TopicModelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "\n",
    "from collections import Counter\n",
    "import ast\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import seaborn as sb\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "news=pd.read_csv(\"news.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "    </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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20030219</td>\n",
       "      <td>ambitious olsson wins triple jump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20030219</td>\n",
       "      <td>antic delighted with record breaking barca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20030219</td>\n",
       "      <td>aussie qualifier stosur wastes four memphis match</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20030219</td>\n",
       "      <td>aust addresses un security council over iraq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20030219</td>\n",
       "      <td>australia is locked into war timetable opp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20030219</td>\n",
       "      <td>australia to contribute 10 million in aid to iraq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20030219</td>\n",
       "      <td>barca take record as robson celebrates birthda...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20030219</td>\n",
       "      <td>bathhouse plans move ahead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20030219</td>\n",
       "      <td>big hopes for launceston cycling championship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20030219</td>\n",
       "      <td>big plan to boost paroo water supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>20030219</td>\n",
       "      <td>blizzard buries united states in bills</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20030219</td>\n",
       "      <td>brigadier dismisses reports troops harassed in</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20030219</td>\n",
       "      <td>british combat troops arriving daily in kuwait</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20030219</td>\n",
       "      <td>bryant leads lakers to double overtime win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20030219</td>\n",
       "      <td>bushfire victims urged to see centrelink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20030219</td>\n",
       "      <td>businesses should prepare for terrorist attacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20030219</td>\n",
       "      <td>calleri avenges final defeat to eliminate massu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20030219</td>\n",
       "      <td>call for ethanol blend fuel to go ahead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>20030219</td>\n",
       "      <td>carews freak goal leaves roma in ruins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>20030219</td>\n",
       "      <td>cemeteries miss out on funds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20030219</td>\n",
       "      <td>code of conduct toughens organ donation regula...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20030219</td>\n",
       "      <td>commonwealth bank cuts fixed home loan rates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>20030219</td>\n",
       "      <td>community urged to help homeless youth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>20030219</td>\n",
       "      <td>council chief executive fails to secure position</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20030219</td>\n",
       "      <td>councillor to contest wollongong as independent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103635</th>\n",
       "      <td>20171231</td>\n",
       "      <td>nepal bans solo climbers from mount everest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103636</th>\n",
       "      <td>20171231</td>\n",
       "      <td>new years eve 2018 celebrated around australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103637</th>\n",
       "      <td>20171231</td>\n",
       "      <td>new years eve australia prepares to bring in 2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103638</th>\n",
       "      <td>20171231</td>\n",
       "      <td>new years eve celebrations around the world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103639</th>\n",
       "      <td>20171231</td>\n",
       "      <td>new years texting data load to surge as clock ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103640</th>\n",
       "      <td>20171231</td>\n",
       "      <td>north korea leader kim jong un watches concert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103641</th>\n",
       "      <td>20171231</td>\n",
       "      <td>now its real tourists converge on sydney harbour</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103642</th>\n",
       "      <td>20171231</td>\n",
       "      <td>nye guide for sydney best venues public transp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103643</th>\n",
       "      <td>20171231</td>\n",
       "      <td>police confirm deaths of six people in seaplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103644</th>\n",
       "      <td>20171231</td>\n",
       "      <td>police officer brett forte; killed in a shooti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103645</th>\n",
       "      <td>20171231</td>\n",
       "      <td>p plate driver caught 100 kph over speed limit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103646</th>\n",
       "      <td>20171231</td>\n",
       "      <td>protesters throw rocks at police in tehran</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103647</th>\n",
       "      <td>20171231</td>\n",
       "      <td>remembering australian lives lost in 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103648</th>\n",
       "      <td>20171231</td>\n",
       "      <td>remount horsemanship helping veterans through ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103649</th>\n",
       "      <td>20171231</td>\n",
       "      <td>roger federer rivals battling injury ahead aus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103650</th>\n",
       "      <td>20171231</td>\n",
       "      <td>russian tankers fuelled north korea via transf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103651</th>\n",
       "      <td>20171231</td>\n",
