ18TqKSEQwM8blpDFnw16amhxRQXTFT9qW7tIPEgu37aO45ABfvKAvlwzuyrAeHeiSmuCXsofmv+Q/njqYlHYxfO3pldQ1eL+edvmBo8zesJcbR2YTF6P/NUUCZXxuGgeO1rN2Z7XXUcSP9FM1SPx29hbSk+MDumZ856R4fnrNeazbVc3v3tocsNc9lScXl2LATaN1WVEkkMblNL+PP29LhcdJxJ9U+EGg+L39LNy2jy9M6ENCbHRAX/uigZl8Ynh3fv/2VlaWebeJRm1DI08vKeNjAzJ1p7BIgGUkJzCgS7Km54U5FX4Q+M1bW+jUPo6bPTqzfeDKgWQmx/O1Z1ZQU9/oSYbX1uym8nAdnxyjqXgiXhifm0bxewc4VufNzwDxPxW+x1aUVTF3UwWfG9+HxDhv7qFMSYjlF9cOZWvFEX45a6MnGaYXldKzcyLjWy4tikhgFeamU9fYxOL39nsdRfxEhe+x387eTIfEWM/PbAtz0/jUmJ48vmA7RdsCu672ht3VLH5vP7eO7qk7hEU8MqpXJ+Kio5i/We/jhysVvofW7DjI7A17uX1c76BYQvZblw6gZ6dE7puxksO1H13cx1+mFZUQFxPFtSO6B+w1ReSD2sVFk9+ro7bLDWMqfA/97q0tJMfH8OmxvbyOAkBiXAwPXT+UnVXHePDldQF5zcO1DTy/bAdXnNeNju3jAvKaInJyhblpbNh9iIpDtV5HET9Q4Xtk4+5DvLZ2N58Z14vUdrFex3nfiJ6d+NyEPjy5uIw5G/f6/fWeX76DI3WNnr+lISIwPicdgAXaPS8sqfA98rs5W2gfF83t43p7HeUjvnZRP/plJvHNmauoOlrnt9dxzjFtYQlDslIZ2j3Vb68jIr4Z1C2FjomxzNX7+GFJhe+BLXsP89KqnXxyTK+gvIwdHxPNw9cPY/+ROh7491q/vc6S9w6wcc8hbi3Ixkw364l4LSrKGJuTxvzNlTjnzvwNElJU+B74w5wtxMdEccf44Du7P25wVipfnZTLiyt28srqXX55jSeKSkhOiOHKoVl+eX4Rab0JuWnsPVTL5r2HvY4ibcwvhW9mnzGzt1s+isysxswKzGyRmS0wswdaxkWZ2aNmtrBlbE7L4z6PDTUl+47w4sqd3DK6J2lJ8V7HOa0vTezLed1T+e7zq9v8Jp6KQ7W8tmYX143oQbu4wK4uKCKnVpjb/D6+7tYPP34pfOfc351zE51zE4GlwFeBR4GbgUJgtJkNB6YCCc65McC3gIdanqI1Y0PKH+ZsJTrK+MKEPl5HOaPY6Cgevn4oR+oa+fZzq9v0Et8zxWXUNzpuKdC6+SLBJKtDO/qktdd8/DDk10v6ZpYPDAKeAuKdc1tdc2vMAibRXOivATjnioB8M0vxdaw/s/tD+YGjPLusnJtG9iAjJcHrOD7JyUjmG1P68+b6PTy7bEebPGdjk2N6UQnjcjrTNz2pTZ5TRNpOYW4ai7bvD4pdNKXt+Ps9/O8APwBSgBP3XTwEpLY8fvCExxtbM9bMPrJajZl93syKzay4oiK4fkN99J2tmMEXLujrdZRWuX1cb0b17sQP/r2WHVXHzvn53tqwl50Ha/hkgabiiQSjwpw0jtY1sqz0gNdRpA35rfDNrAMwwDk3h+YCTz7hy8lA1Ukej2rNWOfcR5aDc8495pzLd87lp6ent8mxtIXdB2t4Zkk5147oQbcQ2w0uKsp46LqhNDrHN2aupKnp3C7tTysqITMlnsl5mW2UUETaUkHfzkRHmXbPC69hTFsAACAASURBVDP+PMOfALwJ4JyrBurMrK81z7+aAswDFgCXQfONesDq1oz1Y/Y29+g7W2l0jjsnhtbZ/XE9OiXyvcsHsmDLPqYtKjnr5ynZd4R3NlVw06hsYqI1SUQkGKUkxDKsRwfmaQGesOLPn7j9gW0n/PmLwHRgMbDcObcIeB6oMbN3gUeAe89ibNDbe6iGJxeXcvX5WfTolOh1nLN206geXNAvnZ+8sp7tlUfO6jmmLyolOsq4aZRu1hMJZoU5aawur/Lr4lsSWD4VfsuUuGgzG29mPq0U45z7pXPuVyf8ucg5V+CcG+mc+27LY03OuS8658Y658Y45za0dmwo+Mu87dQ3NnHXhSE5k/B9ZsbPP3EecdFRfP2ZFTS28tJ+TX0jzxSXMWVQJpkhctOiSKQan5tGk4N3twZ290zxnzMWvpn9HPgc8CPgu8Cf/R0qnOw7XMsTC0u4cmg3eqe19zrOOeuSmsCPpg5mWWkVj83dduZvOMHLq3ZRdbSeW3WznkjQG9qjA8nxMZqPH0Z8OcMvdM79CRjjnLsE0B6mrfDX+dupaWjkyx8L7bP7E105tBuXDu7CI29sYsPu6jN/Q4snikrom96eMX06+zGdiLSF2OgoCvp2Zv6W4JrtJGfPl8KPNrNRwHstl/OD59b3IFd1tI5/LizhssFdyclIPvM3hAgz48dTB5PSLoavPb3Sp7m6q8sPsqKsilsLemrdfJEQMT43jbL9xyjZd3b37Ehw8aXw/wH8Fvg/4BfAr04/XI7724L3OFzbEFZn98d1TornJ1cPYd2uan771uYzjp9WVEK72GiuGa4LRCKhojAnDdAyu+HCl8Ivc86Nds6tdc7dA2hHBR8cqqnnbwu2c9HATPK6pngdxy8uHtSFTwzvzh/e3sqKsqpTjjt4rJ4XV+5g6vndSG0XG8CEInIueqe1J6tDO83HDxMfWanuODP7ODAOuMnMxrY8HAVcBTwTgGwh7Z8LS6iuaeCrH8v1Oopf3X/FQN7dWsnXn1nBy18dT0LsRzfCeXZpOTX1TdwyWjfriYQSM6MwJ41X1+yisckRHaW340LZ6c7wVwIbgGPAxpaPtcBNAcgV0o7UNvCXedu4sH86Q7qneh3Hr1LbxfLLa4eyteIIv5y18SNfd84xraiE87M7MDgrvP9diISjwtw0qmsaWFV+6qt4EhpOWfjOuTLn3D9o3vzmXWAvMIfmXwTkNKYVlXDgaD1fmRTeZ/fHFeam8akxPXl8wXaKtn1wzu67W/exrfKI1s0XCVHjctIwQ5f1w4Av7+HfSfN2tT8BPkHzDXxyCsfqGvnzvG0U5qQxPLuj13EC5luXDqBnp0Tum7GSw7X/3eJgWlEJHRNjuWxIVw/TicjZ6tQ+jkHdUnTjXhjwpfBvBCYDVc65XwOj/RsptD25uJTKw3V8JQzvzD+dxLgYHrp+KDurjvHgy+uA5g2DXl+3h+vze5z0vX0RCQ3jc9NZVnrgA7/MS+jxpfCPjzm+jmqtn7KEvJr6Rv40dyujendidAQuLjOiZyc+N6EPTy4uY87GvTy5uJQm57h5tNbNFwll43PSaGhyLNqmZXZDmS+F/y9gLpBjZq8AL/g3UuiasbScPdW13B0h792fzNcu6ke/zCS+OXMVTy4u5YJ+6fTsHPpLCotEshG9OpIQG6XL+iHulNPyjnPO/c7MZgODgY3OuVX+jxV66hqaePTtrQzP7sDYvpF3dn9cfEw0D18/jKm/X0BDk+OnullPJOTFx0Qzqndn5mu73JDmy+Y5WTRvmnMHMNrM9B7+STy3rJwdVcf4yqTciF86dnBWKvdfMZAL+qUzsX+G13FEpA2Mz0ljy97D7Dp4zOsocpZ8uaT/GPA4EEfzpf1f+zVRCGpobOIPb2/lvO6pTOynrQYAPjWmF/+4fZQW6hAJE4W5zcvsanpe6PKl8BOcc28Bzjm3Eajxc6aQ8+KKnZTuP8qXL8yJ+LN7EQlPA7okk5YUr8v6IcyXwq81syk075pXgAr/AxqbHL+fs4W8rilcNDDT6zgiIn7RvMxuZxZsqaSpyZ35GyTo+FL4nwduA9KA+4Av+TVRiHl59S62VR7hKx/T2b2IhLfC3HQqD9exfne111HkLJzxLn3gVuB+59wmf4cJNU1Njt+9tZncjCQuGdTF6zgiIn51fLvc+ZsrGdRNe2OEGp+2xwV+aGYLzOx/zew8f4cKFbPW7mbTnsN8+WM5ROnmNBEJc11SE+iXmaT38UPUGQvfOTcduAX4HjAFWOzvUKHAOcdv39pC77T2fPy8bl7HEREJiMKcdBZv309NfaPXUaSVfJmH/wKwCLgGeJDm9/Ij3uz1e1m3q5o7J/bV1DMRiRjjc9OobWii+L0DXkeRVvLlkv4iYA/QA+gDZPk1UQhoPrvfTI9O7Zh6fsT/6xCRCDK6Tydio415Wyq8jiKt5Msl/Z865y4HfgxcDazwe6ogN3dzJSvLD3LnxBxio335nUlEJDwkxsUwPLujFuAJQb5c0v+tma0AvgH8GYjoyebOOX4zezPdUhP4xPDuXscREQm48blprN1Zzb7D2jw1lPhyeloCFDjnrnfO/Qs438+ZgtrCrftYWnKAL07sS1yMzu5FJPIU5jYvIb5gq7bLDSWnnIdvZuOBgTRvmlPdsqhMFPBlmnfOi0i/eWszGcnxXJ/fw+soIiKeGJKVSmq7WOZtquDKoZqlFCpOd4p6AOgCxANdWz7Sab60H5GWvLefom37+cIFfUmIjfY6joiIJ6KjjLF9m7fLdU7L7IaKU57hO+fWAGvM7M/OuZ0BzBS0fjN7M2lJcdw8KtvrKCIinhqfm86ra3azteIIORlJXscRH/hyl77KHlheeoB5myu5Y3wf2sXp7F5EItv497fL1fS8UHHKwjczLZR8gtfX7aFT+zhuLejpdRQREc/16JRIz86JWmY3hJzuDP8/AGb2xwBlCWpfvjCHl79aSFK8L/sNiYiEv8KcNIq27ae+scnrKOKD0xX+MTNbAtxkZu+2fCw0s3cDFS6YtI+PoWtqO69jiIgEjfG5aRyubWBFWZXXUcQHpztdvRToBvwJ+BKgBeNFROR9Y/qmEWUwb3MlI3t18jqOnMEpz/Cdc03OuXLgKuDjNE/HmwrsClA2EREJYqntYjmvewfduBcifFkq7k9ADvAG0Av4iz8DiYhI6Bifm8aKsioOHqv3OoqcgS+Fn+uc+5pz7gXn3L00l7+IiAiFOWk0ueZlxyW4+VL4CWaWCGBm7QBNQhcREQDOz+5I+7ho5mu73KDnyxyzXwMrzWwNzWvrP+DfSCIiEiriYqIo6NNZ2+WGgDMWvnNuupm9CvQBtjvndN1GRETeV5ibxuwNeynbf5QenRK9jiOn4NP+rs65/c65YpW9iIh82PvL7GrVvaCmDd1FROSc9E1PoktKgi7rB7kzFr6Z3ReIICIiEprMjMLcNBZsraSxSdvlBitfzvAvMzPdmS8iIqc0PjeNqqP1rN150Osocgq+FH4asNPMiiJ5LX0RETm1cTnN7+PP02X9oOXLtLwr/J5CRERCWlpSPIOzUpizYS93Xaj12YKRL2f4DcCDwGPAxUAXvyYSEZGQNGlAJstKD7DvcK3XUeQkfCn8x4DHgThgLs0L8YiIiHzA5LxMmhzM2ahV94KRT0vrOufeApxzbiNQ4+dMIiISggZnpZCZEs/s9Xu8jiIn4Uvh15rZFCDazApQ4YuIyEmYGZPyMpm7qYLahkav48iH+FL4nwduo/lu/fuAL/k1kYiIhKzJeRkcqWtk0bb9XkeRD/FlLf1yM/sJ0A9Y45zb7v9YIiISisb2TSMhNoo31+9hQr90r+PICXxZae97wB+AccBfzeweX57YzL7dMm9/qZl91sxyzGy+mc0zsz+aWVTLuAfMbLGZvWtmo1oe83msiIgEj4TYaApz0pm9fi/OadW9YOLTSnvABOfcvcAFwI1n+gYzmwiMpfmXhAuAHsDDwPecc+MBA64ys+EtXx/d8ry/b3mK1owVEZEgctHADHZUHWPD7kNeR5ET+FL4e4Hj+x3GAb7Mt5gCrAaeB/4DvASMAN5p+fqrwGSgEHjdNSsFYswsvZVjP8DMPm9mxWZWXFGhqSEiIoF24YAMAN2tH2ROWfgnLKM7GNhsZq8BG4DuPjxvGpAPXAd8EZgORLn/Xt85BKQCKcCJCy8ff9xaMfYDnHOPOefynXP56el6/0hEJNAykhMY2qMDb67f63UUOcHpbto746X709gHbHDO1QEbzayG5sv6xyUDVUB1y+cffrypFWNFRCTITB6QwUNvbGLvoRoykhO8jiOc5gzfOVfinCsBMoG7gW+e8HEm84FLrFk3oD0wu+W9fYBLgXnAAmCKmUWZWTbNVwEqgeWtGCsiIkFmUl4mAHM26Cw/WPiyec4/gJ8DB3x9UufcS2Y2AVhM8y8VdwHbgT+bWRywHpjpnGs0s3nAwhPGAXy9FWNFRCTI5HVNJqtDO95cv5cbRmZ7HUdofq/89APM/u2cuzJAedpUfn6+Ky4u9jqGiEhEuv/FNcwoLmf5/ReREBvtdZyIYGZLnXP5J/uaL3fpP2tmT5nZ/cc/2jifiIiEoUl5mRyrb+TdrXr3NRj4Uvh3AsuBPSd8iIiInFZBn060j4vW3fpBwpf38Pc7537u9yQiIhJW4mOimdAvnbfW78VNdZiZ15Eimi9n+JVm9icz+0LLojaf93sqEREJC5PyMtldXcPandVeR4l4vhT+FmAn0AXo2vIhIiJyRhf