LDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LDA\n",
    "Gonna try something a little different here from some of the things that I've attempted before. Gonna try some LDA with the `gensim` package and see what sorts of interesting things we can find out. Maybe this could be a back-door way into predicting topics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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_category</th>\n",
       "      <th>headline_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>Smurfit's share price in retreat despite recor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>Jamont plans £5m investment to update plant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>Management is blamed for most company failures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>Forte expected to announce a special dividend ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>Accountancy firm adopts name change</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publish_date headline_category  \\\n",
       "0      19960102          business   \n",
       "1      19960102          business   \n",
       "2      19960102          business   \n",
       "3      19960102          business   \n",
       "4      19960102          business   \n",
       "\n",
       "                                       headline_text  \n",
       "0  Smurfit's share price in retreat despite recor...  \n",
       "1        Jamont plans £5m investment to update plant  \n",
       "2     Management is blamed for most company failures  \n",
       "3  Forte expected to announce a special dividend ...  \n",
       "4                Accountancy firm adopts name change  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/irishtimes-date-text.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis='index', how='any', inplace=True)\n",
    "df['simple_category'] = df.headline_category.str.split('.').str.get(0)\n",
    "df['headline_text'] = df['headline_text'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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_category</th>\n",
       "      <th>headline_text</th>\n",
       "      <th>simple_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>[smurfit, share, price, retreat, despite, reco...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>[jamont, plan, 5m, investment, update, plant]</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>[management, blamed, company, failure]</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>[forte, expected, announce, special, dividend,...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19960102</td>\n",
       "      <td>business</td>\n",
       "      <td>[accountancy, firm, adopts, name, change]</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   publish_date headline_category  \\\n",
       "0      19960102          business   \n",
       "1      19960102          business   \n",
       "2      19960102          business   \n",
       "3      19960102          business   \n",
       "4      19960102          business   \n",
       "\n",
       "                                       headline_text simple_category  \n",
       "0  [smurfit, share, price, retreat, despite, reco...        business  \n",
       "1      [jamont, plan, 5m, investment, update, plant]        business  \n",
       "2             [management, blamed, company, failure]        business  \n",
       "3  [forte, expected, announce, special, dividend,...        business  \n",
       "4          [accountancy, firm, adopts, name, change]        business  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "\n",
    "# corpora_path = '/media/sean/634d9e6c-0d5c-4a60-aa43-9d19a276b10a/nltk_data'\n",
    "# nltk.data.path.append(corpora_path)\n",
    "\n",
    "nltk.data.find('corpora/wordnet')\n",
