Topic Modelling with Gensim_v6c_SE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic modelling with Gensim : the LDA algorithm on the Statistics Explained articles\n",
    "\n",
    "LDA is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.\n",
    "\n",
    "### 1. Loading of the required libraries and vocabularies.\n",
    "***\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import re\n",
    "import pprint\n",
    "\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis # don't skip this\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n",
    "\n",
    "from pprint import pprint\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Pre-processing\n",
    "\n",
    "* Read a file with scraped content from the Statistics Explained articles. This is the file currently produced with an additional column \"raw content\" which gathers all titles and contents from an article (a refinement here can be to give more weight to the title words).\n",
    "* In later versions, the **corresponding tables will be directly exported from the database**.\n",
    "* Discard records with duplicate abstracts and/or titles and/or contents.\n",
    "* Apply some cleansing steps in the contents: replace anything except digits,letters,commas and dots by space, remove more than one consecutive spaces and other redundant spaces. \n",
    "\n",
    "There are 615 articles available for the analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>categories</th>\n",
       "      <th>raw content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air safety statistics in the EU</td>\n",
       "      <td>Detailed data from the  European Aviation Safe...</td>\n",
       "      <td>['Air', 'Passengers', 'Statistical article', '...</td>\n",
       "      <td>Overview of fatalities in air transport in the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASEAN-EU - international trade in goods statis...</td>\n",
       "      <td>This article provides a picture of the inte...</td>\n",
       "      <td>['Non-EU countries', 'Trade in goods', 'Statis...</td>\n",
       "      <td>ASEAN countries trade in goods with main partn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air pollution statistics - emission inventories</td>\n",
       "      <td>This article is about emissions of air pollut...</td>\n",
       "      <td>['Air pollution', 'Environment', 'Health', 'He...</td>\n",
       "      <td>General overview Air pollution harms human hea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Absences from work - quarterly statistics</td>\n",
       "      <td>Absences from work can be classified into two...</td>\n",
       "      <td>['Employment', 'Labour market', 'Statistical a...</td>\n",
       "      <td>Absences from work sharply increase in first h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Annual national accounts - evolution of the in...</td>\n",
       "      <td>This article explains the income components of...</td>\n",
       "      <td>['Authored article', 'National accounts (incl....</td>\n",
       "      <td>Shares of income components to GDP in 2019 In ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Accommodation and food service statistics - NA...</td>\n",
       "      <td>This article presents an overview of statist...</td>\n",
       "      <td>['Services', 'Statistical article', 'Structura...</td>\n",
       "      <td>Structural profile The accommodation and food ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>Accidents at work statistics</td>\n",
       "      <td>This article presents a set of main statistica...</td>\n",
       "      <td>['Accidents at work', 'Health', 'Health and sa...</td>\n",
       "      <td>Number of accidents In 2018, there were 3.1 mi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Accidents at work - statistics on causes and c...</td>\n",
       "      <td>This article presents a set of main statistica...</td>\n",
       "      <td>['Accidents at work', 'Health', 'Health and sa...</td>\n",
       "      <td>Workstation accidents Non fatal accidents In 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Accidents at work - statistics by economic act...</td>\n",
       "      <td>This article presents a set of main statistic...</td>\n",
       "      <td>['Accidents at work', 'Health', 'Health and sa...</td>\n",
       "      <td>Developments over time Non fatal accidents In ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>Accidents and injuries statistics</td>\n",
       "      <td>This article presents an overview of  European...</td>\n",
       "      <td>['Health', 'Health status', 'Statistical artic...</td>\n",
       "      <td>Deaths from accidents, injuries and assault In...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>615 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  \\\n",
       "0                      Air safety statistics in the EU   \n",
       "1    ASEAN-EU - international trade in goods statis...   \n",
       "2      Air pollution statistics - emission inventories   \n",
       "3            Absences from work - quarterly statistics   \n",
       "4    Annual national accounts - evolution of the in...   \n",
       "..                                                 ...   \n",
       "610  Accommodation and food service statistics - NA...   \n",
       "611                       Accidents at work statistics   \n",
       "612  Accidents at work - statistics on causes and c...   \n",
       "613  Accidents at work - statistics by economic act...   \n",
       "614                  Accidents and injuries statistics   \n",
       "\n",
       "                                              abstract  \\\n",
       "0    Detailed data from the  European Aviation Safe...   \n",
       "1       This article provides a picture of the inte...   \n",
       "2     This article is about emissions of air pollut...   \n",
       "3     Absences from work can be classified into two...   \n",
       "4    This article explains the income components of...   \n",
       "..                                                 ...   \n",
       "610    This article presents an overview of statist...   \n",
       "611  This article presents a set of main statistica...   \n",
       "612  This article presents a set of main statistica...   \n",
       "613   This article presents a set of main statistic...   \n",
       "614  This article presents an overview of  European...   \n",
       "\n",
       "                                            categories  \\\n",
       "0    ['Air', 'Passengers', 'Statistical article', '...   \n",
       "1    ['Non-EU countries', 'Trade in goods', 'Statis...   \n",
       "2    ['Air pollution', 'Environment', 'Health', 'He...   \n",
       "3    ['Employment', 'Labour market', 'Statistical a...   \n",
       "4    ['Authored article', 'National accounts (incl....   \n",
       "..                                                 ...   \n",
       "610  ['Services', 'Statistical article', 'Structura...   \n",
       "611  ['Accidents at work', 'Health', 'Health and sa...   \n",
       "612  ['Accidents at work', 'Health', 'Health and sa...   \n",
       "613  ['Accidents at work', 'Health', 'Health and sa...   \n",
       "614  ['Health', 'Health status', 'Statistical artic...   \n",
       "\n",
       "                                           raw content  \n",
       "0    Overview of fatalities in air transport in the...  \n",
       "1    ASEAN countries trade in goods with main partn...  \n",
       "2    General overview Air pollution harms human hea...  \n",
       "3    Absences from work sharply increase in first h...  \n",
       "4    Shares of income components to GDP in 2019 In ...  \n",
       "..                                                 ...  \n",
       "610  Structural profile The accommodation and food ...  \n",
       "611  Number of accidents In 2018, there were 3.1 mi...  \n",
       "612  Workstation accidents Non fatal accidents In 2...  \n",
       "613  Developments over time Non fatal accidents In ...  \n",
       "614  Deaths from accidents, injuries and assault In...  \n",
       "\n",
       "[615 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
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    }
   ],
   "source": [
    "dat = pd.read_excel('articles_4_30_12_26.xlsx')\n",
    "dat = dat[['title','abstract','categories','raw content']]\n",
    "\n",
    "dat = dat.dropna(axis=0,subset=[\"title\"])\n",
    "dat = dat.dropna(axis=0,subset=[\"abstract\"])\n",
    "dat = dat.dropna(axis=0,subset=[\"raw content\"])\n",
    "dat.reset_index(drop=True, inplace=True)\n",
    "\n",
    "dat['raw content'] = dat['raw content'].apply(lambda x: re.sub(\"[^a-z\\\\.,A-Z0-9]\", \" \",x)) ## replace anything except digits,letters,comma and dot by space \n",
    "dat['raw content'] = dat['raw content'].apply(lambda x: re.sub(' +', ' ',x)) ## remove more than one spaces\n",
    "dat['raw content'] = dat['raw content'].apply(lambda x: re.sub('^ +| +$', '',x)) ## remove start and end spaces\n",
    "dat['raw content'] = dat['raw content'].apply(lambda x: re.sub(' ,',',',x)) ## space-comma-space -> comma-space\n",
    "\n",
    "dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Pre-processing input data (cont).\n",
    "***\n",
    "\n",
    "Next we define a function _sent_to_words_ which accepts a series of texts and:\n",
    "* Converts everything to lowercase and drops accents, \n",
    "* Deletes stop words, \n",
    "* Tokenizes the texts, \n",
    "* Apply Porter's stemmer and keeps as final tokens the resulting terms with minimum length 5. \n",
    "\n",
    "We call this function with the articles definitions in column \"raw content\" of dataframe _dat_. The result, _texts_ is a nested list with 615 outer elements corresponding to the SE articles. The inner lists contain the tokens for each text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First text: \n",