2T8cM3lind4O95ssl/b/5PYWIiISlzknxDM/uyOwNe7j3on5ex4lovpzhPw08BTwDLAH+5ddEIiISVibnZbJmRzW7D9Z4HSWinbHwnXNjnHNjnXMFQH9gh/9jiYhIuJic17KZzgZd1veSL2f4JzoI9PVHEBERCU85GUlkd0rkTb2P76kzvodvZgsBR/O+9OnAm/4OJSIi4cPMmJSXwfRFpRytayAxzpfbx6St+XKGfyNwU8s/xznnvujfSCIiEm4uysukrqGJ+Zu16p5XTvlrlpl96hSP45z7p/8iiYhIuBnZuxPJCTHMXr+Xiwd18TpORDrddZW8D/3ZgNuAo4AKX0REfBYbHcUF/dKZvWEvTU2OqCituhdopyx859y3j39uZjnA34GXgHv8H0tERMLN5LxMXlq1i5XlVZyf3dHrOBHnjO/hm9ldwKvAz5xzn3XOHfJ/LBERCTcT+6cTHWXM1mY6njhl4ZtZlpm9DowHRjnnXgpcLBERCTcdEuPI79mRN9drep4XTneGvwYYSvOUvN+b2b+OfwQmmoiIhJvJeZls2H2I8gNHvY4ScU53097UgKUQEZGIMCkvgwdfWc/s9Xv59NheXseJKKe7ae+dQAYREZHw1yc9iT5p7Xlz/R4VfoC1dmldERGRczJ5YCaLtu3ncG2D11EiigpfREQCatKADOoam5i3qcLrKBFFhS8iIgE1omdHUtvF8obu1g8oFb6IiARUTHQUF/ZP5+2NFTQ2Oa/jRAwVvoiIBNzkgZnsP1LH8tIDXkeJGCp8EREJuAn90omJMt7UqnsBo8IXEZGAS0mIZXSfTlp1L4BU+CIi4olJAzLZsvcwJfuOeB0lIqjwRUTEE5PzMgF0WT9AVPgiIuKJ7M6J9MtMYrYu6weECl9ERDwzKS+Txdv3c/BYvddRwp4KX0REPDM5L4OGJsc7WnXP71T4IiLimWE9OtK5fZwu6weACl9ERDwTHWVcOCCDORv2Ut/Y5HWcsKbCFxERT03Oy6C6poHi97Tqnj+p8EVExFPjc9OJi47SZX0/U+GLiIin2sfHMKZvZ2Zv0Hx8f1Lhi4iI5ybnZbC98ghbKw57HSVsqfBFRMRzHzu+6t46Xdb3FxW+iIh4LqtDO/K6pjBby+z6jQpfRESCwuS8DIpL9nPgSJ3XUcKSCl9ERILC5LxMmhy8vUln+f6gwhcRkaAwJCuV9OR47Z7nJyp8EREJClFRxqQBGbyzsYK6Bq2619ZU+CIiEjQm5WVyuLaBxdv3ex0l7KjwRUQkaBTmpBEfE8WbWnWvzanwRUQkaLSLi6YwJ43ZG/bgnPM6TlhR4YuISFCZlJdJ2f5jbNqjVffakgpfRESCyqS8DABd1m9jKnwREQkqmSkJnNc9VbvntTEVvoiIBJ1JAzJZXlZF5eFar6OEDRW+iIgEnUl5GTgHb2nL3DajwhcRkaAzqFsKXVMTdFm/Dfmt8M1suZm93fLxNzMrMLNFZrbAzB5oGRNlZo+a2cKWcTktj/s8VkREwo+ZMSkvg3mbK6mpb/Q6TliI8ceTmlkCgHNu4gmPrQA+AWwDXjaz4UAvIME5N8bMCoCHgKuAR1sxVkREwtCkvEymFZVStG0fE/tneB0n5PnrDH8okGhmr5vZW2Y2AYh3zm11zSspzAImAYXAawDOuSIg38xSfB17shc2s8+bWbGZFVdUVPjp8ERExN/G9OlMYly0pue1EX8V/lHg/4ApwBeBv7U8dtwhIBVIAQ6e8Hhjy2PVvow1s49coXDOPeacy3fO5aenp7fBoYiIiBcSYptX3Xtr/V6tutcG/FX4m4Bprtkmmou60wlfTwaqaC725A/l+fBjpxzrnGvwQ3YREQkSkwdmsvNgDet2VZ95sJyWvwr/dprfY8fMugGJwBEz62tmRvOZ/zxgAXBZy7gCYLVzrhqo82Wsn7KLiEiQ+NiADMxg9npNzztXfrlpD/gr8Hczmw84mn8BaAKmA9HA6865RWa2BLjIzN4FDLit5fu/2IqxIiISptKS4hnWowNvrt/DVyfleh0npPml8J1zdcDNJ/lSwYfGNdFc7h/+/iJfx4qISHibnJfJL2dtZE91DZkpCV7HCVlaeEdERILa8c10tOreuVHhi4hIUOufmUz3ju206t45UuGLiEhQMzMm52Uyb3Mlx+q06t7ZUuGLiEjQm5SXQW1DEwu2VHodJWSp8EVEJOiN7t2ZpPgYZm/QZf2zpcIXEZGgFxcTxQX90pm9fi9NTVp172yo8EVEJCRMystg76Fa1uw8eObB8hEqfBERCQkX9s8gyuDNdbqsfzZU+CIiEhI6to9jRM+OvL5ujzbTOQsqfBERCRlXDstiw+5DWlv/LKjwRUQkZNw4sgd909vz4CvrqWto8jpOSFHhi4hIyIiNjuJ7Hx/I9soj/HPhe17HCSkqfBERCSkX9s9gYv90fj17M/sO13odJ2So8EVEJOR87/I8jtY18vAbm7yOEjJU+CIiEnJyMpL5ZEFPnlxcyvpd1V7HCQkqfBERCUn3TM4lpV0sP355nabp+UCFLyIiIalDYhz3Tu7Hgi37eEOL8ZyRCl9ERELWLaOzyc1I4sFX1lPboK1zT0eFLyIiISumZZpeyb6j/OPd97yOE9RU+CIiEtIu6JfOxwZk8NvZW6jUNL1TUuGLiEjI++7leRyrb+Sh1zd6HSVoqfBFRCTk9U1P4lNjevHUkjLWavvck1Lhi4hIWLh7Ui4d2sXyo5c0Te9kVPgiIhIWUhNj+dpF/Sjatp9ZazVN78NU+CIiEjZuGpVNv8wkfqJpeh+hwhcRkbAREx3F9z8+kNL9R3l8/ntexwkqKnwREQkr43PTmZyXwe/nbGHvoRqv4wQNFb6IiISd714+kNqGRh6apd30jlPhi4hI2Omd1p5Pj+nFM0vLWLND0/RAhS8iImHqK5Ny6ZgYxw81TQ9Q4YuISJhKbdc8TW/x9v28uma313E8p8IXEZGwdePIHgzoksxPXllPTX1kT9NT4YuISNiKiY7i/o8PpPzAMf46f7vXcTylwhcRkbA2NieNiwZm8oc5W9hbHbnT9FT4IiIS9r57WR51jU38clbk7qanwhcRkbDXK609t4/rzcxl5awuj8xpeip8ERGJCHd9LIdOiXH88KW1ETlNT4UvIiIRISUhlvum9GfJewd4efUur+MEnApfREQixvX5PcjrmsJPX9kQcdP0VPgiIhIxoqOM+z8+kB1Vx/jLvG1exwkoFb6IiESUMX07c8mgLvzh7a3siaBpeip8ERGJON+5LI+GRscvXoucaXoqfBERiTjZnRO5vbA3zy4rZ2VZlddxAkKFLyIiEemuC/uSlhQfMbvpqfBFRCQiJSfE8j9T+rG05AD/WRX+0/RU+CIiErGuHdGDQd1S+Nkr6zlWF97T9FT4IiISsY5P09t5sIY/h/k0PRW+iIhEtNF9OnPZkC788e2t7D4YvtP0VPgiIhLxvn1pHo3O8YvXNngdxW9U+CIiEvF6dErkjsLePLd8B8tLD3gdxy9U+CIiIsCdF+aQnhy+0/T8VvhmlmFmZWY2wMxyzGy+mc0zsz+aWVTLmAfMbLGZvWtmo1oe83msiIhIW0mKj+F/pvRneWkV/1650+s4bc4vhW9mscCfgGMtDz0MfM85Nx4w4CozGw5cAIwGbgR+fxZjRURE2sy1w7szOCuFn726gaN1DV7HaVP+OsP/P+BR4PivSCOAd1o+fxWYDBQCr7tmpUCMmaW3cqyIiEibiYoyHrhiELsO1vDLWeG1zn6bF76ZfQaocM7NOvFh9983RA4BqUAKcPCEMccfb83Yk73+582s2MyKKyoqzvVwREQkwozs1YnPjO3F3xa8x9xN4dMj/jjDvx24yMzeBoYB/wQyTvh6MlAFVLd8/uHHm1ox9iOcc4855/Kdc/np6boIICIirfetSweQm5HEfTNWcuBInddx2kSbF75zboJz7gLn3ERgBfAp4FUzm9gy5FJgHrAAmGJmUWaWDUQ55yqB5a0YKyIi0uYSYqP51Y3DOHC0ju88vzos7tqPCdDrfB34s5nFAeuBmc65RjObByyk+RePu85irIiIiF8M6pbKfRf356evbuDZZTu4dkR3ryOdEwuH31pOJT8/3xUXF3sdQ0REQlRjk+PmPxexZsdBXr17AtmdE72OdFpmttQ5l3+yr2nhHRERkVOIjjIevmEYUVHG155ZQUNj05m/KUip8EVERE4jq0M7fjx1MMUlB3j0na1exzlrKnwREZEzuGpYFlcO7cav3tzMqvKTThILeip8ERERH/zoqsGkJ8dzz1MrQnIVPhW+iIiID1ITY3no+qFs33eEn7yy3us4rabCFxER8dHYvml8bnwfphWV8taGPV7HaRUVvoiISCt8/eJ+DOiSzDdmrqLycK3XcXymwhcREWmF+Jhofn3j+VTXNPCtZ0NnFT4VvoiISCv175LMNy8ZwJvr9/DUkjKv4/hEhS8iInIWbhvbi8KcNH74n3VsrzzidZwzUuGLiIichago4/+uG0pcTBT3PL2C+iBfhU+FLyIicpa6pCbw02uGsLKsit+9tcXrOKelwhcRETkHlw3pyjXDs/jdnC0sLTngdZxTUuGLiIicox9cOYiuqQl87ZkVHK4NzlX4VPgiIiLnKDkhloevH0bZ/qP86D/rvI5zUip8ERGRNjCqdye+NLEvTxeXMWvtbq/jfIQKX0REpI3cPakfQ7JS+dazq9hbXeN1nA9Q4YuIiLSRuJgoHrlhGMfqG/mfmauCahU+Fb6IiEgbyslI4ruX5fHOpgqeKCrxOs77VPgiIiJt7NaCnkzsn86DL69ny95DXscBVPgiIiJtzsz4xbXn0T4+hnueXkFdg/er8KnwRURE/CAjOYGfXTOENTuq+dWbm7yOo8IXERHxl4sHdeHGkT344ztbWbx9v6dZVPgiIiJ+9P2PDyS7UyL3Pr2C6pp6z3Ko8EVERPyofXwMj9wwjN3VNfzvi2s9y6HCFxER8bPh2R35ysdyeG75Dl5atdOTDCp8ERGRAPjyhTkM69GB7z6/hl0HjwX89VX4IiIiARAT3bwKX31jE/fNWElTU2BX4VPhi4iIBEjvtPbc//GBLNiyj8cXbA/oa6vwRUREAuiGkT24aGAmv5i1kQ27qwP2uip8ERGRADIzfnbNEFLbxfLMkvKAvW5MwF5JREREAOicFM8b904gNjpw590qfBEREQ90SIwL6Ovpkr6IiEgEUOGLiIhEABW+iIhIBFDhi4iIRAAVvoiISARQ4YuIiEQAFb6IiEgEUOGLiIhEABW+iIhIBFDhi4iIRAAVvoiISARQ4YuIiEQAFb6IiEgEUOGLiIhEABW+iIhIBDDnnNcZ/MbMKoASr3O0UhpQ6XWINqJjCU7hcizhchygYwlWoXgsPZ1z6Sf7QlgXfigys2LnXL7XOdqCjiU4hcuxhMtxgI4lWIXTsYAu6YuIiEQEFb6IiEgEUOEHn8e8DtCGdCzBKVyOJVyOA3QswSqcjkXv4YuIiEQCneGLiIhEABW+iIhIBFDhB4iZxZrZE2Y2z8wWm9mVZpZjZvNbHvujmUW1jH2gZcy7ZjbqQ89zs5kt9OYozv04zCzDzF40s7lmtsDM+obwsQwzs6KW8Y8fHxvsx9IyPsfM1pzw5zQze71l7NNmlhiix5FtZm+a2dtm9o6Z9ffiOFqynNOxnPD4BDMrC2z6j2Q41/8u7c3sny1jF33451ogtdH/Y++0/Ax7wau/K2fFOaePAHwAtwG/avm8M1AK/BuY2PLYo8DVwHDgLcCAbGDJCc8xDJgNFIXqcQB/B65v+fxC4PIQPpbngctaPp8OXBHsx9Ly+SeBYmD3Cd//G+AzLZ9/C7g3RI/jH8DUls+nAM+F6n+Tlsd7AC9++PFQOxbgf4FvtHx+HvDJED6WR4A7Wz5/EPiKl/9tWvOhM/zAmQF8/4Q/NwAjgHda/vwqMBkoBF53zUqBGDNLN7POwM+AewKY+WTO6TiAcUB3M3sTuAV4O1DBT+Jcj2U50MnMDEgG6gOW/KN8PRaAA8AFH/r+QuC1k4wNtHM9jq8DL7d8HgPU+CemT87pWMwsgebyudO/MX1yrv9dpgB1Zjar5Xlm+S/qGZ3rsawAOrZ8noK3f+9bRYUfIM65w865Q2aWDP/feB46kQAABAVJREFU3v28WFXGcRx/f5x+QCVDIlhCJf2gUqJVEOTUBILZP5C4iEhKAmFoZYugolVkPwaigiYRg5iFQWCb2hikEGGIC61pNhEqLkxNcVPUp8VzdK7D3Game+85XM7ntTr33HMP3y/nPnzPec45z8N+4FXKWxJXXpO4BIxS/kB/dPz0ErAK+BR4ufrcmB7zGAXWAedtb6KcWe+qK/b5+pDLLOXK+CdgDQ2evCwjF2x/ZfvyvF105nh127r1mofts7b/qrrydwNv1Bj+NfpwTD4Adts+VVvQXfQhl9XArbY3Awcox6YRfcjlJLBT0nFgC+UEYiik4NdI0h3AQeAz258D/3R8vRK4AFysljvXjwL3AR8B08B6Se/XEvQCesjjAvA7pfsMSsNvdNjKHnOZBMZsPwDsA96pJegulphLN505LrbtQPWYB5KeBL6kdBvPDCzQJfi/uUhaC4wBr0n6ltKTND3gcP9Tj8dlGNt9N29Tbn9tACYobX8opODXRNIa4Btgl+091eqjksar5S3Ad8BhYLOkFZLuBFbY/sH2BtvjwFbghO1GuvZ7zOMscAh4utr2ceB4bcHP04dczlEKJcBp5rr5areMXLo5zNxxWWzbgek1j6rYTwJP2T4yyFgX00sutk/bvt/2eNXuz9neOuiYu+nD/2sY230355nrDWu03S9XBt6piaRJ4Bng547VE5Qu4Rso3cIv2P5b0uuUP90KysNThzr2sw6Ytv1oPZFfq9c8JN0FTAE3UxrNNtvna0zhqj7kshF4i3IP8M9q21/ry2DOcnLp+M0Z27dVy2soD7ytpMwOtm2BrsyB60Mex4AbgTPV1zO2d9QR+3y95jJvXwuur0sfjssqSru/nXLP+9khbivrKbdbRigP8k7YPlpT+D1JwY+IiGiBdOlHRES0QAp+REREC6TgR0REtEAKfkRERAuk4EdERLRACn5ELJmk/ZJe6fh8i6QZSQ83GVdELC6v5UXEkklaDfwIbLF9QtLHwKztRkcZjIjF5Qo/IpasGmFwJzAl6QngHuBdSQ9JOqgyLe0XkkYljUiakvS1pCOS3gSQtFfSAZWphodmlLKIYZcr/IhYNkl7KNMbb7R9StL3wPPVVf924G7gE2CT7alq5reTtldL2gscs/1eYwlEtNB1TQcQEUNpH3BTx0xuDwIflpmCuR74hTLXwCPV+PYXKUPeXtHopDYRbZSCHxH9MEMZH/03SY9Rxkx/Drhge4eke4EXVZ0RcO3sZBFRgxT8iOiHl4B9kkaqz9spk5BMSxoDLgOzwNqG4otovdzDj4iIaIE8pR8REdECKfgREREtkIIfERHRAin4ERERLZCCHxER0QIp+BERES2Qgh8REdEC/wKzdnwJwMuTmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,7))\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Number of tweets\")\n",
    "sns.set()\n",
    "sns.lineplot(x = year_freq.index,y = year_freq.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "fig,ax = plt.subplots()\n",
    "d = pd.DataFrame(data={\"year\":year_freq.index,\"frequency\":year_freq.values})\n",
    "ax.barh(width = 'frequency', y= 'year',data = d)\n",
    "ax.set_yticks(d['year'])\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(10)\n",
    "ax.set_xlabel(\"Number of headlines\")\n",
    "ax.set_ylabel(\"Year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Preprocessing using NLTK and GENSIM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>publish_date</th>\n",
       "      <th>headline_text</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20030219</td>\n",
       "      <td>aba decides against community broadcasting lic...</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20030219</td>\n",
       "      <td>act fire witnesses must be aware of defamation</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20030219</td>\n",
       "      <td>a g calls for infrastructure protection summit</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20030219</td>\n",
       "      <td>air nz staff in aust strike for pay rise</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20030219</td>\n",
       "      <td>air nz strike to affect australian travellers</td>\n",
       "      <td>2003-02-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publish_date                                      headline_text       date\n",
       "0      20030219  aba decides against community broadcasting lic... 2003-02-19\n",
       "1      20030219     act fire witnesses must be aware of defamation 2003-02-19\n",
       "2      20030219     a g calls for infrastructure protection summit 2003-02-19\n",
       "3      20030219           air nz staff in aust strike for pay rise 2003-02-19\n",
       "4      20030219      air nz strike to affect australian travellers 2003-02-19"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['publish_date'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    aba decides against community broadcasting lic...\n",
       "1       act fire witnesses must be aware of defamation\n",
       "2       a g calls for infrastructure protection summit\n",
       "3             air nz staff in aust strike for pay rise\n",
       "4        air nz strike to affect australian travellers\n",
       "Name: headline_text, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headline_text.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    aba decides against community broadcasting lic...\n",
       "1       act fire witnesses must be aware of defamation\n",
       "2       a g calls for infrastructure protection summit\n",
       "3             air nz staff in aust strike for pay rise\n",
       "4        air nz strike to affect australian travellers\n",
       "Name: headline_text, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headline_text.apply(lambda x:x.lower()).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_preprocess(x,stemmer,word2cnt={}):\n",
    "        \n",
    "    x = simple_preprocess(x)\n",
    "    \n",
    "    x = [word for word in x if word not in STOPWORDS]\n",
    "    \n",
    "    x = [word for word in x if len(word)>3]\n",
    "\n",
    "    x = [stemmer.stem(WordNetLemmatizer().lemmatize(word,pos='v')) for word in x]\n",
    "    \n",
    "    for word in x:\n",
    "        word2cnt[word] = word2cnt.get(word,0) + 1\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before preprocessing\n",
      " aba decides against community broadcasting licence\n",
      "after preprocessing\n",
      " ['decid', 'commun', 'broadcast', 'licenc']\n"
     ]
    }
   ],
   "source": [
    "sample = data.iloc[0,0]\n",
    "print(\"before preprocessing\\n\",sample)\n",
    "print(\"after preprocessing\\n\",text_preprocess(sample,stemmer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2cnt = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['token'] = data.headline_text.apply(lambda x: text_preprocess(x,stemmer,word2cnt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View data preprocessed and tokenized for first few rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [decid, commun, broadcast, licenc]\n",
       "1                        [wit, awar, defam]\n",
       "2    [call, infrastructur, protect, summit]\n",
       "3               [staff, aust, strike, rise]\n",
       "4      [strike, affect, australian, travel]\n",
       "Name: token, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.token.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Dictionary for corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = corpora.Dictionary(data.token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 broadcast\n",
      "1 commun\n",
      "2 decid\n",
      "3 licenc\n",
      "4 awar\n",
      "5 defam\n",
      "6 wit\n",
      "7 call\n",
      "8 infrastructur\n",
      "9 protect\n"
     ]
    }
   ],
   "source": [
    "for word,index in list(dictionary.iteritems())[:10]:\n",
    "    print(word,index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Most Frequent Words in corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2c = list(word2cnt.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67259"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(w2c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "w2c.sort(key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = [word for word,_ in w2c[:15]]\n",