       "      <td>sa transport department defends major intersec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103652</th>\n",
       "      <td>20171231</td>\n",
       "      <td>sea plane has crashed into the hawkesbury rive...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103653</th>\n",
       "      <td>20171231</td>\n",
       "      <td>search for survivors in hawkesbury sea plane c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103654</th>\n",
       "      <td>20171231</td>\n",
       "      <td>second sexual assault reported at falls festiv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103655</th>\n",
       "      <td>20171231</td>\n",
       "      <td>severe storms forecast for nye in south east q...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103656</th>\n",
       "      <td>20171231</td>\n",
       "      <td>snake catcher pleads for people not to kill re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103657</th>\n",
       "      <td>20171231</td>\n",
       "      <td>south australia prepares for party to welcome ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103658</th>\n",
       "      <td>20171231</td>\n",
       "      <td>strikers cool off the heat with big win in ade...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103659</th>\n",
       "      <td>20171231</td>\n",
       "      <td>stunning images from the sydney to hobart yacht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103660</th>\n",
       "      <td>20171231</td>\n",
       "      <td>the ashes smiths warners near miss liven up bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103661</th>\n",
       "      <td>20171231</td>\n",
       "      <td>timelapse: brisbanes new year fireworks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103662</th>\n",
       "      <td>20171231</td>\n",
       "      <td>what 2017 meant to the kids of australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103663</th>\n",
       "      <td>20171231</td>\n",
       "      <td>what the papodopoulos meeting may mean for ausus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103664</th>\n",
       "      <td>20171231</td>\n",
       "      <td>who is george papadopoulos the former trump ca...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1103665 rows × 2 columns</p>\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\n",
       "5            20030219                  ambitious olsson wins triple jump\n",
       "6            20030219         antic delighted with record breaking barca\n",
       "7            20030219  aussie qualifier stosur wastes four memphis match\n",
       "8            20030219       aust addresses un security council over iraq\n",
       "9            20030219         australia is locked into war timetable opp\n",
       "10           20030219  australia to contribute 10 million in aid to iraq\n",
       "11           20030219  barca take record as robson celebrates birthda...\n",
       "12           20030219                         bathhouse plans move ahead\n",
       "13           20030219      big hopes for launceston cycling championship\n",
       "14           20030219             big plan to boost paroo water supplies\n",
       "15           20030219             blizzard buries united states in bills\n",
       "16           20030219     brigadier dismisses reports troops harassed in\n",
       "17           20030219     british combat troops arriving daily in kuwait\n",
       "18           20030219         bryant leads lakers to double overtime win\n",
       "19           20030219           bushfire victims urged to see centrelink\n",
       "20           20030219    businesses should prepare for terrorist attacks\n",
       "21           20030219    calleri avenges final defeat to eliminate massu\n",
       "22           20030219            call for ethanol blend fuel to go ahead\n",
       "23           20030219             carews freak goal leaves roma in ruins\n",
       "24           20030219                       cemeteries miss out on funds\n",
       "25           20030219  code of conduct toughens organ donation regula...\n",
       "26           20030219       commonwealth bank cuts fixed home loan rates\n",
       "27           20030219             community urged to help homeless youth\n",
       "28           20030219   council chief executive fails to secure position\n",
       "29           20030219    councillor to contest wollongong as independent\n",
       "...               ...                                                ...\n",
       "1103635      20171231        nepal bans solo climbers from mount everest\n",
       "1103636      20171231     new years eve 2018 celebrated around australia\n",
       "1103637      20171231  new years eve australia prepares to bring in 2018\n",
       "1103638      20171231        new years eve celebrations around the world\n",
       "1103639      20171231  new years texting data load to surge as clock ...\n",
       "1103640      20171231     north korea leader kim jong un watches concert\n",
       "1103641      20171231   now its real tourists converge on sydney harbour\n",
       "1103642      20171231  nye guide for sydney best venues public transp...\n",
       "1103643      20171231    police confirm deaths of six people in seaplane\n",
       "1103644      20171231  police officer brett forte; killed in a shooti...\n",
       "1103645      20171231     p plate driver caught 100 kph over speed limit\n",
       "1103646      20171231         protesters throw rocks at police in tehran\n",
       "1103647      20171231          remembering australian lives lost in 2017\n",
       "1103648      20171231  remount horsemanship helping veterans through ...\n",
       "1103649      20171231  roger federer rivals battling injury ahead aus...\n",
       "1103650      20171231  russian tankers fuelled north korea via transf...\n",
       "1103651      20171231  sa transport department defends major intersec...\n",
       "1103652      20171231  sea plane has crashed into the hawkesbury rive...\n",
       "1103653      20171231  search for survivors in hawkesbury sea plane c...\n",
       "1103654      20171231  second sexual assault reported at falls festiv...\n",
       "1103655      20171231  severe storms forecast for nye in south east q...\n",
       "1103656      20171231  snake catcher pleads for people not to kill re...\n",