      " [['overview', 'fatal', 'transport', 'annual', 'safeti', 'review', 'european', 'aviat', 'safeti', 'agenc', 'highlight', 'except', 'global', 'airlin', 'safeti', 'fewer', 'fatal', 'industri', 'histori', 'death', 'annual', 'safeti', 'review', 'remind', 'there', 'complac', 'after', 'increas', 'observ', 'person', 'total', 'aviat', 'accid', 'territori', 'involv', 'aircraft', 'regist', 'member', 'state', 'european', 'union', 'record', 'slight', 'decreas', 'fatal', 'everi', 'accid', 'fatal', 'regist', 'categori', 'gener', 'aviat', 'figur', 'specif', 'categori', 'aircraft', 'maximum', 'categori', 'compris', 'small', 'aeroplan', 'glider', 'microlight', 'balloon', 'compar', 'which', 'where', 'transport', 'fatal', 'record', 'period', 'categori', 'commerci', 'transport', 'fatal', 'categori', 'fatal', 'fatal', 'regist', 'aerial', 'accid', 'while', 'person', 'gener', 'aviat', 'accid', 'involv', 'aircraft', 'accid', 'gener', 'aviat', 'under', 'fatal', 'occur', 'countri', 'franc', 'fatal', 'germani', 'fatal', 'spain', 'fatal', 'itali', 'fatal', 'figur', 'number', 'person', 'accid', 'involv', 'regist', 'aircraft', 'column', 'split', 'fatal', 'regist', 'accid', 'territori', 'fatal', 'record', 'elsewher', 'world', 'often', 'singl', 'accid', 'respons', 'consider', 'proport', 'fatal', 'given', 'major', 'accid', 'involv', 'regist', 'aircraft', 'regist', 'howev', 'crash', 'german', 'aircraft', 'french', 'fatal', 'accid', 'sinai', 'peninsula', 'egypt', 'involv', 'irish', 'regist', 'aircraft', 'charter', 'flight', 'fatal', 'aircraft', 'regist', 'spain', 'algerian', 'crash', 'fatal', 'accid', 'south', 'atlant', 'ocean', 'involv', 'french', 'aircraft', 'brazil', 'claim', 'repres', 'fatal', 'regist', 'earlier', 'crash', 'spanish', 'madrid', 'baraja', 'airport', 'result', 'fatal', 'accid', 'involv', 'french', 'regist', 'aircraft', 'russian', 'compani', 'crash', 'irkutsk', 'russia', 'incid', 'account', 'death', 'fatal', 'victim', 'crash', 'cypru', 'regist', 'aircraft', 'close', 'athen', 'there', 'other', 'fatal', 'transport', 'accid', 'airspac', 'european', 'union', 'involv', 'aircraft', 'regist', 'european', 'union', 'figur', 'present', 'number', 'fatal', 'commerci', 'transport', 'accid', 'territori', 'regardless', 'whether', 'aircraft', 'involv', 'regist', 'member', 'state', 'anoth', 'countri', 'inform', 'figur', 'includ', 'accid', 'crash', 'ukrainian', 'regist', 'passeng', 'aircraft', 'close', 'thessaloniki', 'greec', 'fatal', 'collis', 'between', 'russian', 'passeng', 'aircraft', 'bahraini', 'regist', 'cargo', 'aircraft', 'south', 'germani', 'victim', 'howev', 'previous', 'mention', 'accid', 'involv', 'irish', 'regist', 'aircraft', 'sinai', 'peninsula', 'egypt', 'french', 'regist', 'aircraft', 'irkutsk', 'russia', 'includ', 'present', 'figur', 'these', 'occur', 'outsid', 'territori', 'experienc', 'particularli', 'number', 'fatal', 'three', 'major', 'crash', 'french', 'aircraft', 'mountain', 'franc', 'death', 'dutch', 'aircraft', 'portug', 'airport', 'fatal', 'injur', 'regist', 'cargo', 'plane', 'amsterdam', 'suburb', 'fatal', 'which', 'ground', 'singl', 'person', 'transport', 'accid', 'european', 'union', 'aerial', 'aerial', 'denot', 'aircraft', 'specialis', 'servic', 'agricultur', 'construct', 'photographi', 'survei', 'observ', 'patrol', 'search', 'rescu', 'aerial', 'advertis', 'between', 'number', 'person', 'aerial', 'accid', 'involv', 'aircraft', 'regist', 'member', 'state', 'fluctuat', 'between', 'death', 'person', 'total', 'slight', 'increas', 'compar', 'previou', 'fatal', 'fatal', 'regist', 'occur', 'sweden', 'parachut', 'there', 'major', 'accid', 'involv', 'aerial', 'special', 'aeroplan', 'collis', 'between', 'aircraft', 'parachut', 'slovakia', 'fatal', 'accid', 'consider', 'higher', 'number', 'fatal', 'annual', 'averag', 'although', 'actual', 'number', 'accid', 'fatal', 'averag', 'gener', 'aviat', 'gener', 'aviat', 'aeroplan', 'helicopt', 'consist', 'civil', 'aviat', 'other', 'commerci', 'transport', 'specif', 'aerial', 'gener', 'aviat', 'categori', 'aircraft', 'maximum', 'under', 'gener', 'aviat', 'categori', 'where', 'accid', 'occur', 'fatal', 'record', 'transport', 'regist', 'categori', 'except', 'number', 'fatal', 'record', 'commerci', 'transport', 'germanw', 'crash', 'mention', 'there', 'person', 'gener', 'aviat', 'accid', 'involv', 'regist', 'aircraft', 'decreas', 'around', 'compar', 'previou', 'where', 'fatal', 'record', 'fewer', 'fatal', 'regist', 'everi', 'accid', 'territori', 'involv', 'aircraft', 'regist', 'where', 'fatal', 'regist', 'accid', 'light', 'gener', 'aviat', 'involv', 'aircraft', 'under', 'person', 'their', 'accid', 'regist', 'aircraft', 'within', 'territori', 'categori', 'includ', 'addit', 'smaller', 'aeroplan', 'balloon', 'dirig', 'motorglid', 'microlight', 'small', 'helicopt', 'annual', 'safeti', 'review', 'fatal', 'occur', 'accid', 'small', 'aeroplan', 'especi', 'phase', 'glider', 'detail', 'aviat', 'categori', 'countri', 'period', 'there', 'singl', 'fatal', 'accid', 'involv', 'aircraft', 'regist', 'luxembourg', 'malta', 'period', 'there', 'twelv', 'further', 'member', 'state', 'where', 'number', 'fatal', 'accid', 'involv', 'aircraft', 'regist', 'countri', 'territori', 'commerci', 'transport', 'there', 'fatal', 'accid', 'aircraft', 'regist', 'germanw', 'accid', 'march', 'account', 'death', 'period', 'person', 'connect', 'aerial', 'accid', 'involv', 'regist', 'aircraft', 'territori', 'period', 'seriou', 'accid', 'relat', 'aerial', 'aforement', 'accid', 'slovakia', 'fatal', 'accid', 'under', 'rather', 'broad', 'categori', 'gener', 'aviat', 'specif', 'aircraft', 'under', 'occur', 'aircraft', 'regist', 'franc', 'germani', 'small', 'aircraft', 'involv', 'accid', 'often', 'occur', 'countri', 'which', 'aircraft', 'regist', 'howev', 'other', 'aviat', 'categori', 'differ', 'between', 'countri', 'registri', 'aircraft', 'countri', 'occurr', 'accid', 'higher', 'similar', 'figur', 'fatal', 'commerci', 'transport', 'territori', 'detail', 'number', 'fatal', 'period', 'occur', 'territori', 'individu', 'member', 'state', 'regardless', 'countri', 'registr', 'aircraft', 'involv']]\n",
      "\n",
      "Total texts:  615 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from gensim.parsing.preprocessing import remove_stopwords\n",
    "from gensim.parsing.porter import PorterStemmer\n",
    "def sent_to_words(sentences):\n",
    "    p = PorterStemmer()\n",
    "    for sentence in sentences:\n",
    "        sentence = str(gensim.utils.simple_preprocess(sentence, deacc=True))\n",
    "        sentence = remove_stopwords(sentence) \n",
    "        tokens = gensim.utils.tokenize(sentence)\n",
    "        sentence = [p.stem(token) for token in tokens if len(p.stem(token)) >= 5] ##minimum length = 5 \n",
    "        yield(sentence)\n",
    "                \n",
    "texts = list(sent_to_words(dat['raw content']))\n",
    "print('\\nFirst text: \\n',texts[:1])\n",
    "print('\\nTotal texts: ',len(texts),'\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Grid search for the selection of the best LDA model.\n",
    "***\n",
    "\n",
    "Before running the grid search, we create a vocabulary from _texts_ with name _id2word_. This is a dictionary the numeric ids of the terms as keys and the terms themselves as values. The terms are initially sorted alphabetically. \n",
    "\n",
    "In the grid search, before calling _ldamodel.LdaModel()_ we create a mapping with name _corpus_ of texts to terms in the bag-of-words (BOW) format. Each text is mapped into a list with 2-tuples: (word id, word count in each text). The terms in the vocabulary are then re-ordered according to their order of appearance in the texts. We set the bounds on the appearance of terms in the texts with the function _filter_extremes()_ on the _id2word_ vocabulary. \n",
    " \n",
    "The parameters in the call of the function _ldamodel.LdaModel()_ and the number of topics were selected by trial and error and also through **grid search runs**. We run several such runs with different combinations and values for the hyper-parameters. \n",
    "\n",
    "The quality measure for the selection of the best LDA model is the coherence score. This is based on the work in Röder, M., Both, A., & Hinneburg, A. (2015, February). Exploring the space of topic coherence measures. In *Proceedings of the eighth ACM international conference on Web search and data mining* (pp. 399-408), available [here](http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf). \n",
    "\n",
    "The (disabled) code for an **indicative** grid search run is shown below. Do **not** comment-out the first line. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%script false --no-raise-error ## DO NOT UN-COMMENT THIS! THIS CELL IS NOT SUPPOSED TO RUN\n",
    "\n",
    "from gensim import corpora\n",
    "from gensim.models import CoherenceModel\n",
    "from itertools import product\n",
    "import copy\n",
    "\n",
    "id2word = corpora.Dictionary(texts) #Gensim creates a unique id for each word in the document. \n",
    "#id2word_keep = id2word\n",
    "id2word_keep = copy.deepcopy(id2word)\n",
    "\n",
    "def expand_grid(dictionary):\n",
    "    return pd.DataFrame([row for row in product(*dictionary.values())], \n",
    "                       columns=dictionary.keys())\n",
    "\n",
    "dictionary = {'num_topics':[20, 30, 40], 'passes': [50], 'alpha': ['auto'], \n",
    "              'decay': [0.3, 0.5, 0.75], 'iterations':[300],'lb':[0.01, 0.05], 'ub':[0.5,0.75]}\n",
    "\n",
    "#dictionary = {'num_topics':[20, 30, 40], 'passes': [40,50,60], 'alpha': ['symmetric', 'asymmetric','auto'], \n",
    "#              'decay': [0.3, 0.5, 0.75], 'iterations':[300,500],'lb':[0.01, 0.05], 'ub':[0.5,0.75]}\n",
    "\n",
    "\n",
    "\n",
    "grid = expand_grid(dictionary)\n",
    "print(grid)\n",
    "best_coherence = 0\n",
    "best_params = -1\n",
    "for i in range(len(grid)):\n",
    "    \n",
    "   \n",
    "\n",
    "    #Create Dictionary\n",
    "    #id2word = id2word_keep \n",
    "    id2word = copy.deepcopy(id2word_keep)\n",
    "    id2word.filter_extremes(no_below=grid.loc[i,'lb']*len(texts), no_above=grid.loc[i,'ub'])\n",
    "\n",
    "    print('Terms in the vocabulary: ',len(id2word),'\\n')\n",
    "\n",
    "    corpus = [id2word.doc2bow(text) for text in texts] \n",
    "\n",
    "    print('Testing with row ',i+1,' of ',len(grid),'\\n')\n",
    "    lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=grid.loc[i,'num_topics'],\n",
    "                                           random_state=100,\n",
    "                                           update_every=0,\n",
    "                                           passes=grid.loc[i,'passes'],\n",
    "                                           alpha=grid.loc[i,'alpha'],\n",
    "                                           iterations=grid.loc[i,'iterations'],\n",
    "                                           decay = grid.loc[i,'decay'],\n",
    "                                           per_word_topics=True,\n",
    "                                           eval_every=None)\n",
    "    \n",
    "    print(grid.loc[i,:])\n",
    "    \n",
    "    coherence_model_lda = CoherenceModel(model=lda_model, texts=texts, dictionary=id2word, coherence='c_v')\n",
    "    coherence_lda = coherence_model_lda.get_coherence()\n",
    "    print('\\nCoherence Score: ', coherence_lda,'\\n')\n",
    "    grid.loc[i,'Coherence'] = coherence_lda\n",
    "    \n",
    "    if coherence_lda > best_coherence:\n",
    "        best_coherence = coherence_lda\n",
    "        best_params = i\n",
    "        \n",
    "    print('\\nBest so far: ',best_coherence,' at row ',best_params+1,'\\n')    \n",
    "\n",
    "grid.to_excel('grid_SE.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Fitting the selected LDA model\n",
    "***\n",
    "\n",
    "The next code shows the fitting of the LDA model with the selected parameters found (20 topics). Compared to the runs with the SE Glossary entries, we found that we had to lower the _decay_ parameter. Also, as expected, the upper bound on the percentage of occurence of terms in the articles had to be set to a much lower value, 0.5. The coherence value of 0.54 is considered quite good. We store the fitted model in an object _lda_model_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary(1827 unique tokens: ['accid', 'actual', 'addit', 'advertis', 'after']...) \n",
      "\n",
      "Firsτ 2 texts in BOW format:\n",
      "\n",
      "[[(0, 37), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 2), (8, 1), (9, 1), (10, 4), (11, 1), (12, 1), (13, 1), (14, 15), (15, 1), (16, 1), (17, 2), (18, 12), (19, 1), (20, 1), (21, 2), (22, 1), (23, 6), (24, 1), (25, 1), (26, 1), (27, 2), (28, 1), (29, 1), (30, 5), (31, 1), (32, 2), (33, 1), (34, 1), (35, 2), (36, 1), (37, 1), (38, 5), (39, 2), (40, 1), (41, 45), (42, 2), (43, 1), (44, 1), (45, 5), (46, 1), (47, 1), (48, 1), (49, 1), (50, 1), (51, 1), (52, 1), (53, 1), (54, 1), (55, 1), (56, 20), (57, 2), (58, 1), (59, 1), (60, 1), (61, 2), (62, 2), (63, 1), (64, 8), (65, 1), (66, 2), (67, 1), (68, 1), (69, 1), (70, 2), (71, 9), (72, 1), (73, 1), (74, 2), (75, 1), (76, 1), (77, 1), (78, 2), (79, 33), (80, 1), (81, 1), (82, 1), (83, 1), (84, 1), (85, 3), (86, 2), (87, 2), (88, 5), (89, 1), (90, 1), (91, 1), (92, 1), (93, 3), (94, 2), (95, 2), (96, 4), (97, 1), (98, 2), (99, 1), (100, 1), (101, 1), (102, 3), (103, 1), (104, 1), (105, 1), (106, 10), (107, 11), (108, 1), (109, 1), (110, 5), (111, 4), (112, 2), (113, 1), (114, 1)], [(8, 1), (10, 3), (18, 1), (21, 2), (27, 1), (32, 8), (47, 1), (95, 1), (97, 3), (115, 1), (116, 1), (117, 45), (118, 1), (119, 9), (120, 1), (121, 44), (122, 2), (123, 2), (124, 1), (125, 2), (126, 1), (127, 1), (128, 1), (129, 1), (130, 7), (131, 4), (132, 1), (133, 2), (134, 2), (135, 2), (136, 1), (137, 16), (138, 17), (139, 2), (140, 1), (141, 7), (142, 2), (143, 1), (144, 1), (145, 2), (146, 1), (147, 4), (148, 5), (149, 1), (150, 1), (151, 11), (152, 1), (153, 1), (154, 2), (155, 1), (156, 9), (157, 1), (158, 1), (159, 1), (160, 3), (161, 1), (162, 1), (163, 7), (164, 16), (165, 1), (166, 1), (167, 3), (168, 1), (169, 29), (170, 2), (171, 8), (172, 1)]] \n",
      "\n",
      "First 2 texts in BOW format together with terms:\n",
      "\n",
      "[('accid', 0, 37), ('actual', 1, 1), ('addit', 2, 1), ('advertis', 3, 1), ('after', 4, 1), ('agenc', 5, 1), ('agricultur', 6, 1), ('airport', 7, 2), ('although', 8, 1), ('amsterdam', 9, 1), ('annual', 10, 4), ('anoth', 11, 1), ('around', 12, 1), ('atlant', 13, 1), ('aviat', 14, 15), ('brazil', 15, 1), ('broad', 16, 1), ('cargo', 17, 2), ('categori', 18, 12), ('civil', 19, 1), ('claim', 20, 1), ('close', 21, 2), ('column', 22, 1), ('commerci', 23, 6), ('compani', 24, 1), ('compris', 25, 1), ('connect', 26, 1), ('consider', 27, 2), ('consist', 28, 1), ('construct', 29, 1), ('death', 30, 5), ('denot', 31, 1), ('detail', 32, 2), ('dutch', 33, 1), ('earlier', 34, 1), ('egypt', 35, 2), ('elsewher', 36, 1), ('especi', 37, 1), ('european', 38, 5), ('everi', 39, 2), ('experienc', 40, 1), ('fatal', 41, 45), ('fewer', 42, 2), ('flight', 43, 1), ('fluctuat', 44, 1), ('french', 45, 5), ('further', 46, 1), ('german', 47, 1), ('given', 48, 1), ('global', 49, 1), ('highlight', 50, 1), ('incid', 51, 1), ('individu', 52, 1), ('industri', 53, 1), ('inform', 54, 1), ('injur', 55, 1), ('involv', 56, 20), ('irish', 57, 2), ('light', 58, 1), ('madrid', 59, 1), ('march', 60, 1), ('maximum', 61, 2), ('mention', 62, 2), ('mountain', 63, 1), ('occur', 64, 8), ('ocean', 65, 1), ('often', 66, 2), ('outsid', 67, 1), ('overview', 68, 1), ('particularli', 69, 1), ('passeng', 70, 2), ('person', 71, 9), ('phase', 72, 1), ('portug', 73, 1), ('previou', 74, 2), ('previous', 75, 1), ('proport', 76, 1), ('rather', 77, 1), ('regardless', 78, 2), ('regist', 79, 33), ('registr', 80, 1), ('relat', 81, 1), ('repres', 82, 1), ('respons', 83, 1), ('result', 84, 1), ('review', 85, 3), ('russia', 86, 2), ('russian', 87, 2), ('safeti', 88, 5), ('search', 89, 1), ('seriou', 90, 1), ('servic', 91, 1), ('similar', 92, 1), ('singl', 93, 3), ('slight', 94, 2), ('slovakia', 95, 2), ('small', 96, 4), ('smaller', 97, 1), ('south', 98, 2), ('spanish', 99, 1), ('special', 100, 1), ('specialis', 101, 1), ('specif', 102, 3), ('split', 103, 1), ('suburb', 104, 1), ('survei', 105, 1), ('territori', 106, 10), ('transport', 107, 11), ('twelv', 108, 1), ('ukrainian', 109, 1), ('under', 110, 5), ('union', 111, 4), ('victim', 112, 2), ('whether', 113, 1), ('world', 114, 1)]\n",
      "[('although', 8, 1), ('annual', 10, 3), ('categori', 18, 1), ('close', 21, 2), ('consider', 27, 1), ('detail', 32, 8), ('german', 47, 1), ('slovakia', 95, 1), ('smaller', 97, 3), ('absolut', 115, 1), ('achiev', 116, 1), ('asean', 117, 45), ('austria', 118, 1), ('balanc', 119, 9), ('behind', 120, 1), ('billion', 121, 44), ('brunei', 122, 2), ('cambodia', 123, 2), ('chemic', 124, 1), ('china', 125, 2), ('consequ', 126, 1), ('continu', 127, 1), ('covid', 128, 1), ('declin', 129, 1), ('deficit', 130, 7), ('destin', 131, 4), ('digit', 132, 1), ('domin', 133, 2), ('doubl', 134, 2), ('equival', 135, 2), ('estonia', 136, 1), ('eurostat', 137, 16), ('export', 138, 17), ('extra', 139, 2), ('fourth', 140, 1), ('growth', 141, 7), ('indonesia', 142, 2), ('japan', 143, 1), ('lithuania', 144, 1), ('machineri', 145, 2), ('mainli', 146, 1), ('malaysia', 147, 4), ('manufactur', 148, 5), ('materi', 149, 1), ('meant', 150, 1), ('million', 151, 11), ('modest', 152, 1), ('myanmar', 153, 1), ('origin', 154, 2), ('pandem', 155, 1), ('partner', 156, 9), ('philippin', 157, 1), ('place', 158, 1), ('point', 159, 1), ('primari', 160, 3), ('shown', 161, 1), ('significantli', 162, 1), ('singapor', 163, 7), ('sourc', 164, 16), ('surpass', 165, 1), ('surplu', 166, 1), ('thailand', 167, 3), ('therebi', 168, 1), ('trade', 169, 29), ('vehicl', 170, 2), ('vietnam', 171, 8), ('whose', 172, 1)]\n",
      "\n",
      "Total texts:  615\n",
      "\n",
      "Coherence Score:  0.5379211394679407\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lower_bound = 0.01\n",
    "upper_bound = 0.5\n",
    "\n",
    "from gensim import corpora\n",
    "\n",
    "#Create Dictionary\n",
    "id2word = corpora.Dictionary(texts) #Gensim creates a unique id for each word in the document. \n",
    "id2word.filter_extremes(no_below=lower_bound*len(texts), no_above=upper_bound)\n",
    "\n",
    "print(id2word,'\\n')\n",
    "\n",
    "corpus = [id2word.doc2bow(text) for text in texts] \n",
    "\n",
    "print('Firsτ 2 texts in BOW format:\\n')\n",
    "print(corpus[:2],'\\n')\n",
    "print('First 2 texts in BOW format together with terms:\\n')\n",
    "for i in range(2):\n",
    "    print([(id2word[term_id],term_id,freq) for (term_id,freq) in corpus[i]])\n",
    "\n",
    "print('\\nTotal texts: ',len(corpus))\n",
    "\n",
    "num_topics = 20\n",
    "passes = 50\n",
    "alpha= 'auto'\n",
    "iterations = 300 \n",
    "decay = 0.30 \n",
    "\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=num_topics, \n",
    "                                           random_state=100,\n",
    "                                           update_every=0,\n",
    "                                           passes=passes,\n",
    "                                           alpha=alpha,\n",
    "                                           iterations=iterations,\n",
    "                                           decay = decay,\n",
    "                                           per_word_topics=True,\n",
    "                                           eval_every=None)\n",
    "\n",
    "\n",
    "from gensim.models import CoherenceModel\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=texts, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Identification of the dominant topic per document.\n",
    "***\n",
    "In this section, we identify the dominant topic, its weight and its keywords, for each document. We define a dataframe _df_topic_sents_keywords_  in order to store these results. As it will be seen later, these are very useful as **complementary information to find and/or verify the interpretation of the topics**.\n",