    "nltk.data.find('corpora/stopwords')\n",
    "stops = set(stopwords.words('english'))\n",
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def clean_headlines(headline):\n",
    "    tokens = tokenizer.tokenize(headline)\n",
    "    return [lemmatizer.lemmatize(token) for token in tokens if token not in stops]\n",
    "\n",
    "df['headline_text'] = df['headline_text'].apply(clean_headlines)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "unable to import 'smart_open.gcs', disabling that module\n"
     ]
    }
   ],
   "source": [
    "from gensim import corpora\n",
    "from gensim.models.ldamulticore import LdaMulticore\n",
    "\n",
    "texts = df['headline_text'].tolist()\n",
    "dictionary = corpora.Dictionary(texts)\n",
    "corpus = [dictionary.doc2bow(txt) for txt in texts]\n",
    "lda = LdaMulticore(corpus, id2word=dictionary, num_topics=6, workers=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.008*\"one\" + 0.007*\"say\" + 0.006*\"get\" + 0.005*\"irish\" + 0.005*\"health\"'),\n",
       " (1, '0.007*\"000\" + 0.007*\"short\" + 0.007*\"1\" + 0.006*\"pay\" + 0.006*\"job\"'),\n",
       " (2, '0.019*\"man\" + 0.010*\"year\" + 0.009*\"dublin\" + 0.007*\"go\" + 0.007*\"two\"'),\n",
       " (3,\n",
       "  '0.010*\"time\" + 0.010*\"irish\" + 0.009*\"back\" + 0.008*\"first\" + 0.008*\"right\"'),\n",
       " (4,\n",
       "  '0.012*\"new\" + 0.008*\"irish\" + 0.008*\"ireland\" + 0.008*\"say\" + 0.008*\"review\"'),\n",
       " (5, '0.009*\"u\" + 0.007*\"ireland\" + 0.006*\"say\" + 0.006*\"eu\" + 0.006*\"new\"')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.print_topics(num_words=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el128301403994779342888393633328\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el128301403994779342888393633328_data = {\"mdsDat\": {\"x\": [0.06157252190524973, 0.016598574577326036, -0.21686772323425363, 0.0033494791752374446, 0.023953854717074563, 0.11139329285936586], \"y\": [-0.07550582061170123, -0.08795024686472872, -0.03760521407919628, 0.2069926639373476, 0.023776754295008005, -0.029708136676729446], \"topics\": [1, 2, 3, 4, 5, 6], \"cluster\": [1, 1, 1, 1, 1, 1], \"Freq\": [15.029424667358398, 17.151519775390625, 17.561418533325195, 15.415337562561035, 15.749667167663574, 19.092636108398438]}, \"tinfo\": {\"Term\": [\"man\", \"time\", \"right\", \"short\", \"first\", \"one\", \"hit\", \"review\", \"1\", \"diary\", \"back\", \"world\", \"000\", \"year\", \"irishman\", \"three\", \"health\", \"warns\", \"go\", \"profit\", \"rise\", \"death\", \"irish\", \"garda\\u00ed\", \"ireland\", \"job\", \"crash\", \"trump\", \"killed\", \"murder\", \"vodafone\", \"picnic\", \"cardiff\", \"cullinan\", \"aidan\", \"punchestown\", \"ipo\", \"samsung\", \"lennon\", \"yemen\", \"hacking\", \"nokia\", \"cyber\", \"gerrard\", \"tipp\", \"lawsuit\", \"phil\", \"thornley\", \"appearance\", \"forthcoming\", \"mcnamara\", \"catalonia\", \"oliver\", \"ali\", \"arrangement\", \"affordable\", \"album\", \"clever\", \"ibrahim\", \"playoff\", \"trump\", \"sam\", \"bin\", \"healthcare\", \"moyes\", \"eircom\", \"fiction\", \"became\", \"device\", \"terry\", \"morning\", \"becoming\", \"iphone\", \"gerry\", \"education\", \"watchdog\", \"christmas\", \"everything\", \"apple\", \"transfer\", \"t\\u00e1naiste\", \"u2\", \"warns\", \"fixture\", \"independence\", \"health\", \"one\", \"six\", \"quiz\", \"getting\", \"benefit\", \"special\", \"business\", \"cross\", \"golf\", \"nation\", \"referendum\", \"release\", \"power\", \"china\", \"abortion\", \"ira\", \"card\", \"red\", \"medical\", \"food\", \"good\", \"get\", \"plan\", \"say\", \"early\", \"year\", \"irish\", \"new\", \"london\", \"win\", \"life\", \"change\", \"state\", \"make\", \"report\", \"court\", \"job\", \"cork\", \"child\", \"cut\", \"claim\", \"sale\", \"online\", \"cowen\", \"2016\", \"cameron\", \"digest\", \"leo\", \"cycling\", \"rodgers\", \"cso\", \"donal\", \"gordon\", \"katie\", \"atlantic\", \"downturn\", \"osprey\", \"redknapp\", \"boosted\", \"colm\", \"luke\", \"default\", \"ar\", \"2002\", \"blatter\", \"pharma\", \"staunton\", \"gara\", \"etc\", \"operate\", \"airbus\", \"pearse\", \"lisbon\", \"briefing\", \"shane\", \"deutsche\", \"momentum\", \"costa\", \"create\", \"short\", \"oecd\", \"homelessness\", \"tech\", \"scrap\", \"premier\", \"nature\", \"vehicle\", \"luas\", \"tool\", \"remembering\", \"1\", \"alcohol\", \"billion\", \"meeting\", \"closer\", \"thinking\", \"book\", \"reader\", \"2013\", \"2m\", \"housing\", \"church\", \"eye\", \"check\", \"na\", \"step\", \"pay\", \"film\", \"rt\\u00e9\", \"rate\", \"job\", \"5m\", \"2\", \"interest\", \"000\", \"st\", \"5\", \"fund\", \"cut\", \"martin\", \"league\", \"buy\", \"hospital\", \"firm\", \"state\", \"deal\", \"may\", \"say\", \"top\", \"irish\", \"u\", \"bank\", \"drug\", \"dublin\", \"fall\", \"rise\", \"government\", \"plan\", \"take\", \"home\", \"city\", \"garda\\u00ed\", \"martyn\", \"miriam\", \"mcilroy\", \"sharon\", \"robbie\", \"gatland\", \"weld\", \"crunch\", \"hull\", \"mercedes\", \"joseph\", \"migration\", \"sachs\", \"thumb\", \"toulon\", \"audi\", \"pep\", \"relishing\", \"flannery\", \"150m\", \"bombardier\", \"cctv\", \"dj\", \"leaked\", \"fitting\", \"honda\", \"rae\", \"villiers\", \"nominated\", \"croke\", \"bmw\", \"nadal\", \"overnight\", \"gael\", \"goldman\", \"barrel\", \"trolley\", \"cody\", \"charged\", \"correction\", \"jazz\", \"lifeline\", \"wounded\", \"clarification\", \"shooting\", \"arrested\", \"assault\", \"stabbing\", \"investigate\", \"inquest\", \"man\", \"monaghan\", \"leg\", \"bomb\", \"three\", \"sentence\", \"hand\", \"oil\", \"crash\", \"fine\", \"murder\", \"rape\", \"held\", \"police\", \"sex\", \"jury\", \"death\", \"go\", \"garda\", \"kill\", \"shot\", \"year\", \"two\", \"girl\", \"house\", \"abuse\", \"co\", \"dublin\", \"killed\", \"road\", \"car\", \"attack\", \"appeal\", \"tax\", \"dy\", \"woman\", \"court\", \"000\", \"face\", \"case\", \"may\", \"new\", \"home\", \"enda\", \"sexton\", \"boi\", \"laden\", \"sony\", \"theresa\", \"mahon\", \"q\", \"toyota\", \"ray\", \"aisling\", \"thailand\", \"inequality\", \"mcgregor\", \"jamie\", \"lawlor\", \"una\", \"angela\", \"stoke\", \"jose\", \"kearney\", \"dave\", \"yahoo\", \"berkeley\", \"heaslip\", \"rb\", \"reaching\", \"hague\", \"focused\", \"damascus\", \"immigration\", \"2008\", \"bounce\", \"slowdown\", \"whole\", \"google\", \"facebook\", \"twitter\", \"hungary\", \"leopardstown\", \"gaza\", \"right\", \"human\", \"night\", \"corruption\", \"garden\", \"scotland\", \"mourinho\", \"sarkozy\", \"hit\", \"wing\", \"bike\", \"something\", \"heart\", \"buying\", \"word\", \"first\", \"privacy\", \"track\", \"time\", 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\"horizon\", \"appoints\", \"art\", \"review\", \"profit\", \"battle\", \"mortgage\", \"level\", \"great\", \"rise\", \"third\", \"zone\", \"euro\", \"phone\", \"bank\", \"new\", \"united\", \"come\", \"family\", \"ireland\", \"woman\", \"irish\", \"end\", \"say\", \"life\", \"uk\", \"record\", \"fall\", \"england\", \"sale\", \"u\", \"high\", \"world\", \"killed\", \"open\", \"day\", \"set\", \"2014\", \"omagh\", \"hike\", \"schmidt\", \"congo\", \"ebola\", \"pregnancy\", \"amazon\", \"gavin\", \"d\\u00fan\", \"fintan\", \"hub\", \"drone\", \"6bn\", \"venezuela\", \"fulham\", \"gdp\", \"f\\u00e9in\", \"providence\", \"volvo\", \"f\\u00e1il\", \"maria\", \"hsbc\", \"2020\", \"billy\", \"2006\", \"loved\", \"obesity\", \"atm\", \"innovator\", \"climate\", \"zimbabwe\", \"seeker\", \"treasure\", \"emission\", \"\\u00e9ireann\", \"lingus\", \"aer\", \"ombudsman\", \"johnny\", \"arcy\", \"putting\", \"coveney\", \"laoghaire\", \"korea\", \"failing\", \"starting\", \"asylum\", \"flu\", \"federer\", \"talent\", \"leinster\", \"ukraine\", \"working\", \"iran\", \"strike\", \"conference\", \"exercise\", \"final\", \"semi\", \"d\\u00e1il\", \"leader\", \"party\", \"europe\", \"world\", \"refugee\", \"watch\", \"talk\", \"nuclear\", \"people\", \"fianna\", \"role\", \"eu\", \"war\", \"cup\", \"u\", \"play\", \"claim\", \"vote\", \"crisis\", \"call\", \"ireland\", \"work\", \"say\", \"brexit\", \"dublin\", \"need\", \"new\", \"plan\", \"north\", \"election\", \"face\", \"irish\", \"deal\", \"child\", \"report\"], \"Freq\": [30939.0, 16630.0, 9831.0, 9325.0, 11968.0, 10497.0, 9233.0, 11962.0, 9496.0, 6891.0, 16527.0, 12493.0, 17401.0, 24215.0, 5833.0, 9826.0, 7227.0, 6690.0, 13692.0, 9589.0, 12282.0, 12516.0, 40166.0, 4720.0, 30331.0, 11936.0, 8030.0, 4430.0, 8788.0, 9653.0, 707.2611083984375, 585.3353881835938, 577.311279296875, 476.4544982910156, 491.1403503417969, 457.6981201171875, 460.49267578125, 440.7767639160156, 408.29791259765625, 526.75244140625, 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\"leopardstown\", \"level\", \"level\", \"level\", \"level\", \"level\", \"level\", \"life\", \"life\", \"life\", \"life\", \"life\", \"life\", \"lifeline\", \"lifeline\", \"lifeline\", \"lingus\", \"lingus\", \"lingus\", \"lingus\", \"lingus\", \"lingus\", \"lisbon\", \"lisbon\", \"lisbon\", \"lisbon\", \"london\", \"london\", \"london\", \"london\", \"london\", \"london\", \"long\", \"long\", \"long\", \"long\", \"long\", \"long\", \"loved\", \"luas\", \"luas\", \"luas\", \"luas\", \"luas\", \"luke\", \"mahon\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"man\", \"man\", \"man\", \"man\", \"man\", \"man\", \"maria\", \"martin\", \"martin\", \"martin\", \"martin\", \"martin\", \"martin\", \"martyn\", \"may\", \"may\", \"may\", \"may\", \"may\", \"may\", \"mcgregor\", \"mcilroy\", \"mcilroy\", \"mcnamara\", \"medical\", \"medical\", \"medical\", \"medical\", \"medical\", \"medical\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"meeting\", \"mercedes\", \"migrant\", \"migrant\", \"migrant\", \"migration\", \"miriam\", \"momentum\", \"momentum\", \"monaghan\", \"monaghan\", \"monaghan\", \"monaghan\", \"monaghan\", \"morning\", \"morning\", \"morning\", \"morning\", \"morning\", \"mortgage\", \"mortgage\", \"mortgage\", \"mortgage\", \"mortgage\", \"mourinho\", \"mourinho\", \"mourinho\", \"mourinho\", \"mourinho\", \"mourinho\", \"moyes\", \"moyes\", \"murder\", \"murder\", \"murder\", \"murder\", \"murder\", \"murder\", \"na\", \"na\", \"na\", \"na\", \"na\", \"na\", \"nadal\", \"nadal\", \"named\", \"named\", \"named\", \"named\", \"named\", \"named\", \"nation\", \"nation\", \"nation\", \"nation\", \"nation\", \"nature\", \"nature\", \"nature\", \"nature\", \"nature\", \"nature\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"nepal\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"night\", \"night\", \"night\", \"night\", \"night\", \"night\", \"nokia\", \"nominated\", \"north\", \"north\", \"north\", \"north\", \"north\", \"north\", \"norway\", \"note\", \"note\", \"note\", \"note\", \"note\", \"note\", \"november\", \"november\", \"november\", \"november\", \"nuclear\", \"nuclear\", \"nuclear\", \"nuclear\", \"nuclear\", \"nuclear\", \"obesity\", \"obesity\", \"oecd\", \"oecd\", \"oil\", \"oil\", \"oil\", \"oil\", \"oil\", \"oil\", \"oliver\", \"omagh\", \"ombudsman\", \"ombudsman\", \"ombudsman\", \"ombudsman\", \"one\", \"one\", \"one\", \"one\", \"one\", \"one\", \"online\", \"open\", \"open\", \"open\", \"open\", \"open\", \"open\", \"operate\", \"osprey\", \"overnight\", \"overnight\", \"pac\", \"paddy\", \"paddy\", \"paperback\", \"party\", \"party\", \"party\", \"party\", \"party\", \"party\", \"past\", \"past\", \"past\", \"past\", \"past\", \"past\", \"pay