       "1103657      20171231  south australia prepares for party to welcome ...\n",
       "1103658      20171231  strikers cool off the heat with big win in ade...\n",
       "1103659      20171231    stunning images from the sydney to hobart yacht\n",
       "1103660      20171231  the ashes smiths warners near miss liven up bo...\n",
       "1103661      20171231            timelapse: brisbanes new year fireworks\n",
       "1103662      20171231           what 2017 meant to the kids of australia\n",
       "1103663      20171231   what the papodopoulos meeting may mean for ausus\n",
       "1103664      20171231  who is george papadopoulos the former trump ca...\n",
       "\n",
       "[1103665 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "headline = news['headline_text']\n",
    "date = news['publish_date']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "headline.index=date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([20030219, 20030219, 20030219, 20030219, 20030219, 20030219,\n",
       "            20030219, 20030219, 20030219, 20030219,\n",
       "            ...\n",
       "            20171231, 20171231, 20171231, 20171231, 20171231, 20171231,\n",
       "            20171231, 20171231, 20171231, 20171231],\n",
       "           dtype='int64', name='publish_date', length=1103665)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headline.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          20030219\n",
       "1          20030219\n",
       "2          20030219\n",
       "3          20030219\n",
       "4          20030219\n",
       "5          20030219\n",
       "6          20030219\n",
       "7          20030219\n",
       "8          20030219\n",
       "9          20030219\n",
       "10         20030219\n",
       "11         20030219\n",
       "12         20030219\n",
       "13         20030219\n",
       "14         20030219\n",
       "15         20030219\n",
       "16         20030219\n",
       "17         20030219\n",
       "18         20030219\n",
       "19         20030219\n",
       "20         20030219\n",
       "21         20030219\n",
       "22         20030219\n",
       "23         20030219\n",
       "24         20030219\n",
       "25         20030219\n",
       "26         20030219\n",
       "27         20030219\n",
       "28         20030219\n",
       "29         20030219\n",
       "             ...   \n",
       "1103635    20171231\n",
       "1103636    20171231\n",
       "1103637    20171231\n",
       "1103638    20171231\n",
       "1103639    20171231\n",
       "1103640    20171231\n",
       "1103641    20171231\n",
       "1103642    20171231\n",
       "1103643    20171231\n",
       "1103644    20171231\n",
       "1103645    20171231\n",
       "1103646    20171231\n",
       "1103647    20171231\n",
       "1103648    20171231\n",
       "1103649    20171231\n",
       "1103650    20171231\n",
       "1103651    20171231\n",
       "1103652    20171231\n",
       "1103653    20171231\n",
       "1103654    20171231\n",
       "1103655    20171231\n",
       "1103656    20171231\n",
       "1103657    20171231\n",
       "1103658    20171231\n",
       "1103659    20171231\n",
       "1103660    20171231\n",
       "1103661    20171231\n",
       "1103662    20171231\n",
       "1103663    20171231\n",
       "1103664    20171231\n",
       "Name: publish_date, Length: 1103665, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All frequencies, including STOPWORDS:\n",
      "============================================================\n",
      "     Word  Frequency\n",
      "0      to     214199\n",
      "1      in     135979\n",
      "2     for     130237\n",
      "3      of      80758\n",
      "4      on      73035\n",
      "5    over      50302\n",
      "6     the      49787\n",
      "7  police      35985\n",
      "8      at      31720\n",
      "9    with      29676\n",
      "\n",
      "All frequencies, not including STOPWORDS:\n",
      "============================================================\n",
      "        Word  Frequency\n",
      "0     police      35985\n",
      "1        new      29087\n",
      "2        man      28109\n",
      "3       says      20001\n",
      "4         us      17536\n",
      "5       govt      16935\n",
      "6      court      16383\n",
      "7    council      16363\n",
      "8  interview      15025\n",
      "9       fire      13908\n",
      "============================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cd0fd60be0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_N = 10\n",
    "\n",
    "df = pd.read_csv('news.csv',\n",
    "                 usecols=['headline_text'])\n",
    "\n",
    "txt = df.headline_text.str.lower().str.replace(r'\\|', ' ').str.cat(sep=' ')\n",
    "words = nltk.tokenize.word_tokenize(txt)\n",
    "word_dist = nltk.FreqDist(words)\n",
    "\n",
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "words_except_stop_dist = nltk.FreqDist(w for w in words if w not in stopwords) \n",
    "\n",
    "print('All frequencies, including STOPWORDS:')\n",
    "print('=' * 60)\n",
    "rslt = pd.DataFrame(word_dist.most_common(top_N),\n",
    "                    columns=['Word', 'Frequency'])\n",
    "print(rslt)\n",
    "\n",
    "print('\\nAll frequencies, not including STOPWORDS:')\n",
    "print('=' * 60)\n",
    "rslt = pd.DataFrame(words_except_stop_dist.most_common(top_N),\n",
    "                    columns=['Word', 'Frequency'])\n",
    "print(rslt)\n",
    "print('=' * 60)\n",
    "\n",
    "rslt = pd.DataFrame(words_except_stop_dist.most_common(top_N),\n",
    "                    columns=['Word', 'Frequency']).set_index('Word')\n",
    "\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "rslt.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting date from Int64Index to DateTimeIndex\n",
    "date2 = pd.to_datetime(date, format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting date, which is DatetimeIndex to dataframe\n",
    "dataf=pd.DataFrame(data=date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataf\n",
    "headline.index=date2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Headline before vectorization:  activists still awaiting details of charges lawyer\n",
      "Headline after vectorization: \n",
      "   (0, 993)\t1\n",
      "  (0, 368)\t1\n",
      "  (0, 6353)\t1\n",
      "  (0, 3282)\t1\n",