    "\n",
    "In function _format_topics_sentences()_ below, the list _ldamodel[corpus]_ has one nested list element per text. Each nested list contains tuples of the form (topic, contribution). We sort the firts element of each nested list by descending contribution to find the dominant topic and then, we retrieve for this topic, the list _wp_ of tuples (word, probability) for the most probable words, using the function _ldamodel.show_topic()_. We join these words into a list and put the result in column 'Topic_Keywords' of the dataframe.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_topics_sentences(ldamodel=None, corpus=corpus):\n",
    "\n",
    "    sent_topics_df = pd.DataFrame()\n",
    "\n",
    "    # Get main topic in each document\n",
    "    for i, row_list in enumerate(ldamodel[corpus]): \n",
    "        row = row_list[0]\n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True) ## sort the nested list by descending contribution\n",
    "        # Get the Dominant topic, Perc Contribution and Keywords for each document\n",
    "        topic_num, prop_topic = row[0]\n",
    "        wp = ldamodel.show_topic(topic_num)\n",
    "        topic_keywords = \", \".join([word for word, prop in wp])\n",
    "        sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
    "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
    "\n",
    "    # Add original tokenized text and title to the end of the output\n",
    "    contents = pd.Series(texts)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, dat[['title','categories','abstract']]], axis=1)\n",
    "    return(sent_topics_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Text title</th>\n",
       "      <th>Text tokenized definition</th>\n",
       "      <th>Text categories</th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Perc_Contribution</th>\n",
       "      <th>Topic_Keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air safety statistics in the EU</td>\n",
       "      <td>[overview, fatal, transport, annual, safeti, r...</td>\n",
       "      <td>['Air', 'Passengers', 'Statistical article', '...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.5372</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASEAN-EU - international trade in goods statis...</td>\n",
       "      <td>[asean, countri, trade, partner, largest, trad...</td>\n",
       "      <td>['Non-EU countries', 'Trade in goods', 'Statis...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.9749</td>\n",
       "      <td>billion, export, trade, china, partner, millio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air pollution statistics - emission inventories</td>\n",
       "      <td>[gener, overview, pollut, human, health, envir...</td>\n",
       "      <td>['Air pollution', 'Environment', 'Health', 'He...</td>\n",
       "      <td>13</td>\n",
       "      <td>0.8452</td>\n",
       "      <td>water, european, develop, target, measur, sust...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Absences from work - quarterly statistics</td>\n",
       "      <td>[absenc, sharpli, increas, first, decreas, qua...</td>\n",
       "      <td>['Employment', 'Labour market', 'Statistical a...</td>\n",
       "      <td>19</td>\n",
       "      <td>0.7886</td>\n",
       "      <td>employ, young, peopl, women, point, labour, qu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Annual national accounts - evolution of the in...</td>\n",
       "      <td>[share, incom, compon, compens, employe, large...</td>\n",
       "      <td>['Authored article', 'National accounts (incl....</td>\n",
       "      <td>12</td>\n",
       "      <td>0.7341</td>\n",
       "      <td>expenditur, benefit, govern, financi, social, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Accommodation and food service statistics - NA...</td>\n",
       "      <td>[structur, profil, accommod, servic, sector, r...</td>\n",
       "      <td>['Services', 'Statistical article', 'Structura...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.7466</td>\n",
       "      <td>region, sector, economi, employ, person, finan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>Accidents at work statistics</td>\n",
       "      <td>[number, accid, there, million, fatal, accid, ...</td>\n",
       "      <td>['Accidents at work', 'Health', 'Health and sa...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9987</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Accidents at work - statistics on causes and c...</td>\n",
       "      <td>[workstat, accid, fatal, accid, million, fatal...</td>\n",
       "      <td>['Accidents at work', 'Health', 'Health and sa...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9929</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Accidents at work - statistics by economic act...</td>\n",
       "      <td>[develop, fatal, accid, there, million, fatal,...</td>\n",
       "      <td>['Accidents at work', 'Health', 'Health and sa...</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>Accidents and injuries statistics</td>\n",
       "      <td>[death, accid, injuri, assault, there, death, ...</td>\n",
       "      <td>['Health', 'Health status', 'Statistical artic...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.5906</td>\n",
       "      <td>death, inhabit, immigr, cancer, diseas, nativ,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>615 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Text title  \\\n",
       "0                      Air safety statistics in the EU   \n",
       "1    ASEAN-EU - international trade in goods statis...   \n",
       "2      Air pollution statistics - emission inventories   \n",
       "3            Absences from work - quarterly statistics   \n",
       "4    Annual national accounts - evolution of the in...   \n",
       "..                                                 ...   \n",
       "610  Accommodation and food service statistics - NA...   \n",
       "611                       Accidents at work statistics   \n",
       "612  Accidents at work - statistics on causes and c...   \n",
       "613  Accidents at work - statistics by economic act...   \n",
       "614                  Accidents and injuries statistics   \n",
       "\n",
       "                             Text tokenized definition  \\\n",
       "0    [overview, fatal, transport, annual, safeti, r...   \n",
       "1    [asean, countri, trade, partner, largest, trad...   \n",
       "2    [gener, overview, pollut, human, health, envir...   \n",
       "3    [absenc, sharpli, increas, first, decreas, qua...   \n",
       "4    [share, incom, compon, compens, employe, large...   \n",
       "..                                                 ...   \n",
       "610  [structur, profil, accommod, servic, sector, r...   \n",
       "611  [number, accid, there, million, fatal, accid, ...   \n",
       "612  [workstat, accid, fatal, accid, million, fatal...   \n",
       "613  [develop, fatal, accid, there, million, fatal,...   \n",
       "614  [death, accid, injuri, assault, there, death, ...   \n",
       "\n",
       "                                       Text categories  Dominant_Topic  \\\n",
       "0    ['Air', 'Passengers', 'Statistical article', '...               7   \n",
       "1    ['Non-EU countries', 'Trade in goods', 'Statis...               3   \n",
       "2    ['Air pollution', 'Environment', 'Health', 'He...              13   \n",
       "3    ['Employment', 'Labour market', 'Statistical a...              19   \n",
       "4    ['Authored article', 'National accounts (incl....              12   \n",
       "..                                                 ...             ...   \n",
       "610  ['Services', 'Statistical article', 'Structura...               5   \n",
       "611  ['Accidents at work', 'Health', 'Health and sa...               7   \n",
       "612  ['Accidents at work', 'Health', 'Health and sa...               7   \n",
       "613  ['Accidents at work', 'Health', 'Health and sa...               7   \n",
       "614  ['Health', 'Health status', 'Statistical artic...              16   \n",
       "\n",
       "     Perc_Contribution                                     Topic_Keywords  \n",
       "0               0.5372  activ, accid, servic, fatal, sector, emploi, p...  \n",
       "1               0.9749  billion, export, trade, china, partner, millio...  \n",
       "2               0.8452  water, european, develop, target, measur, sust...  \n",
       "3               0.7886  employ, young, peopl, women, point, labour, qu...  \n",
       "4               0.7341  expenditur, benefit, govern, financi, social, ...  \n",
       "..                 ...                                                ...  \n",
       "610             0.7466  region, sector, economi, employ, person, finan...  \n",
       "611             0.9987  activ, accid, servic, fatal, sector, emploi, p...  \n",
       "612             0.9929  activ, accid, servic, fatal, sector, emploi, p...  \n",
       "613             0.9993  activ, accid, servic, fatal, sector, emploi, p...  \n",
       "614             0.5906  death, inhabit, immigr, cancer, diseas, nativ,...  \n",
       "\n",
       "[615 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, corpus=corpus)\n",
    "df_topic_sents_keywords.rename(columns = {0:'Text tokenized definition'}, inplace = True)\n",
    "df_topic_sents_keywords.rename(columns = {'title':'Text title','categories':'Text categories','abstract':'Text abstract'}, inplace = True)\n",
    "df_topic_sents_keywords = df_topic_sents_keywords[['Text title','Text tokenized definition','Text categories','Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']]\n",
    "df_topic_sents_keywords['Dominant_Topic'] = df_topic_sents_keywords['Dominant_Topic'].astype(int)\n",