\", \"pay\", \"pay\", \"pay\", \"pay\", \"pay\", \"pearse\", \"people\", \"people\", \"people\", \"people\", \"people\", \"people\", \"pep\", \"pharma\", \"phil\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"phone\", \"picnic\", \"plan\", \"plan\", \"plan\", \"plan\", \"plan\", \"plan\", \"planet\", \"planet\", \"planet\", \"planet\", \"play\", \"play\", \"play\", \"play\", \"play\", \"play\", \"playing\", \"playing\", \"playing\", \"playing\", \"playing\", \"playing\", \"playoff\", \"police\", \"police\", \"police\", \"police\", \"police\", \"police\", \"power\", \"power\", \"power\", \"power\", \"power\", \"power\", \"pregnancy\", \"premier\", \"premier\", \"premier\", \"premier\", \"premier\", \"preview\", \"privacy\", \"privacy\", \"privacy\", \"privacy\", \"profit\", \"profit\", \"profit\", \"profit\", \"profit\", \"profit\", \"providence\", \"punchestown\", \"putting\", \"putting\", \"putting\", \"putting\", \"putting\", \"q\", \"q\", \"q\", \"q\", \"quiz\", \"quiz\", \"quiz\", \"quiz\", \"rae\", \"rape\", \"rape\", \"rape\", \"rape\", \"rape\", \"rape\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"ray\", \"rb\", \"reaching\", \"reader\", \"reader\", \"reader\", \"reader\", \"recession\", \"record\", \"record\", \"record\", \"record\", \"record\", \"record\", \"red\", \"red\", \"red\", \"red\", \"red\", \"redknapp\", \"referendum\", \"referendum\", \"referendum\", \"referendum\", \"referendum\", \"referendum\", \"refugee\", \"refugee\", \"refugee\", \"refugee\", \"refugee\", \"refugee\", \"release\", \"release\", \"release\", \"release\", \"release\", \"release\", \"relishing\", \"remembering\", \"remembering\", \"remembering\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"repossession\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"right\", \"right\", \"right\", \"right\", \"right\", \"right\", \"rise\", \"rise\", \"rise\", \"rise\", \"rise\", \"rise\", \"road\", \"road\", \"road\", \"road\", \"road\", \"road\", \"robbie\", \"rodgers\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"ronan\", \"root\", \"root\", \"root\", \"root\", \"root\", \"root\", \"rory\", \"rt\\u00e9\", \"rt\\u00e9\", \"rt\\u00e9\", \"rt\\u00e9\", \"rt\\u00e9\", \"rt\\u00e9\", \"r\\u00f3is\\u00edn\", \"sachs\", \"said\", \"said\", \"said\", \"said\", \"said\", \"said\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sam\", \"sam\", \"samsung\", \"sarkozy\", \"sarkozy\", \"sarkozy\", \"say\", \"say\", \"say\", \"say\", \"say\", \"say\", \"schmidt\", \"scotland\", \"scotland\", \"scotland\", \"scotland\", \"scotland\", \"scotland\", \"scrap\", \"scrap\", \"seanad\", \"seanad\", \"seanad\", \"seanad\", \"seeker\", \"seeker\", \"seeker\", \"semi\", \"semi\", \"semi\", \"semi\", \"semi\", \"sentence\", \"sentence\", \"sentence\", \"sentence\", \"sentence\", \"sentence\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"sex\", \"sex\", \"sex\", \"sex\", \"sex\", \"sex\", \"sexton\", \"shane\", \"shane\", \"sharon\", \"shooting\", \"shooting\", \"shooting\", \"shooting\", \"shooting\", \"shooting\", \"short\", \"short\", \"short\", \"short\", \"short\", \"short\", \"shot\", \"shot\", \"shot\", \"shot\", \"shot\", \"shot\", \"six\", \"six\", \"six\", \"six\", \"six\", \"six\", \"slowdown\", \"slowdown\", \"something\", \"something\", \"something\", \"something\", \"something\", \"sony\", \"space\", \"space\", \"space\", \"space\", \"space\", \"special\", \"special\", \"special\", \"special\", \"special\", \"special\", \"st\", \"st\", \"st\", \"st\", \"st\", \"st\", \"stabbing\", \"stabbing\", \"stabbing\", \"stabbing\", \"stabbing\", \"stabbing\", \"starting\", \"starting\", \"starting\", \"starting\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"staunton\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"stoke\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"supreme\", \"swansea\", \"swine\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"talent\", \"talent\", \"talent\", \"talent\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tech\", \"tech\", \"tech\", \"temperature\", \"terry\", \"terry\", \"tesco\", \"tesco\", \"thailand\", \"theresa\", \"thinking\", \"thinking\", \"thinking\", \"thinking\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"thornley\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"thumb\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tipp\", \"tony\", \"tony\", \"tony\", \"tony\", \"tony\", \"tony\", \"tool\", \"tool\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"tottenham\", \"tottenham\", \"toulon\", \"toyota\", \"track\", \"track\", \"track\", \"track\", \"track\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"treasure\", \"treasure\", \"trichet\", \"trolley\", \"trolley\", \"trump\", \"trump\", \"trump\", \"trump\", \"turner\", \"turner\", \"turner\", \"turner\", \"turner\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"t\\u00e1naiste\", \"t\\u00e1naiste\", \"t\\u00e1naiste\", \"t\\u00e1naiste\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u2\", \"u2\", \"u2\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"ukraine\", \"ukraine\", \"ukraine\", \"ukraine\", \"una\", \"united\", \"united\", \"united\", \"united\", \"united\", \"united\", \"vehicle\", \"vehicle\", \"vehicle\", \"vehicle\", \"venezuela\", \"villiers\", \"vodafone\", \"volvo\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"warns\", \"warns\", \"warns\", \"warns\", \"warns\", \"warns\", \"watch\", \"watch\", \"watch\", \"watch\", \"watch\", \"watch\", \"watchdog\", \"watchdog\", \"watchdog\", \"watchdog\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"weekend\", \"weekend\", \"weekend\", \"weekend\", \"weekend\", \"weekend\", \"weld\", \"whole\", \"whole\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"wing\", \"wing\", \"wing\", \"wing\", \"wing\", \"winner\", \"winner\", \"winner\", \"winner\", \"winner\", \"winner\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"working\", \"working\", \"working\", \"working\", \"working\", \"working\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"wounded\", \"wounded\", \"wounded\", \"yahoo\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yemen\", \"yemen\", \"zimbabwe\", \"zimbabwe\", \"zone\", \"zone\", \"zone\", \"zone\", \"zone\", \"zone\", \"\\u00e9ireann\", \"\\u00e9ireann\", \"\\u00e9ireann\", \"\\u00f3\", \"\\u00f3\", \"\\u00f3\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3, 4, 5, 6]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el128301403994779342888393633328\", ldavis_el128301403994779342888393633328_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el128301403994779342888393633328\", ldavis_el128301403994779342888393633328_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el128301403994779342888393633328\", ldavis_el128301403994779342888393633328_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis as ldavis\n",
    "import pyLDAvis.gensim as ldgensim\n",
    "\n",
    "lda_display = ldgensim.prepare(lda, corpus, dictionary, sort_topics=False)\n",
    "ldavis.display(lda_display)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the prevalence of 'Ireland' words in each of the topics ('irish', 'ireland', 'dublin', 'irishman'), I'm going to treat these as stop words and remove them from the headlines before running the analysis again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import multiprocessing\n",
    "\n",
    "def remove_ireland_words(headline_lst):\n",
    "    return [word for word in headline_lst if word not in ('irish', 'ireland', 'dublin', 'irishman', 'u')]\n",
    "\n",
    "df['headline_text'] = df['headline_text'].apply(remove_ireland_words)\n",
    "\n",
    "texts = df['headline_text'].tolist()\n",