      "  (0, 2187)\t1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AS0C76546\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Converting each string to a numerical vector using CountVectorizer\n",
    "#Create nxK document-term matrix, where n= headlines (document), K= distinct words\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import random\n",
    "small_count_vectorizer = CountVectorizer(stop_words='english', max_features=40000)\n",
    "small_text_sample = headline.sample(n=10000, random_state=0).as_matrix()\n",
    "\n",
    "rand=random.randint(0,10000)\n",
    "\n",
    "print('Headline before vectorization: ', small_text_sample[rand])\n",
    "\n",
    "small_document_term_matrix = small_count_vectorizer.fit_transform(small_text_sample) #removes stopwords from small_text_sample\n",
    "\n",
    "print('Headline after vectorization: \\n', small_document_term_matrix[rand])\n",
    "\n",
    "#small_document_term_matrix is training data\n",
    "\n",
    "# The fit_transform method applies to feature extraction objects such as CountVectorizer and TfidfTransformer.\n",
    "# The \"fit\" part applies to the feature extractor itself:\n",
    "# it determines what features it will base future transformations on. The \"transform\" part is what takes the data and spits some transformed data back at you. \n",
    "# For instance, let's say you have a CountVectorizer Object. \n",
    "# Well, you call fit_transform for this object on some collection of strings.\n",
    "# What it does is tokenize the strings and give you a vector for each string, \n",
    "# each dimension of which corresponds to the number of times a token is found in the corresponding string.\n",
    "# Most of the entries in all of the vectors will be zero, since only the entries which correspond to tokens \n",
    "# found in that specific string will have positive values, but the vector is as long as the total number of tokens for the whole corpus. \n",
    "\n",
    "\n",
    "# So, fit_transform has both determined which tokens it will count,\n",
    "# and how they correspond to entries in the count vector. It also gives you the count vectors for the training data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latent Dirichilet Allocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "n_topics=8\n",
    "lda_model = LatentDirichletAllocation(learning_method='online', \n",
    "                                          random_state=0, verbose=0)\n",
    "lda_topic_matrix = lda_model.fit_transform(small_document_term_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper functions\n",
    "def get_keys(topic_matrix):\n",
    "    '''returns an integer list of predicted topic categories for a given topic matrix'''\n",
    "    keys = []\n",
    "    for i in range(topic_matrix.shape[0]):\n",
    "        keys.append(topic_matrix[i].argmax())\n",
    "    return keys\n",
    "\n",
    "def keys_to_counts(keys):\n",
    "    '''returns a tuple of topic categories and their accompanying magnitudes for a given list of keys'''\n",
    "    count_pairs = Counter(keys).items()\n",
    "    categories = [pair[0] for pair in count_pairs]\n",
    "    counts = [pair[1] for pair in count_pairs]\n",
    "    return (categories, counts)\n",
    "\n",
    "def get_top_n_words(n, keys, document_term_matrix, count_vectorizer):\n",
    "    '''returns a list of n_topic strings, where each string contains the n most common \n",
    "        words in a predicted category, in order'''\n",
    "    top_word_indices = []\n",
    "    for topic in range(big_lda_model.n_components):\n",
    "        temp_vector_sum = 0\n",
    "        for i in range(len(keys)):\n",
    "            if keys[i] == topic:\n",
    "                temp_vector_sum += document_term_matrix[i]\n",
    "        temp_vector_sum = temp_vector_sum.toarray()\n",
    "        top_n_word_indices = np.flip(np.argsort(temp_vector_sum)[0][-n:],0)\n",
    "        top_word_indices.append(top_n_word_indices)   \n",
    "    top_words = []\n",
    "    for topic in top_word_indices:\n",
    "        topic_words = []\n",
    "        for index in topic:\n",
    "            temp_word_vector = np.zeros((1,document_term_matrix.shape[1]))\n",
    "            temp_word_vector[:,index] = 1\n",
    "            the_word = count_vectorizer.inverse_transform(temp_word_vector)[0][0]\n",
    "            topic_words.append(the_word.encode('ascii').decode('utf-8'))\n",
    "        top_words.append(\" \".join(topic_words))         \n",
    "    return top_words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#taking argmax of each entry in topic matrix\n",
    "lda_keys = get_keys(lda_topic_matrix)\n",
    "lda_categories, lda_counts = keys_to_counts(lda_keys)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:  new world cup australia national final abc melbourne arrested victory\n",
      "Topic 1:  govt market crash death help win dead police election new\n",
      "Topic 2:  health rural dies residents year mental search adelaide river laws\n",
      "Topic 3:  interview sa hospital country hour work funds appeal police league\n",
      "Topic 4:  court woman wins trial house centre face sydney farmers killed\n",
      "Topic 5:  man police water charged hit minister government pay faces land\n",
      "Topic 6:  council new plan qld nsw wa group says report school\n",
      "Topic 7:  coast gold day power new years case open indigenous support\n",
      "Topic 8:  says public car talks probe future union labor mp new\n",
      "Topic 9:  police murder charges child guilty jailed man new abuse assault\n"
     ]
    }
   ],
   "source": [
    "top_n_words_lda = get_top_n_words(10, lda_keys, small_document_term_matrix, small_count_vectorizer)\n",
    "\n",
    "for i in range(len(top_n_words_lda)):\n",
    "    print(\"Topic {}: \".format(i), top_n_words_lda[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'LDA Topic Category Counts')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_3_words = get_top_n_words(3, lda_keys, small_document_term_matrix, small_count_vectorizer)\n",
    "labels = ['Topic {}: \\n'.format(i) + top_3_words[i] for i in lda_categories]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "ax.bar(lda_categories, lda_counts)\n",
    "ax.set_xticks(lda_categories)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.set_title('LDA Topic Category Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AS0C76546\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration: 1 of max_iter: 10\n",