    "df_topic_sents_keywords\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Most representative document for each topic.\n",
    "***\n",
    "\n",
    "Here, we are searching for the reverse, i.e. we want to find the document that best represents a given topic.We create the dataframe _sent_topics_sorteddf_mallet_ to store the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic_Num</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Topic Keywords</th>\n",
       "      <th>Text title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.9114</td>\n",
       "      <td>popul, peopl, older, birth, project, chang, wo...</td>\n",
       "      <td>Population projections in the EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.9971</td>\n",
       "      <td>product, price, million, quarter, agricultur, ...</td>\n",
       "      <td>Farms and farmland in the European Union - sta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.9977</td>\n",
       "      <td>energi, consumpt, product, electr, emiss, rene...</td>\n",
       "      <td>Production of lignite in the EU - statistics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.9976</td>\n",
       "      <td>billion, export, trade, china, partner, millio...</td>\n",
       "      <td>Norway-EU - international trade in goods stati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.8063</td>\n",
       "      <td>household, cultur, internet, adult, onlin, poi...</td>\n",
       "      <td>Digital economy and society statistics - house...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.9989</td>\n",
       "      <td>region, sector, economi, employ, person, finan...</td>\n",
       "      <td>Water supply, sewerage, waste management and r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.9493</td>\n",
       "      <td>person, group, emploi, health, children, women...</td>\n",
       "      <td>Main place of work and commuting time - statis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "      <td>Accidents at work - statistics by economic act...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.8624</td>\n",
       "      <td>peopl, activ, popul, particip, adult, famili, ...</td>\n",
       "      <td>Social participation and integration statistics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.9915</td>\n",
       "      <td>citizen, foreign, resid, nation, permit, first...</td>\n",
       "      <td>Asylum quarterly report</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.9937</td>\n",
       "      <td>servic, tourist, night, tourism, spent, accomm...</td>\n",
       "      <td>Tourism statistics - winter season occupancy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.9994</td>\n",
       "      <td>tertiari, student, secondari, graduat, pupil, ...</td>\n",
       "      <td>Learning mobility statistics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.9792</td>\n",
       "      <td>expenditur, benefit, govern, financi, social, ...</td>\n",
       "      <td>Government finance statistics - quarterly data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.9990</td>\n",
       "      <td>water, european, develop, target, measur, sust...</td>\n",
       "      <td>SDG 15 - Life on land</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.8798</td>\n",
       "      <td>enterpris, economi, pension, turnov, invest, i...</td>\n",
       "      <td>European Neighbourhood Policy - East - economi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.7435</td>\n",
       "      <td>unemploy, candid, potenti, turkei, outsid, lab...</td>\n",
       "      <td>Labour market slack – annual statistics on unm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.9990</td>\n",
       "      <td>death, inhabit, immigr, cancer, diseas, nativ,...</td>\n",
       "      <td>Cancer statistics - specific cancers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.9985</td>\n",
       "      <td>transport, passeng, freight, million, regist, ...</td>\n",
       "      <td>Maritime freight and vessels statistics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.9991</td>\n",
       "      <td>incom, household, poverti, peopl, social, popu...</td>\n",
       "      <td>Quality of life indicators - economic security...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>employ, young, peopl, women, point, labour, qu...</td>\n",
       "      <td>Employment - quarterly statistics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic_Num  Topic_Perc_Contrib  \\\n",
       "0           0              0.9114   \n",
       "1           1              0.9971   \n",
       "2           2              0.9977   \n",
       "3           3              0.9976   \n",
       "4           4              0.8063   \n",
       "5           5              0.9989   \n",
       "6           6              0.9493   \n",
       "7           7              0.9993   \n",
       "8           8              0.8624   \n",
       "9           9              0.9915   \n",
       "10         10              0.9937   \n",
       "11         11              0.9994   \n",
       "12         12              0.9792   \n",
       "13         13              0.9990   \n",
       "14         14              0.8798   \n",
       "15         15              0.7435   \n",
       "16         16              0.9990   \n",
       "17         17              0.9985   \n",
       "18         18              0.9991   \n",
       "19         19              0.9978   \n",
       "\n",
       "                                       Topic Keywords  \\\n",
       "0   popul, peopl, older, birth, project, chang, wo...   \n",
       "1   product, price, million, quarter, agricultur, ...   \n",
       "2   energi, consumpt, product, electr, emiss, rene...   \n",
       "3   billion, export, trade, china, partner, millio...   \n",
       "4   household, cultur, internet, adult, onlin, poi...   \n",
       "5   region, sector, economi, employ, person, finan...   \n",
       "6   person, group, emploi, health, children, women...   \n",
       "7   activ, accid, servic, fatal, sector, emploi, p...   \n",
       "8   peopl, activ, popul, particip, adult, famili, ...   \n",
       "9   citizen, foreign, resid, nation, permit, first...   \n",
       "10  servic, tourist, night, tourism, spent, accomm...   \n",
       "11  tertiari, student, secondari, graduat, pupil, ...   \n",
       "12  expenditur, benefit, govern, financi, social, ...   \n",
       "13  water, european, develop, target, measur, sust...   \n",
       "14  enterpris, economi, pension, turnov, invest, i...   \n",
       "15  unemploy, candid, potenti, turkei, outsid, lab...   \n",
       "16  death, inhabit, immigr, cancer, diseas, nativ,...   \n",
       "17  transport, passeng, freight, million, regist, ...   \n",
       "18  incom, household, poverti, peopl, social, popu...   \n",
       "19  employ, young, peopl, women, point, labour, qu...   \n",
       "\n",
       "                                           Text title  \n",
       "0                    Population projections in the EU  \n",
       "1   Farms and farmland in the European Union - sta...  \n",
       "2        Production of lignite in the EU - statistics  \n",
       "3   Norway-EU - international trade in goods stati...  \n",
       "4   Digital economy and society statistics - house...  \n",
       "5   Water supply, sewerage, waste management and r...  \n",
       "6   Main place of work and commuting time - statis...  \n",
       "7   Accidents at work - statistics by economic act...  \n",
       "8     Social participation and integration statistics  \n",
       "9                             Asylum quarterly report  \n",
       "10       Tourism statistics - winter season occupancy  \n",
       "11                       Learning mobility statistics  \n",
       "12     Government finance statistics - quarterly data  \n",
       "13                              SDG 15 - Life on land  \n",
       "14  European Neighbourhood Policy - East - economi...  \n",
       "15  Labour market slack – annual statistics on unm...  \n",
       "16               Cancer statistics - specific cancers  \n",
       "17            Maritime freight and vessels statistics  \n",
       "18  Quality of life indicators - economic security...  \n",
       "19                  Employment - quarterly statistics  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group top 5 sentences under each topic\n",
    "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
    "\n",
    "sent_topics_outdf_grpd = df_topic_sents_keywords[['Text title','Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']].groupby('Dominant_Topic')\n",
    "\n",
    "for i, grp in sent_topics_outdf_grpd:\n",
    "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
    "                                             grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n",
    "                                             axis=0)\n",
    "\n",
    "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
    "\n",
    "sent_topics_sorteddf_mallet.columns = ['Text title','Topic_Num', 'Topic_Perc_Contrib', 'Topic Keywords' ]\n",
    "sent_topics_sorteddf_mallet = sent_topics_sorteddf_mallet[['Topic_Num', 'Topic_Perc_Contrib', 'Topic Keywords','Text title']]\n",
    "\n",
    "sent_topics_sorteddf_mallet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. The topics as a mix of keywords.\n",
    "***\n",
    "\n",
    "The above LDA model is built with 20 different topics where each topic is a combination of keywords and each keyword contributes with a certain weight to the topic.  This mix of keywords (limited to the top 10 keywords) is shown below, for each topic.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.120*\"popul\" + 0.053*\"peopl\" + 0.049*\"older\" + 0.031*\"birth\" + '\n",
      "  '0.027*\"project\" + 0.024*\"chang\" + 0.023*\"women\" + 0.022*\"expect\" + '\n",
      "  '0.013*\"sport\" + 0.013*\"person\"'),\n",
      " (1,\n",
      "  '0.063*\"product\" + 0.031*\"price\" + 0.028*\"million\" + 0.015*\"quarter\" + '\n",