    "dictionary = corpora.Dictionary(texts)\n",
    "corpus = [dictionary.doc2bow(txt) for txt in texts]\n",
    "lda2 = LdaMulticore(corpus, id2word=dictionary, num_topics=6, workers=multiprocessing.cpu_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.009*\"short\" + 0.006*\"life\" + 0.006*\"man\" + 0.005*\"game\" + 0.005*\"trial\"'),\n",
       " (1, '0.011*\"new\" + 0.007*\"uk\" + 0.006*\"job\" + 0.006*\"court\" + 0.005*\"get\"'),\n",
       " (2,\n",
       "  '0.015*\"man\" + 0.011*\"year\" + 0.008*\"000\" + 0.008*\"child\" + 0.008*\"world\"'),\n",
       " (3,\n",
       "  '0.008*\"new\" + 0.007*\"report\" + 0.007*\"market\" + 0.006*\"say\" + 0.006*\"go\"'),\n",
       " (4,\n",
       "  '0.011*\"time\" + 0.006*\"take\" + 0.005*\"road\" + 0.005*\"back\" + 0.005*\"week\"'),\n",
       " (5,\n",
       "  '0.011*\"say\" + 0.006*\"review\" + 0.005*\"eu\" + 0.005*\"price\" + 0.005*\"talk\"')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda2.print_topics(num_words=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el128301403950130870569306231042\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el128301403950130870569306231042_data = {\"mdsDat\": {\"x\": [0.1335093631001764, -0.048037948422105646, 0.13752274942839732, -0.06736801135581888, -0.02838652776288152, -0.12723962498776764], \"y\": [0.036956302820196754, -0.20418845198490662, -0.002910032463037063, 0.07398588599353605, 0.029099360299143427, 0.06705693533506726], \"topics\": [1, 2, 3, 4, 5, 6], \"cluster\": [1, 1, 1, 1, 1, 1], \"Freq\": [14.66075325012207, 16.177053451538086, 17.68581199645996, 16.895666122436523, 16.77235984802246, 17.80835723876953]}, \"tinfo\": {\"Term\": [\"man\", \"short\", \"time\", \"uk\", \"child\", \"market\", \"diary\", \"world\", \"report\", \"year\", \"charge\", \"go\", \"home\", \"road\", \"euro\", \"health\", \"held\", \"garda\", \"000\", \"game\", \"review\", \"life\", \"job\", \"final\", \"price\", \"trial\", \"garda\\u00ed\", \"murder\", \"week\", \"come\", \"hike\", \"ronaldo\", \"rodgers\", \"briefing\", \"4bn\", \"q\", \"gordon\", \"mahon\", \"mcgregor\", 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\"tribunal\", \"tribunal\", \"tribunal\", \"tribunal\", \"tribunal\", \"tribunal\", \"trick\", \"trick\", \"trick\", \"trick\", \"trick\", \"trump\", \"trump\", \"trump\", \"turner\", \"turner\", \"turner\", \"twitter\", \"twitter\", \"twitter\", \"twitter\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"typhoon\", \"u2\", \"u2\", \"u2\", \"ubs\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"uk\", \"ulster\", \"ulster\", \"ulster\", \"ulster\", \"ulster\", \"ulster\", \"una\", \"upper\", \"usa\", \"valencia\", \"varadkar\", \"varadkar\", \"varadkar\", \"varadkar\", \"varadkar\", \"venezuela\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"visit\", \"visit\", \"visit\", \"visit\", \"visit\", \"visit\", \"vodafone\", \"volkswagen\", \"volkswagen\", \"volvo\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"watchdog\", \"watchdog\", \"watchdog\", \"watchdog\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"website\", \"week\", \"week\", 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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el128301403950130870569306231042\", ldavis_el128301403950130870569306231042_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el128301403950130870569306231042\", ldavis_el128301403950130870569306231042_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el128301403950130870569306231042\", ldavis_el128301403950130870569306231042_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_display2 = ldgensim.prepare(lda2, corpus, dictionary, sort_topics=False)\n",
    "ldavis.display(lda_display2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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