      "iteration: 2 of max_iter: 10\n",
      "iteration: 3 of max_iter: 10\n",
      "iteration: 4 of max_iter: 10\n",
      "iteration: 5 of max_iter: 10\n",
      "iteration: 6 of max_iter: 10\n",
      "iteration: 7 of max_iter: 10\n",
      "iteration: 8 of max_iter: 10\n",
      "iteration: 9 of max_iter: 10\n",
      "iteration: 10 of max_iter: 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
       "             evaluate_every=-1, learning_decay=0.7,\n",
       "             learning_method='online', learning_offset=10.0,\n",
       "             max_doc_update_iter=100, max_iter=10, mean_change_tol=0.001,\n",
       "             n_components=10, n_jobs=1, n_topics=None, perp_tol=0.1,\n",
       "             random_state=None, topic_word_prior=None,\n",
       "             total_samples=1000000.0, verbose=1)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "big_sample_size = 100000 \n",
    "\n",
    "big_count_vectorizer = CountVectorizer(stop_words='english', max_features=40000)\n",
    "big_text_sample = headline.sample(n=big_sample_size, random_state=0).as_matrix()\n",
    "big_document_term_matrix = big_count_vectorizer.fit_transform(big_text_sample)\n",
    "\n",
    "big_lda_model = LatentDirichletAllocation(learning_method='online', verbose=1)\n",
    "big_lda_model.fit(big_document_term_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:  police court new child abc centre minister charges school nt\n",
      "Topic 1:  coast gold day new wins election open china year trial\n",
      "Topic 2:  crash says pm drug power police attack probe laws future\n",
      "Topic 3:  man charged country hour dies face sa set hit police\n",
      "Topic 4:  council plan killed urged murder farmers car fears union police\n",
      "Topic 5:  says death group government guilty plans talks workers wa help\n",
      "Topic 6:  water rural win market missing melbourne labor news dead public\n",
      "Topic 7:  new health nsw qld report calls indigenous sex funding boost\n",
      "Topic 8:  interview govt world cup sydney hospital australia accused claims new\n",
      "Topic 9:  australian home south house residents west adelaide new police lead\n"
     ]
    }
   ],
   "source": [
    "lda_topic_matrix_big = lda_model.fit_transform(big_document_term_matrix)\n",
    "#argmax\n",
    "lda_keys1 = get_keys(lda_topic_matrix_big)\n",
    "lda_categories, lda_counts = keys_to_counts(lda_keys1)\n",
    "\n",
    "top_n_words_lda1 = get_top_n_words(10, lda_keys1, big_document_term_matrix, big_count_vectorizer)\n",
    "\n",
    "for i in range(len(top_n_words_lda1)):\n",
    "    print(\"Topic {}: \".format(i), top_n_words_lda1[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AS0C76546\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Topic 0  Topic 1  Topic 2  Topic 3  Topic 4  Topic 5  Topic 6  Topic 7  \\\n",
      "2003     7534     6770     5835     6305     5397     7422     6387     6470   \n",
      "2004     7626     7551     8325     6570     6232     6713     6790     6513   \n",
      "2005     7351     6691     6976     8938     7465     7763     8495     6067   \n",
      "2006     5654     8742     5975     6957     5625     6714     6129     8038   \n",
      "2007     7780     7304     7580     9778     9422     6168     7690     6983   \n",
      "2008     8539     9655     6922     7922     8760     7385     7983     7821   \n",
      "2009     8968     7582     7494     7346     7387     7581     8659     7879   \n",
      "2010     7018     9611     6674     7512     7381     7459     6892     8609   \n",
      "2011     9207     8332     8084     6834     7732     7457     7308     7330   \n",
      "2012     9745     8464     8497     8953     8973     8209     8446    10304   \n",
      "2013     9474     9111     8059    10621     9731     9984     8774     9098   \n",
      "2014    10711     8030     7712     8651     7578     8730     7636     8052   \n",
      "2015     7920     7315     7625     7649     8131     6369     7807    10172   \n",
      "2016     5400     6097     5654     4526     5644     5809     5436     5888   \n",
      "\n",
      "      Topic 8  Topic 9  \n",
      "2003     6081     5802  \n",
      "2004     7516     8838  \n",
      "2005     6891     6487  \n",
      "2006     6480     6598  \n",
      "2007     7568     6919  \n",
      "2008     7795     7233  \n",
      "2009     6930     6628  \n",
      "2010     7043     6749  \n",
      "2011     8002     7543  \n",
      "2012     9070     8448  \n",
      "2013     8618     8867  \n",
      "2014     8233     6997  \n",
      "2015     7116     7837  \n",
      "2016     5945     4216  \n"
     ]
    }
   ],
   "source": [
    "yearly_data = []\n",
    "for i in range(2003,2017):\n",
    "    yearly_data.append(headline['{}'.format(i)].as_matrix())\n",
    "\n",
    "yearly_topic_matrices = []\n",
    "for year in yearly_data:\n",
    "    document_term_matrix = big_count_vectorizer.transform(year)\n",
    "    topic_matrix = big_lda_model.transform(document_term_matrix)\n",
    "    yearly_topic_matrices.append(topic_matrix)\n",
    "\n",
    "yearly_keys = []\n",
    "for topic_matrix in yearly_topic_matrices:\n",
    "    yearly_keys.append(get_keys(topic_matrix))\n",
    "    \n",
    "yearly_counts = []\n",
    "for keys in yearly_keys:\n",
    "    categories, counts = keys_to_counts(keys)\n",
    "    yearly_counts.append(counts)\n",
    "\n",
    "yearly_topic_counts = pd.DataFrame(np.array(yearly_counts), index=range(2003,2017))\n",
    "yearly_topic_counts.columns = ['Topic {}'.format(i) for i in range(big_lda_model.n_components)]\n",
    "\n",
    "print(yearly_topic_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AS0C76546\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:  health\n",
      "Topic 1:  nsw\n",
      "Topic 2:  australian\n",
      "Topic 3:  police\n",
      "Topic 4:  interview\n",
      "Topic 5:  govt\n",
      "Topic 6:  australia\n",
      "Topic 7:  new\n",
      "Topic 8:  says\n",
      "Topic 9:  council\n"
     ]
    }
   ],
   "source": [
    "complete_keys = [key for year in yearly_keys for key in year]\n",
    "complete_topic_matrix = np.concatenate(yearly_topic_matrices)\n",
    "complete_document_term_matrix = big_count_vectorizer.transform(headline.as_matrix())\n",
    "\n",
    "top_n_words = get_top_n_words(1, complete_keys, complete_document_term_matrix, big_count_vectorizer)\n",