      "  '0.015*\"agricultur\" + 0.014*\"industri\" + 0.014*\"produc\" + 0.014*\"crisi\" + '\n",
      "  '0.011*\"develop\" + 0.011*\"declin\"'),\n",
      " (2,\n",
      "  '0.098*\"energi\" + 0.057*\"consumpt\" + 0.057*\"product\" + 0.034*\"electr\" + '\n",
      "  '0.033*\"emiss\" + 0.017*\"renew\" + 0.016*\"gross\" + 0.016*\"final\" + '\n",
      "  '0.015*\"primari\" + 0.014*\"environment\"'),\n",
      " (3,\n",
      "  '0.109*\"billion\" + 0.105*\"export\" + 0.094*\"trade\" + 0.027*\"china\" + '\n",
      "  '0.026*\"partner\" + 0.024*\"million\" + 0.022*\"product\" + 0.022*\"manufactur\" + '\n",
      "  '0.020*\"world\" + 0.018*\"extra\"'),\n",
      " (4,\n",
      "  '0.079*\"household\" + 0.061*\"cultur\" + 0.059*\"internet\" + 0.032*\"adult\" + '\n",
      "  '0.019*\"onlin\" + 0.017*\"point\" + 0.017*\"servic\" + 0.017*\"comput\" + '\n",
      "  '0.016*\"proport\" + 0.015*\"access\"'),\n",
      " (5,\n",
      "  '0.148*\"region\" + 0.035*\"sector\" + 0.024*\"economi\" + 0.023*\"employ\" + '\n",
      "  '0.023*\"person\" + 0.020*\"financi\" + 0.018*\"emploi\" + 0.018*\"activ\" + '\n",
      "  '0.018*\"capit\" + 0.017*\"labour\"'),\n",
      " (6,\n",
      "  '0.062*\"person\" + 0.034*\"group\" + 0.032*\"emploi\" + 0.028*\"health\" + '\n",
      "  '0.024*\"children\" + 0.020*\"women\" + 0.017*\"peopl\" + 0.012*\"unmet\" + '\n",
      "  '0.012*\"limit\" + 0.011*\"reason\"'),\n",
      " (7,\n",
      "  '0.102*\"activ\" + 0.058*\"accid\" + 0.046*\"servic\" + 0.038*\"fatal\" + '\n",
      "  '0.018*\"sector\" + 0.017*\"emploi\" + 0.016*\"person\" + 0.015*\"construct\" + '\n",
      "  '0.015*\"inform\" + 0.014*\"manufactur\"'),\n",
      " (8,\n",
      "  '0.077*\"peopl\" + 0.054*\"activ\" + 0.047*\"popul\" + 0.047*\"particip\" + '\n",
      "  '0.044*\"adult\" + 0.028*\"famili\" + 0.025*\"least\" + 0.018*\"incom\" + '\n",
      "  '0.017*\"women\" + 0.017*\"formal\"'),\n",
      " (9,\n",
      "  '0.099*\"citizen\" + 0.058*\"foreign\" + 0.039*\"resid\" + 0.028*\"nation\" + '\n",
      "  '0.024*\"permit\" + 0.024*\"first\" + 0.024*\"citizenship\" + 0.020*\"applic\" + '\n",
      "  '0.016*\"languag\" + 0.012*\"reliabl\"'),\n",
      " (10,\n",
      "  '0.110*\"servic\" + 0.038*\"tourist\" + 0.037*\"night\" + 0.037*\"tourism\" + '\n",
      "  '0.036*\"spent\" + 0.032*\"accommod\" + 0.031*\"resid\" + 0.018*\"travel\" + '\n",
      "  '0.015*\"domest\" + 0.015*\"million\"'),\n",
      " (11,\n",
      "  '0.050*\"tertiari\" + 0.040*\"student\" + 0.039*\"secondari\" + 0.034*\"graduat\" + '\n",
      "  '0.017*\"pupil\" + 0.016*\"studi\" + 0.016*\"abroad\" + 0.016*\"recent\" + '\n",
      "  '0.016*\"proport\" + 0.015*\"attain\"'),\n",
      " (12,\n",
      "  '0.070*\"expenditur\" + 0.033*\"benefit\" + 0.031*\"govern\" + 0.028*\"financi\" + '\n",
      "  '0.025*\"social\" + 0.020*\"ratio\" + 0.016*\"incom\" + 0.014*\"gross\" + '\n",
      "  '0.014*\"asset\" + 0.013*\"revenu\"'),\n",
      " (13,\n",
      "  '0.016*\"water\" + 0.015*\"european\" + 0.013*\"develop\" + 0.013*\"target\" + '\n",
      "  '0.011*\"measur\" + 0.010*\"sustain\" + 0.010*\"progress\" + 0.009*\"toward\" + '\n",
      "  '0.009*\"environment\" + 0.009*\"trend\"'),\n",
      " (14,\n",
      "  '0.078*\"enterpris\" + 0.021*\"economi\" + 0.014*\"pension\" + 0.013*\"turnov\" + '\n",
      "  '0.013*\"invest\" + 0.012*\"intern\" + 0.012*\"econom\" + 0.012*\"ukrain\" + '\n",
      "  '0.011*\"belaru\" + 0.011*\"financi\"'),\n",
      " (15,\n",
      "  '0.070*\"unemploy\" + 0.055*\"candid\" + 0.029*\"potenti\" + 0.028*\"turkei\" + '\n",
      "  '0.025*\"outsid\" + 0.025*\"labour\" + 0.021*\"serbia\" + 0.020*\"south\" + '\n",
      "  '0.019*\"macedonia\" + 0.019*\"north\"'),\n",
      " (16,\n",
      "  '0.063*\"death\" + 0.051*\"inhabit\" + 0.042*\"immigr\" + 0.025*\"cancer\" + '\n",
      "  '0.024*\"diseas\" + 0.022*\"nativ\" + 0.019*\"origin\" + 0.019*\"women\" + '\n",
      "  '0.018*\"second\" + 0.018*\"patient\"'),\n",
      " (17,\n",
      "  '0.112*\"transport\" + 0.041*\"passeng\" + 0.028*\"freight\" + 0.022*\"million\" + '\n",
      "  '0.018*\"regist\" + 0.018*\"perform\" + 0.018*\"kilometr\" + 0.015*\"vehicl\" + '\n",
      "  '0.014*\"quarter\" + 0.014*\"intern\"'),\n",
      " (18,\n",
      "  '0.059*\"incom\" + 0.049*\"household\" + 0.045*\"poverti\" + 0.040*\"peopl\" + '\n",
      "  '0.028*\"social\" + 0.028*\"popul\" + 0.024*\"point\" + 0.022*\"children\" + '\n",
      "  '0.016*\"exclus\" + 0.012*\"depriv\"'),\n",
      " (19,\n",
      "  '0.090*\"employ\" + 0.085*\"young\" + 0.052*\"peopl\" + 0.038*\"women\" + '\n",
      "  '0.029*\"point\" + 0.027*\"labour\" + 0.026*\"quarter\" + 0.026*\"emploi\" + '\n",
      "  '0.023*\"employe\" + 0.021*\"gender\"')]\n"
     ]
    }
   ],
   "source": [
    "pprint(lda_model.print_topics())\n",
    "\n",
    "## equivalently:\n",
    "##for i in range(num_topics):\n",
    "##    print([(id2word[term_id],prob) for (term_id,prob) in lda_model.get_topic_terms(i,topn=10)],'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Dominant topics.\n",
    "***\n",
    "\n",
    "\n",
    "To better understand the volume and distribution of topics, we construct the dataframe _df_dominant_topics_ with relevant information.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Keywords</th>\n",
       "      <th>Num_Documents</th>\n",
       "      <th>Perc_Documents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>billion, export, trade, china, partner, millio...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>water, european, develop, target, measur, sust...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>employ, young, peopl, women, point, labour, qu...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>expenditur, benefit, govern, financi, social, ...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>5</td>\n",
       "      <td>region, sector, economi, employ, person, finan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>7</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>7</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>7</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>16</td>\n",
       "      <td>death, inhabit, immigr, cancer, diseas, nativ,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>615 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dominant_Topic                                     Topic_Keywords  \\\n",
       "0                 7  activ, accid, servic, fatal, sector, emploi, p...   \n",
       "1                 3  billion, export, trade, china, partner, millio...   \n",
       "2                13  water, european, develop, target, measur, sust...   \n",
       "3                19  employ, young, peopl, women, point, labour, qu...   \n",
       "4                12  expenditur, benefit, govern, financi, social, ...   \n",
       "..              ...                                                ...   \n",
       "610               5  region, sector, economi, employ, person, finan...   \n",
       "611               7  activ, accid, servic, fatal, sector, emploi, p...   \n",
       "612               7  activ, accid, servic, fatal, sector, emploi, p...   \n",
       "613               7  activ, accid, servic, fatal, sector, emploi, p...   \n",
       "614              16  death, inhabit, immigr, cancer, diseas, nativ,...   \n",
       "\n",
       "     Num_Documents  Perc_Documents  \n",
       "0             18.0          0.0293  \n",
       "1             67.0          0.1089  \n",
       "2             37.0          0.0602  \n",
       "3             54.0          0.0878  \n",
       "4             17.0          0.0276  \n",
       "..             ...             ...  \n",
       "610            NaN             NaN  \n",
       "611            NaN             NaN  \n",
       "612            NaN             NaN  \n",
       "613            NaN             NaN  \n",
       "614            NaN             NaN  \n",
       "\n",
       "[615 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Topic distribution across documents\n",
    "\n",
    "# Number of Documents for Each Topic\n",
    "topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n",
    "\n",
    "# Percentage of Documents for Each Topic\n",
    "topic_contribution = round(topic_counts/topic_counts.sum(), 4)\n",
    "\n",
    "# Topic Number and Keywords\n",
    "topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\n",
    "\n",
    "# Concatenate Column wise\n",
    "df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\n",
    "\n",
    "# Change Column names\n",
    "df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\n",
    "\n",
    "# Show\n",
    "df_dominant_topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Visualization of the topics.\n",
    "***\n",
    "\n",
    "To visualize the fitted LDA model we use the _pyLDAvis_ package. This is the Python porting of the R package _LDAvis_, see [LDAvis vignette](https://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf) for details and Chuang, Jason, Manning, Christopher D., and Heer, Jeffrey (2012). Termite: Visualization Techniques for Assessing Textual Topic Models, *Advanced Visual Interfaces* for the theory behind the visualization algorithm. The paper is available [here](https://dl.acm.org/doi/pdf/10.1145/2254556.2254572?casa_token=q2BavKP415QAAAAA:MhcYHzz4PJpC7dNkkm12GL-ohQRUXBgumPJ9l1t_5n3M4qVE1kdDqKGfPmtnR7qbale_ukS-2nJs). \n",
    "\n",