    "counts = [count for _,count in w2c[:15]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "fig,ax = plt.subplots()\n",
    "d = pd.DataFrame(data={\"words\":words[::-1],\"counts\":counts[::-1]})\n",
    "ax.barh(width = 'counts', y= 'words',data = d)\n",
    "ax.set_yticks(d['words'])\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(10)\n",
    "ax.set_ylabel(\"Words in corpus\")\n",
    "ax.set_xlabel(\"Frequencies\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA Model Training "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Filter Tokens that are rare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary.filter_extremes(no_below=100,no_above=0.1,keep_n=10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Convert text to bag of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.token.apply(lambda x:dictionary.doc2bow(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Split training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test = sklearn.model_selection.train_test_split(df,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(948814,)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1156984    [(71, 1), (216, 1), (291, 1), (870, 1), (883, ...\n",
       "168351           [(435, 1), (2411, 1), (3404, 1), (3428, 1)]\n",
       "663158              [(40, 1), (216, 1), (989, 1), (3597, 1)]\n",
       "223940                      [(116, 1), (2776, 1), (3774, 1)]\n",
       "856329                        [(173, 1), (174, 1), (703, 1)]\n",
       "Name: token, dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken for 20 epochs is 11.042136351267496\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "epochs = 20\n",
    "lda_model = gensim.models.LdaMulticore(train,num_topics=6,id2word=dictionary,workers=4,passes=epochs)\n",
    "print(\"time taken for {} epochs is {}\".format(epochs, (time.time()-start_time)/60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Words in each topic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "words in topic 1 :\n",
      " ['open', 'hous', 'elect', 'world', 'final', 'lead', 'labor', 'guilti', 'offic', 'aussi']\n",
      "\n",
      "words in topic 2 :\n",
      " ['australia', 'coast', 'test', 'south', 'boost', 'talk', 'die', 'north', 'worker', 'rural']\n",
      "\n",
      "words in topic 3 :\n",
      " ['govt', 'market', 'say', 'fund', 'govern', 'rise', 'chang', 'group', 'price', 'busi']\n",
      "\n",
      "words in topic 4 :\n",
      " ['polic', 'death', 'crash', 'kill', 'attack', 'health', 'hospit', 'investig', 'protest', 'sydney']\n",
      "\n",
      "words in topic 5 :\n",
      " ['plan', 'council', 'interview', 'water', 'flood', 'urg', 'centr', 'resid', 'countri', 'mayor']\n",
      "\n",
      "words in topic 6 :\n",
      " ['court', 'face', 'charg', 'murder', 'miss', 'accus', 'seek', 'win', 'jail', 'trial']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(6):\n",
    "    topic = lda_model.show_topic(i)\n",
    "    topic = [x[0] for x in topic]\n",
    "    print(\"words in topic {} :\\n {}\\n\".format(i+1,topic))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the top words in each topic, one could say that the documents have been classified into following topics:   \n",