    "for i in range(len(top_n_words)):\n",
    "    print(\"Topic {}: \".format(i), top_n_words[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23ecd27e940>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "labels = ['Topic {}: \\n '.format(i) + ' '.join([topic.split() for topic in top_n_words][i][:3]) for i in range(big_lda_model.n_components)]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14,10))\n",
    "sb.heatmap(yearly_topic_counts, xticklabels=labels, cmap=\"YlGnBu\", ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3,\n",
       " 6,\n",
       " 3,\n",
       " 0,\n",
       " 4,\n",
       " 3,\n",
       " 3,\n",
       " 1,\n",
       " 7,\n",
       " 7,\n",
       " 2,\n",
       " 6,\n",
       " 2,\n",
       " 6,\n",
       " 4,\n",
       " 2,\n",
       " 1,\n",
       " 3,\n",
       " 5,\n",
       " 2,\n",
       " 5,\n",
       " 1,\n",
       " 4,\n",
       " 3,\n",
       " 6,\n",
       " 3,\n",
       " 6,\n",
       " 6,\n",
       " 4,\n",
       " 0,\n",
       " 7,\n",
       " 2,\n",
       " 4,\n",
       " 3,\n",
       " 7,\n",
       " 6,\n",
       " 6,\n",
       " 1,\n",
       " 2,\n",
       " 4,\n",
       " 6,\n",
       " 7,\n",
       " 7,\n",
       " 0,\n",
       " 7,\n",
       " 7,\n",
       " 0,\n",
       " 7,\n",
       " 3,\n",
       " 3,\n",
       " 2,\n",
       " 3,\n",
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       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['police new world australia cup day country charges hospital sa',\n",
       " 'market farmers murder set power family dead urged fight missing',\n",
       " 'health govt new coast gold council rural boost police labor',\n",
       " 'says man crash pay police court south claims guilty canberra',\n",
       " 'man court plan council sydney qld charged face wins police',\n",
       " 'win high centre accused abc public housing darwin review risk',\n",
       " 'interview says wa nsw school sex killed iraq child report',\n",
       " 'death water australian home new china car action adelaide opposition']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n_words_lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['aba decides against community broadcasting licence',\n",
       "        'act fire witnesses must be aware of defamation',\n",
       "        'a g calls for infrastructure protection summit', ...,\n",
       "        'with heat on tamworth water use climbs',\n",
       "        'wolfowitz replaced as guantanamo trial head',\n",
       "        'your new years eve photos'], dtype=object),\n",
       " array(['9 dead as bomb ends aceh new years concert',\n",
       "        'abandoned pets crowd rspca shelters', 'act water charges double',\n",
       "        ..., 'youth anti cannabis campaign to be extended',\n",
       "        'youth in custody after adelaide car chase',\n",
       "        'zimbabwe confident of successful test return'], dtype=object),\n",
       " array(['10 killed in kashmir new year party called off',\n",
       "        'aftershocks continue to rattle aceh',\n",
       "        'aid for some aceh survivors weeks away', ...,\n",
       "        'wa govt puts 5m towards research machine',\n",
       "        'wenger all but gives up hope of premier league',\n",
       "        'workcover premiums reduced'], dtype=object),\n",
       " array(['adelaide end losing streak',\n",
       "        'adelaide notch 4 2 win against roar',\n",
       "        'adelaide trounce roar to extend lead', ...,\n",
       "        'woman raises 14k in fraudulent cancer claim',\n",
       "        'woman stabbed in back with broken beer bottle',\n",
       "        'woods to become a dad'], dtype=object),\n",
       " array(['140 arrested in adelaide during new years',\n",
       "        '1976 govt papers show funding shortfall for',\n",
       "        '2006 deadliest year for reporters in over a decade', ...,\n",
       "        'woman arrested twice for drink driving in 3 days',\n",
       "        'worlds oldest orangutan dies',\n",
       "        'yuvraj under pressure to hold spot for sydney test'], dtype=object),\n",
       " array(['2007 deadliest year yet for us troops in iraq',\n",
       "        '372 french cars torched over calm nye',\n",
       "        '550 palestinians stranded in egypt to return to', ...,\n",
       "        'winning yachties handed the silverware',\n",
       "        'woman stabbed in neck and arms',\n",
       "        'zimbabwe activists kept in prison over alleged'], dtype=object),\n",
       " array(['2008 deadliest year for us forces in afghanistan',\n",
       "        '2008 dubbed wet year for hunter',\n",
       "        '2 israelis injured in danish mall shooting', ...,\n",
       "        'welcome rains creating flooding problems',\n",
       "        'woman accused of assaulting driver who hit',\n",
       "        'woman to face court over normanton stabbing'], dtype=object),\n",
       " array(['12m degas stolen from museum', '17 die in bangladesh bus crash',\n",
       "        '2010 to be drier after very wet 2009', ...,\n",
       "        'woman still critical after robbery bashing',\n",
       "        'youngsters win wheels',\n",
       "        'zimbabweans queue ahead of citizenship deadline'], dtype=object),\n",
       " array(['11 killed in nigerian market blast',\n",
       "        '15 drown in egypt bus accident', 'adelaide aims to reel in roar',\n",
       "        ..., 'turkish pm sorry over deadly strike on kurds',\n",
       "        'vic boy killed by car at caravan park',\n",
       "        'young girl dies after being hit by arrow'], dtype=object),\n",
       " array(['abc entertainment news', 'abc sport', 'abc weather', ...,\n",
       "        'wozniacki bows out in brisbane',\n",
       "        'xstrata coal mine job cuts alarm town',\n",
       "        'young girl killed crashing quad bike'], dtype=object),\n",
       " array(['abc business news and market analysis', 'abc entertainment',\n",
       "        'abc sport', ..., 'wonawinta edges closer to production target',\n",
       "        'woodruff a new tax on illness',\n",
       "        'your say the most important news stories of 2013'], dtype=object),\n",
       " array(['abbott wants conversation on indigenous referendum',\n",
       "        'an bangladesh issues warrant for fire disaster factory owner',\n",
       "        'anderson smashes odi century record', ...,\n",