    "Note that the parameter _sort_topics=False_ in the call to function _pyLDAvis.gensim_models.prepare()_ is necessary, otherwise the numbering of the topics is different than the one in the rest of the analysis. Also, **the numbering of the topics start from zero**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el998824653611404165884686479\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el998824653611404165884686479_data = {\"mdsDat\": {\"x\": [0.11160415416192843, -0.1634898040466864, -0.21600006062125543, -0.19839918224908648, 0.10344958389573032, -0.05884118498893688, 0.16326337484999723, -0.026928269405444995, 0.23863703771040512, 0.06596408546272561, -0.12558029796624304, 0.10040062224428639, -0.07938879192019566, -0.10900355141333778, -0.10063218898419753, 0.04094475411067271, 0.11826443785469355, -0.13918332289744345, 0.14449006331323644, 0.13042854088915243], \"y\": [-0.03517050005564586, 0.006107332330601688, 0.06021938697129994, -0.32422170887386303, 0.04082807796347688, 0.06804049952482236, 0.008039223960627978, 0.11050459465518458, 0.0016226974527175067, -0.061204351974673825, 0.028626485909521004, -0.05856800101920763, 0.09630581314168277, 0.15502990689146112, 0.11247838278419457, -0.028739631650713186, -0.08417662432805298, -0.09944896102832924, 0.05984385431395278, -0.056116476969057916], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [4.632118731072242, 7.135528048027318, 3.91816747671946, 6.583110872515868, 3.0025105773614675, 5.779392002253111, 5.3226208801557915, 3.651397912541807, 3.7227902989114354, 3.7143573640850525, 3.2655431336620975, 3.9331814166110632, 6.775399037787588, 7.492369654524018, 5.32261240858811, 4.044869756238132, 5.171280813518223, 3.6931292206420197, 7.391347648948614, 5.448272745836561]}, \"tinfo\": {\"Term\": [\"region\", \"export\", \"billion\", \"popul\", \"trade\", \"peopl\", \"activ\", \"household\", \"servic\", \"product\", \"transport\", \"employ\", \"energi\", \"young\", \"expenditur\", \"citizen\", \"enterpris\", \"incom\", \"death\", \"unemploy\", \"poverti\", \"person\", \"adult\", \"women\", \"emploi\", \"consumpt\", \"foreign\", \"candid\", \"accid\", \"inhabit\", \"longev\", \"woman\", \"boomer\", \"project\", \"fertil\", \"birth\", \"agglomer\", 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\"machineri\", \"malaysia\", \"thailand\", \"export\", \"korea\", \"portfolio\", \"mexico\", \"trader\", \"canada\", \"indonesia\", \"china\", \"myanmar\", \"philippin\", \"brunei\", \"lubric\", \"surplus\", \"brazil\", \"partner\", \"bilater\", \"billion\", \"africa\", \"extra\", \"trade\", \"australia\", \"india\", \"arabia\", \"saudi\", \"japan\", \"surplu\", \"russia\", \"world\", \"deficit\", \"intra\", \"manufactur\", \"chemic\", \"balanc\", \"kingdom\", \"switzerland\", \"vehicl\", \"million\", \"product\", \"south\", \"shown\", \"ratio\", \"cover\", \"group\", \"internet\", \"broadband\", \"onlin\", \"commerc\", \"newspap\", \"softwar\", \"subscript\", \"phone\", \"music\", \"tablet\", \"video\", \"devic\", \"websit\", \"cultur\", \"bought\", \"media\", \"opinion\", \"telephon\", \"stream\", \"custom\", \"messag\", \"author\", \"advertis\", \"broadcast\", \"comput\", \"strengthen\", \"membership\", \"televis\", \"accessori\", \"access\", \"semest\", \"household\", \"purchas\", 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\"million\", \"tourist\", \"tourism\", \"outbound\", \"night\", \"hotel\", \"inbound\", \"visitor\", \"overnight\", \"intellectu\", \"august\", \"accommod\", \"spent\", \"travel\", \"offshor\", \"dispers\", \"holidai\", \"arriv\", \"debit\", \"season\", \"america\", \"slowest\", \"pleasur\", \"summer\", \"destin\", \"establish\", \"septemb\", \"winter\", \"servic\", \"contin\", \"coastal\", \"resid\", \"domest\", \"beverag\", \"place\", \"month\", \"nearli\", \"european\", \"million\", \"expens\", \"turnov\", \"transport\", \"statist\", \"similar\", \"industri\", \"expenditur\", \"intern\", \"quarter\", \"graduat\", \"vocat\", \"pupil\", \"teacher\", \"leaver\", \"bachelor\", \"master\", \"teach\", \"student\", \"successfulli\", \"scienc\", \"academ\", \"childhood\", \"cours\", \"tertiari\", \"enrol\", \"compet\", \"secondari\", \"learn\", \"upper\", \"school\", \"mathemat\", \"field\", \"programm\", \"compulsori\", \"studi\", \"techniqu\", \"benchmark\", \"headlin\", \"abroad\", 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\"agenda\", \"biodivers\", \"ecosystem\", \"symbol\", \"inciner\", \"nutrient\", \"landfil\", \"solut\", \"forest\", \"river\", \"circular\", \"decoupl\", \"pollut\", \"parliament\", \"ammonia\", \"sustain\", \"paper\", \"settlement\", \"propos\", \"monitor\", \"inventori\", \"acidif\", \"eutroph\", \"unfavour\", \"progress\", \"speci\", \"commiss\", \"climat\", \"track\", \"water\", \"toward\", \"strategi\", \"target\", \"improv\", \"assess\", \"plastic\", \"qualiti\", \"environ\", \"environment\", \"european\", \"resourc\", \"manag\", \"polici\", \"trend\", \"object\", \"develop\", \"measur\", \"europ\", \"treatment\", \"reduc\", \"cover\", \"econom\", \"system\", \"estim\", \"agricultur\", \"protect\", \"relat\", \"under\", \"chang\", \"provid\", \"azerbaijan\", \"georgia\", \"armenia\", \"moldova\", \"investor\", \"bangladesh\", \"penetr\", \"belaru\", \"pakistan\", \"kazakhstan\", \"feder\", \"multin\", \"uninterrupt\", \"asian\", \"globalis\", \"depreci\", \"japanes\", \"outsourc\", \"ultim\", \"control\", \"outward\", \"enterpris\", \"internation\", \"ukrain\", \"underestim\", \"subdu\", \"inward\", \"zealand\", \"reloc\", \"turnov\", \"exchang\", \"currenc\", \"pension\", \"invest\", \"global\", \"economi\", \"intern\", \"sourc\", \"stock\", \"class\", \"small\", \"econom\", \"growth\", \"develop\", \"foreign\", \"intens\", \"financi\", \"consider\", \"inform\", \"receiv\", \"point\", \"provid\", \"sector\", \"ratio\", \"servic\", \"palestin\", \"youth\", \"kosovo\", \"jordan\", \"lebanon\", \"israel\", \"herzegovina\", \"bosnia\", \"candid\", \"tunisia\", \"unemploy\", \"algeria\", \"albania\", \"morocco\", \"montenegro\", \"serbia\", \"egypt\", \"macedonia\", \"neither\", \"potenti\", \"trebl\", \"unemploi\", \"immedi\", \"jobless\", \"extend\", \"north\", \"abbrevi\", \"turkei\", \"outsid\", \"turkish\", \"south\", \"nativ\", \"elsewher\", \"labour\", \"market\", \"person\", \"popul\", \"inhabit\", \"develop\", \"point\", \"econom\", \"cancer\", \"diseas\", \"physician\", \"respiratori\", \"hospit\", \"circulatori\", \"breast\", \"discharg\", \"neoplasm\", \"blood\", \"death\", \"diagnosi\", \"disord\", \"patient\", \"standardis\", \"immigr\", \"background\", \"intent\", \"diagnos\", \"heart\", \"prematur\", \"cardiovascular\", \"surgic\", \"mortal\", \"infant\", \"licens\", \"treat\", \"procedur\", \"medic\", \"behaviour\", \"inhabit\", \"origin\", \"nativ\", \"length\", \"elderli\", \"second\", \"migrat\", \"first\", \"women\", \"femal\", \"system\", \"health\", \"portug\", \"group\", \"popul\", \"proport\", \"lithuania\", \"latvia\", \"peopl\", \"hungari\", \"passeng\", \"freight\", \"waterwai\", \"motorwai\", \"motoris\", \"cargo\", \"airport\", \"unload\", \"kilometr\", \"antwerpen\", \"rotterdam\", \"maritim\", \"registr\", \"railwai\", \"tonnag\", \"liquefi\", \"lorri\", \"vessel\", \"coach\", \"handl\", \"danger\", \"amsterdam\", \"transport\", \"liquid\", \"valencia\", \"flight\", \"traffic\", \"hamburg\", \"fleet\", 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\"region\", \"region\", \"region\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"regist\", \"registr\", \"registr\", \"rehabilit\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"relat\", \"reliabl\", \"reliabl\", \"reliabl\", \"reliabl\", \"reliant\", \"reliant\", \"reliant\", \"reliev\", \"reliev\", \"reloc\", \"reloc\", \"reloc\", \"reloc\", \"remedi\", \"remedi\", \"renew\", \"renew\", \"renew\", \"renew\", \"renew\", \"repeat\", \"repeat\", \"repeat\", \"repeat\", \"repeat\", \"repeat\", \"repeat\", \"repeat\", \"repeat\", \"repeat\", \"repeat\", \"reproduc\", \"reproduc\", \"reproduc\", \"resid\", \"resid\", \"resid\", \"resid\", \"resid\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"resourc\", \"respiratori\", \"respond\", \"respond\", \"respond\", \"respond\", \"respond\", \"respond\", \"respond\", \"retail\", \"retail\", \"retail\", \"retail\", \"retail\", \"retail\", \"retir\", \"retir\", \"retir\", \"retir\", \"retir\", \"retir\", \"retir\", \"retir\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"revenu\", \"revenu\", \"revenu\", \"richest\", \"river\", \"river\", \"romanian\", \"romanian\", \"romanian\", \"rotterdam\", \"rotterdam\", \"russia\", \"russia\", \"russia\", \"russia\", \"russia\", \"russia\", \"salari\", \"salari\", \"salari\", \"salari\", \"sanitari\", \"sanitari\", \"satisfact\", \"satisfact\", \"saudi\", \"saudi\", \"scheme\", \"scheme\", \"scheme\", \"school\", \"school\", \"school\", \"school\", \"school\", \"scienc\", \"scienc\", \"search\", \"search\", \"search\", \"search\", \"season\", \"season\", \"season\", \"season\", \"season\", \"season\", \"season\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"secondari\", \"secondari\", \"secondari\", \"secondari\", \"secondari\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"section\", \"sector\", \"sector\", \"sector\", \"sector\", \"sector\", \"sector\", \"sector\", \"sector\", \"sector\", \"sector\", \"secur\", \"secur\", \"secur\", \"secur\", \"secur\", \"secur\", \"secur\", \"seeker\", \"seeker\", \"semest\", \"semest\", \"semest\", \"senior\", \"senior\", \"septemb\", \"septemb\", \"septemb\", \"septemb\", \"serbia\", \"serbia\", \"serbia\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"servic\", \"settlement\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"sever\", \"severozapaden\", \"sewerag\", \"sewerag\", \"shade\", \"shade\", \"shade\", \"sharpest\", \"sharpest\", \"sharpest\", \"sheep\", \"short\", \"short\", \"short\", \"short\", \"short\", \"short\", \"short\", \"short\", \"short\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"shown\", \"sicilia\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"similar\", \"singapor\", \"singl\", \"singl\", \"singl\", \"singl\", \"singl\", \"singl\", \"singl\", \"singl\", \"singl\", \"singl\", \"singl\", \"singl\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"situat\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovakia\", \"slovenian\", \"slovenian\", \"slovenian\", \"slovenian\", \"slowest\", \"slowest\", \"slowest\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