    "1. Politics\n",
    "2. Unknown\n",
    "3. Business\n",
    "4. National\n",
    "5. Unknown\n",
    "6. Crime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test set classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_text = data[data.index.isin(list(test.index))]['headline_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2        a g calls for infrastructure protection summit\n",
       "5                     ambitious olsson wins triple jump\n",
       "7     aussie qualifier stosur wastes four memphis match\n",
       "12                           bathhouse plans move ahead\n",
       "13        big hopes for launceston cycling championship\n",
       "Name: headline_text, dtype: object"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_text.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2                 [(7, 1), (8, 1), (9, 1), (10, 1)]\n",
       "5                       [(18, 1), (19, 1), (20, 1)]\n",
       "7     [(25, 1), (26, 1), (27, 1), (28, 1), (29, 1)]\n",
       "12                               [(41, 1), (42, 1)]\n",
       "13             [(43, 1), (44, 1), (45, 1), (46, 1)]\n",
       "Name: token, dtype: object"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx2topic = {\n",
    "    0:\"Politics\",\n",
    "    1:\"Unknown\",\n",
    "    2:\"Business\",\n",
    "    3:\"National\",\n",
    "    4:\"Unknown\",\n",
    "    5:\"Crime\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_topic(x):\n",
    "    \n",
    "    topic_distribution = lda_model[x]\n",
    "    \n",
    "    topic_distribution.sort(key = lambda x:x[1])\n",
    "    \n",
    "    topic = topic_distribution[0][0]\n",
    "    \n",
    "    return idx2topic[topic]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_topic_names = test.apply(lambda x:get_topic(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2                 [(7, 1), (8, 1), (9, 1), (10, 1)]\n",
       "5                       [(18, 1), (19, 1), (20, 1)]\n",
       "7     [(25, 1), (26, 1), (27, 1), (28, 1), (29, 1)]\n",
       "12                               [(41, 1), (42, 1)]\n",
       "13             [(43, 1), (44, 1), (45, 1), (46, 1)]\n",
       "Name: token, dtype: object"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results = pd.DataFrame(data={\"text\":test_text.to_list(),\"topic\":test_topic_names.to_list()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text: a g calls for infrastructure protection summit \n",
      " Topic:Politics\n",
      "\n",
      "\n",
      "text: ambitious olsson wins triple jump \n",
      " Topic:Politics\n",
      "\n",
      "\n",
      "text: aussie qualifier stosur wastes four memphis match \n",
      " Topic:Business\n",
      "\n",
      "\n",
      "text: bathhouse plans move ahead \n",
      " Topic:National\n",
      "\n",
      "\n",
      "text: big hopes for launceston cycling championship \n",
      " Topic:National\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(\"text: {} \\n Topic:{}\\n\\n\".format(test_results.iloc[i,0],test_results.iloc[i,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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