       "        'woman charged with attempting to murder children faces court',\n",
       "        'wreck of light plane found at bottom of sea off tasmania',\n",
       "        'zoo keepers on the nsw central coast begin annual'], dtype=object),\n",
       " array(['1000s celebrate nye in adelaide',\n",
       "        '1984 qld cabinet papers released',\n",
       "        '2014 year in interactive storytelling', ...,\n",
       "        'win tv could go black if nine impasse continues',\n",
       "        'woolly sheep found stuck in culvert by farmer in tasmania',\n",
       "        'your favourite movies of 2015'], dtype=object),\n",
       " array(['1985 qld cabinet documents mabo decision',\n",
       "        '49ers juggle jarryd haynes talent mistakes',\n",
       "        'abjorensen the 2016 battlelines have been drawn', ...,\n",
       "        'video shows indonesian pilot unsteadily going through security',\n",
       "        'water police warn boaties to take more care',\n",
       "        'woman injured in braddon supermarket robbery'], dtype=object)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         0.025     ],\n",
       "        ...,\n",
       "        [0.02083333, 0.02083333, 0.18749991, ..., 0.02083333, 0.02083333,\n",
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       "         0.02083334],\n",
       "        [0.025     , 0.025     , 0.30016009, ..., 0.02501164, 0.025     ,\n",
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       " array([[0.015625  , 0.015625  , 0.15995753, ..., 0.01562875, 0.14062546,\n",
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       "        [0.22500025, 0.025     , 0.22500022, ..., 0.025     , 0.025     ,\n",
       "         0.02503099],\n",
       "        ...,\n",
       "        [0.02083333, 0.52083523, 0.02083333, ..., 0.02083333, 0.02083333,\n",
       "         0.18749956],\n",
       "        [0.02083333, 0.18750053, 0.18749906, ..., 0.02083333, 0.18749882,\n",
       "         0.35416825],\n",
       "        [0.02083333, 0.18750137, 0.35416976, ..., 0.02083333, 0.02083333,\n",
       "         0.02083333]]),\n",
       " array([[0.26562958, 0.14061173, 0.01563657, ..., 0.17378869, 0.015625  ,\n",
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       "        [0.1875009 , 0.18749871, 0.02083333, ..., 0.18749925, 0.02083333,\n",
       "         0.02083333],\n",
       "        ...,\n",
       "        [0.30356924, 0.01785714, 0.01785714, ..., 0.16071498, 0.16071569,\n",
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       "        [0.03125   , 0.03125   , 0.03125   , ..., 0.28125255, 0.03125   ,\n",
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       "        ...,\n",
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       "        [0.025     , 0.025     , 0.025     , ..., 0.2249911 , 0.42500555,\n",
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       "         0.18749964],\n",
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       "        [0.025     , 0.2250044 , 0.025     , ..., 0.025     , 0.22501154,\n",
       "         0.025     ],\n",
       "        [0.01785714, 0.16071786, 0.01785714, ..., 0.01785714, 0.30357395,\n",
       "         0.30355649]]),\n",
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       "         0.18750418],\n",
       "        [0.02083333, 0.02083333, 0.35416601, ..., 0.02083333, 0.02083333,\n",
       "         0.02083333],\n",
       "        [0.18742942, 0.02083334, 0.35420561, ..., 0.02083334, 0.02083334,\n",
       "         0.02083334],\n",
       "        ...,\n",
       "        [0.02500002, 0.02500002, 0.22487848, ..., 0.02500002, 0.22505634,\n",
       "         0.02500002],\n",
       "        [0.025     , 0.025     , 0.025     , ..., 0.22499551, 0.42500191,\n",
       "         0.22500257],\n",
       "        [0.1874934 , 0.02083333, 0.35416993, ..., 0.02083333, 0.02083333,\n",
       "         0.02083333]]),\n",
       " array([[0.35416355, 0.02083334, 0.02083334, ..., 0.18749269, 0.18750572,\n",
       "         0.18750469],\n",
       "        [0.354163  , 0.02083334, 0.02083334, ..., 0.18749377, 0.02083334,\n",
       "         0.18750612],\n",
       "        [0.02083334, 0.1874963 , 0.18749025, ..., 0.18749939, 0.02083334,\n",
       "         0.35418071],\n",
       "        ...,\n",
       "        [0.35417055, 0.02083333, 0.18748945, ..., 0.02083333, 0.02083333,\n",
       "         0.02083333],\n",
       "        [0.02083333, 0.02083333, 0.18749605, ..., 0.02083333, 0.18750084,\n",
       "         0.35416894],\n",
       "        [0.18744641, 0.02083334, 0.02083334, ..., 0.02083334, 0.3541972 ,\n",
       "         0.18751123]]),\n",
       " array([[0.28123951, 0.03125   , 0.28125579, ..., 0.03125   , 0.28125469,\n",
       "         0.03125   ],\n",
       "        [0.18749881, 0.02083333, 0.02083333, ..., 0.02083333, 0.35416825,\n",
       "         0.35416627],\n",
       "        [0.025     , 0.025     , 0.025     , ..., 0.42502436, 0.22496663,\n",
       "         0.025     ],\n",
       "        ...,\n",
       "        [0.025     , 0.025     , 0.22499909, ..., 0.025     , 0.4250005 ,\n",
       "         0.22500041],\n",
       "        [0.03125001, 0.03125001, 0.28126541, ..., 0.03125001, 0.28125267,\n",
       "         0.03125   ],\n",
       "        [0.02083334, 0.1875185 , 0.18747512, ..., 0.02083334, 0.02083334,\n",
       "         0.18751778]]),\n",
       " array([[0.02083333, 0.02083333, 0.02083333, ..., 0.02083333, 0.18749423,\n",
       "         0.3541698 ],\n",
       "        [0.02083334, 0.02083334, 0.18750511, ..., 0.02083334, 0.35418015,\n",
       "         0.18747815],\n",
       "        [0.22497059, 0.22500967, 0.025     , ..., 0.025     , 0.025     ,\n",
       "         0.42501973],\n",
       "        ...,\n",
       "        [0.16068534, 0.16072089, 0.1607217 , ..., 0.1607171 , 0.01785715,\n",
       "         0.01785715],\n",
       "        [0.1607138 , 0.01785714, 0.01785714, ..., 0.01785714, 0.01785714,\n",
       "         0.30357252],\n",
       "        [0.18749994, 0.02083333, 0.02083333, ..., 0.02083333, 0.52083296,\n",
       "         0.02083333]]),\n",
       " array([[0.03125   , 0.03125   , 0.53124989, ..., 0.03125   , 0.03125   ,\n",
       "         0.03125   ],\n",
       "        [0.04166667, 0.04166667, 0.70833333, ..., 0.04166667, 0.04166667,\n",
       "         0.04166667],\n",
       "        [0.04166667, 0.04166667, 0.70833333, ..., 0.04166667, 0.04166667,\n",
       "         0.04166667],\n",
       "        ...,\n",
       "        [0.28123685, 0.53126313, 0.03125   , ..., 0.03125   , 0.03125   ,\n",
       "         0.03125   ],\n",
       "        [0.44643786, 0.01785714, 0.01785714, ..., 0.16071619, 0.01785714,\n",
       "         0.16070786],\n",
       "        [0.16071518, 0.01785714, 0.01785714, ..., 0.01785714, 0.30357257,\n",
       "         0.303568  ]]),\n",
       " array([[0.02083333, 0.18750003, 0.18750005, ..., 0.02083333, 0.02083333,\n",
       "         0.18750006],\n",
       "        [0.04166667, 0.04166667, 0.70833333, ..., 0.04166667, 0.04166667,\n",
       "         0.04166667],\n",
       "        [0.04166667, 0.04166667, 0.70833333, ..., 0.04166667, 0.04166667,\n",
       "         0.04166667],\n",
       "        ...,\n",
       "        [0.025     , 0.22500093, 0.22500151, ..., 0.025     , 0.025     ,\n",
       "         0.22499599],\n",
       "        [0.03125   , 0.03125   , 0.03128307, ..., 0.39409806, 0.03125   ,\n",
       "         0.41826992],\n",
       "        [0.02083333, 0.35416548, 0.02083333, ..., 0.02083333, 0.02083333,\n",
       "         0.02083333]]),\n",
       " array([[0.18747974, 0.18750486, 0.02083334, ..., 0.18750462, 0.35417744,\n",
       "         0.02083333],\n",
       "        [0.16071532, 0.01785714, 0.16070921, ..., 0.16071431, 0.01785714,\n",
       "         0.30357503],\n",
       "        [0.18750249, 0.02083333, 0.18749392, ..., 0.18749979, 0.02083333,\n",
       "         0.02083333],\n",
       "        ...,\n",
       "        [0.015625  , 0.015625  , 0.015625  , ..., 0.14062569, 0.39062027,\n",
       "         0.14062599],\n",
       "        [0.02083333, 0.02083333, 0.02083333, ..., 0.52080498, 0.02083333,\n",
       "         0.02083333],\n",
       "        [0.015625  , 0.015625  , 0.015625  , ..., 0.26563649, 0.015625  ,\n",
       "         0.14062919]]),\n",
       " array([[0.03125   , 0.28125449, 0.28124309, ..., 0.03125   , 0.03125   ,\n",
       "         0.03125   ],\n",
       "        [0.02083334, 0.02083334, 0.02083334, ..., 0.1875025 , 0.18745129,\n",
       "         0.35419931],\n",
       "        [0.2250342 , 0.22499883, 0.02500001, ..., 0.22503493, 0.02500001,\n",
       "         0.224932  ],\n",
       "        ...,\n",
       "        [0.02083334, 0.52085284, 0.18750476, ..., 0.02083334, 0.18747572,\n",
       "         0.02083333],\n",
       "        [0.18750481, 0.02083334, 0.02083334, ..., 0.20805957, 0.02083334,\n",
       "         0.02083334],\n",
       "        [0.03125   , 0.03125   , 0.03125   , ..., 0.28125609, 0.2812443 ,\n",
       "         0.03125   ]]),\n",
       " array([[0.01785715, 0.01785715, 0.01785715, ..., 0.16072962, 0.01785715,\n",
       "         0.16073343],\n",
       "        [0.44642144, 0.16073049, 0.01785715, ..., 0.01785715, 0.01785715,\n",
       "         0.16072577],\n",
       "        [0.04166667, 0.04166667, 0.37500511, ..., 0.04166667, 0.37499488,\n",
       "         0.04166667],\n",
       "        ...,\n",
       "        [0.01785714, 0.01785714, 0.16071459, ..., 0.3035724 , 0.16071289,\n",
       "         0.01785714],\n",
       "        [0.02083333, 0.02083333, 0.35417128, ..., 0.02083333, 0.02083333,\n",
       "         0.18749017],\n",
       "        [0.02083334, 0.02083334, 0.3541504 , ..., 0.02083334, 0.02083334,\n",
       "         0.35418025]])]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_topic_matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54615"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(topic_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ...]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.183336  , 0.01666667, 0.18332218, ..., 0.01666667, 0.01666667,\n",
       "        0.01666667],\n",
       "       [0.02      , 0.02      , 0.02      , ..., 0.21999873, 0.02      ,\n",
       "        0.42000101],\n",
       "       [0.21999987, 0.02      , 0.21999995, ..., 0.02      , 0.02      ,\n",
       "        0.42000019],\n",
       "       ...,\n",
       "       [0.01428571, 0.30000021, 0.01428571, ..., 0.01428571, 0.01428571,\n",
       "        0.15714285],\n",
       "       [0.18333317, 0.01666667, 0.01666667, ..., 0.01666667, 0.01666667,\n",
       "        0.01666667],\n",
       "       [0.18332588, 0.01666667, 0.35000426, ..., 0.01666667, 0.18333506,\n",
       "        0.01666667]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_topic_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_lda_model.n_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 1329)\t1\n",
      "  (0, 5450)\t1\n",
      "  (0, 7777)\t1\n",
      "  (0, 9382)\t1\n",
      "  (0, 19254)\t1\n",
      "  (1, 1582)\t1\n",
      "  (1, 3340)\t1\n",
      "  (1, 9456)\t1\n",
      "  (1, 35937)\t1\n",
      "  (2, 6053)\t1\n",
      "  (2, 16819)\t1\n",
      "  (2, 25861)\t1\n",
      "  (2, 31727)\t1\n",
      "  (3, 1928)\t1\n",
      "  (3, 3213)\t1\n",
      "  (3, 23091)\t1\n",
      "  (3, 24298)\t1\n",
      "  (3, 27845)\t1\n",
      "  (3, 30972)\t1\n",
      "  (3, 31455)\t1\n",
      "  (4, 1775)\t1\n",
      "  (4, 1928)\t1\n",
      "  (4, 3232)\t1\n",
      "  (4, 23091)\t1\n",
      "  (4, 31455)\t1\n",
      "  :\t:\n",
      "  (1103659, 36271)\t1\n",
      "  (1103660, 2926)\t1\n",
      "  (1103660, 5158)\t1\n",
      "  (1103660, 9281)\t1\n",
      "  (1103660, 19481)\t1\n",
      "  (1103660, 21460)\t1\n",
      "  (1103660, 22463)\t1\n",
      "  (1103660, 30293)\t1\n",
      "  (1103660, 32643)\t1\n",
      "  (1103660, 35230)\t1\n",
      "  (1103661, 5420)\t1\n",
      "  (1103661, 12792)\t1\n",
      "  (1103661, 22607)\t1\n",
      "  (1103661, 32950)\t1\n",
      "  (1103661, 36344)\t1\n",
      "  (1103662, 503)\t1\n",
      "  (1103662, 3230)\t1\n",
      "  (1103662, 18182)\t1\n",
      "  (1103662, 20820)\t1\n",
      "  (1103663, 20814)\t1\n",
      "  (1103663, 20896)\t1\n",
      "  (1103664, 1908)\t1\n",
      "  (1103664, 6093)\t1\n",
      "  (1103664, 13942)\t1\n",
      "  (1103664, 33660)\t1\n"
     ]
    }
   ],
   "source": [
    "print(complete_document_term_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 5, 0, 7, 1, 8, 4, 2, 6, 3]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<54615x36581 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 327275 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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