"smoke\", \"smoke\", \"smoke\", \"smoke\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"socio\", \"socio\", \"socio\", \"socio\", \"socio\", \"softwar\", \"softwar\", \"solar\", \"solid\", \"solid\", \"solid\", \"solut\", \"someon\", \"someon\", \"someon\", \"someon\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"sourc\", \"south\", \"south\", \"south\", \"south\", \"south\", \"south\", \"south\", \"southern\", \"southern\", \"southern\", \"southern\", \"southern\", \"southern\", \"southern\", \"spanish\", \"spanish\", \"spanish\", \"spanish\", \"spanish\", \"spanish\", \"speci\", \"speci\", \"specialis\", \"specialis\", \"specialis\", \"specialis\", \"specialis\", \"specialis\", \"specialis\", \"specialis\", \"specialis\", \"specialist\", \"specialist\", \"specialist\", \"specialist\", \"spent\", \"spent\", \"spent\", \"spent\", \"spent\", \"spent\", \"spent\", \"sport\", \"sport\", \"sport\", \"sport\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"standardis\", \"standardis\", \"standardis\", \"standardis\", \"standardis\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"statist\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"storag\", \"storag\", \"storag\", \"storag\", \"strategi\", \"strategi\", \"strategi\", \"stream\", \"stream\", \"stream\", \"stream\", \"street\", \"strengthen\", \"strengthen\", \"strengthen\", \"strict\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"structur\", \"student\", \"student\", \"student\", \"student\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"studi\", \"stuttgart\", \"subdu\", \"subdu\", \"subdu\", \"subdu\", \"subgroup\", \"subgroup\", \"subgroup\", \"subpopul\", \"subpopul\", \"subpopul\", \"subpopul\", \"subpopul\", \"subscript\", \"subscript\", \"subsector\", \"subsector\", \"subsidi\", \"subsidi\", \"subsidi\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"substanti\", \"successfulli\", \"summer\", \"summer\", \"summer\", \"suppli\", \"suppli\", \"suppli\", \"suppli\", \"suppli\", \"suppli\", \"supplier\", \"supplier\", \"supplier\", \"supplier\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"surgic\", \"surgic\", \"surplu\", \"surplu\", \"surplu\", \"surplu\", \"surplus\", \"surplus\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"survei\", \"surviv\", \"surviv\", \"surviv\", \"surviv\", \"survivor\", \"survivor\", \"survivor\", \"sustain\", \"sustain\", \"sustain\", \"sustain\", \"sustain\", \"sustain\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"switzerland\", \"symbol\", \"syrian\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"tablet\", \"target\", \"target\", \"target\", \"target\", \"target\", \"target\", \"taxat\", \"taxat\", \"taxat\", \"taxat\", \"teach\", \"teacher\", \"techniqu\", \"techniqu\", \"techniqu\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"technolog\", \"telephon\", \"telephon\", \"televis\", \"televis\", \"televis\", \"televis\", \"temporari\", \"temporari\", \"temporari\", \"temporari\", \"tertiari\", \"tertiari\", \"tertiari\", \"tertiari\", \"thailand\", \"thing\", \"thing\", \"thing\", \"thing\", \"thought\", \"thought\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"thousand\", \"threshold\", \"threshold\", \"threshold\", \"threshold\", \"threshold\", \"threshold\", \"threshold\", \"tobacco\", \"tobacco\", \"tobacco\", \"tobacco\", \"tobacco\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"togeth\", \"toilet\", \"toilet\", \"tonnag\", \"tonnag\", \"tourism\", \"tourist\", \"toward\", \"toward\", \"toward\", \"toward\", \"toward\", \"toward\", \"toward\", \"toward\", \"toward\", \"toward\", \"toward\", \"toward\", \"track\", \"track\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trader\", \"trader\", \"traffic\", \"traffic\", \"train\", \"train\", \"train\", \"train\", \"train\", \"train\", \"train\", \"transact\", \"transact\", \"transact\", \"transact\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transform\", \"transform\", \"transform\", \"transform\", \"transmiss\", \"transmiss\", \"transmiss\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"travel\", \"travel\", \"travel\", \"travel\", \"travel\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"trebl\", \"trebl\", \"trebl\", \"trend\", \"trend\", \"trend\", \"trend\", \"trend\", \"trend\", \"trend\", \"trend\", \"trend\", \"trend\", \"trend\", \"trend\", \"tunisia\", \"tunisia\", \"turkei\", \"turkei\", \"turkei\", \"turkei\", \"turkei\", \"turkei\", \"turkei\", \"turkei\", \"turkei\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turnov\", \"turnov\", \"turnov\", \"turnov\", \"turnov\", \"twenti\", \"twenti\", \"twenti\", \"twenti\", \"typolog\", \"typolog\", \"typolog\", \"typolog\", \"typolog\", \"ukrain\", \"ukrain\", \"ukrain\", \"ukrain\", \"ultim\", \"ultim\", \"ultim\", \"ultim\", \"unabl\", \"unabl\", \"unabl\", \"uncommon\", \"uncommon\", \"uncommon\", \"uncommon\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"under\", \"underestim\", \"underestim\", \"underestim\", \"unemploi\", \"unemploi\", \"unemploi\", \"unemploi\", \"unemploi\", \"unemploi\", \"unemploy\", \"unemploy\", \"unexpect\", \"unfavour\", \"uninterrupt\", \"unload\", \"unmet\", \"unpaid\", \"unpaid\", \"unpaid\", \"unpaid\", \"unpaid\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"upturn\", \"upturn\", \"upturn\", \"urban\", \"urban\", \"urban\", \"urban\", \"urban\", \"utilis\", \"utilis\", \"utrecht\", \"valencia\", \"valencia\", \"valid\", \"valid\", \"valid\", \"vandal\", \"veget\", \"veget\", \"vehicl\", \"vehicl\", \"vehicl\", \"vehicl\", \"vehicl\", \"vehicl\", \"versu\", \"versu\", \"versu\", \"versu\", \"vessel\", \"vessel\", \"victim\", \"victim\", \"victim\", \"video\", \"video\", \"video\", \"vietnam\", \"visit\", \"visit\", \"visitor\", \"vital\", \"vital\", \"vital\", \"vocat\", \"volum\", \"volum\", \"volum\", \"volum\", \"volum\", \"voluntari\", \"voluntari\", \"voluntari\", \"voluntari\", \"voluntari\", \"voluntari\", \"vulner\", \"vulner\", \"vulner\", \"wastewat\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"waterwai\", \"websit\", \"websit\", \"websit\", \"websit\", \"weekli\", \"weekli\", \"weekli\", \"weekli\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"whole\", \"wholesal\", \"wholesal\", \"window\", \"window\", \"winter\", \"winter\", \"winter\", \"winter\", \"winter\", \"winter\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"without\", \"woman\", \"woman\", \"woman\", \"women\", \"women\", \"women\", \"women\", \"women\", \"women\", \"women\", \"women\", \"women\", \"worker\", \"worker\", \"worker\", \"worker\", \"worker\", \"worker\", \"worker\", \"worker\", \"workforc\", \"workforc\", \"workforc\", \"workforc\", \"workforc\", \"workplac\", \"workplac\", \"workplac\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"young\", \"youngest\", \"youngest\", \"youngest\", \"youngest\", \"youngest\", \"youth\", \"youth\", \"zealand\", \"zealand\", \"zealand\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]};\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_el998824653611404165884686479\", ldavis_el998824653611404165884686479_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://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el998824653611404165884686479\", ldavis_el998824653611404165884686479_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el998824653611404165884686479\", ldavis_el998824653611404165884686479_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
       "topic                                               \n",
       "0      0.111604 -0.035171       1        1  4.632119\n",
       "1     -0.163490  0.006107       2        1  7.135528\n",
       "2     -0.216000  0.060219       3        1  3.918167\n",
       "3     -0.198399 -0.324222       4        1  6.583111\n",
       "4      0.103450  0.040828       5        1  3.002511\n",
       "5     -0.058841  0.068040       6        1  5.779392\n",
       "6      0.163263  0.008039       7        1  5.322621\n",
       "7     -0.026928  0.110505       8        1  3.651398\n",
       "8      0.238637  0.001623       9        1  3.722790\n",
       "9      0.065964 -0.061204      10        1  3.714357\n",
       "10    -0.125580  0.028626      11        1  3.265543\n",
       "11     0.100401 -0.058568      12        1  3.933181\n",
       "12    -0.079389  0.096306      13        1  6.775399\n",
       "13    -0.109004  0.155030      14        1  7.492370\n",
       "14    -0.100632  0.112478      15        1  5.322612\n",
       "15     0.040945 -0.028740      16        1  4.044870\n",
       "16     0.118264 -0.084177      17        1  5.171281\n",
       "17    -0.139183 -0.099449      18        1  3.693129\n",
       "18     0.144490  0.059844      19        1  7.391348\n",
       "19     0.130429 -0.056116      20        1  5.448273, topic_info=          Term         Freq        Total Category  logprob  loglift\n",
       "395     region  2468.000000  2468.000000  Default  30.0000  30.0000\n",
       "138     export  1990.000000  1990.000000  Default  29.0000  29.0000\n",
       "121    billion  2248.000000  2248.000000  Default  28.0000  28.0000\n",
       "255      popul  3430.000000  3430.000000  Default  27.0000  27.0000\n",
       "169      trade  1964.000000  1964.000000  Default  26.0000  26.0000\n",
       "..         ...          ...          ...      ...      ...      ...\n",
       "73      portug   118.102690   946.654020  Topic20  -4.8687   0.8285\n",
       "144  lithuania   115.293578   955.592345  Topic20  -4.8927   0.7950\n",
       "95    slovakia   111.107213   828.175280  Topic20  -4.9297   0.9011\n",
       "71      person   123.389741  2388.117474  Topic20  -4.8249  -0.0530\n",
       "299     econom   112.558519  1040.115547  Topic20  -4.9168   0.6863\n",
       "\n",
       "[1114 rows x 6 columns], token_table=      Topic      Freq     Term\n",
       "term                          \n",
       "846       3  0.381611  abbrevi\n",
       "846      16  0.508814  abbrevi\n",
       "403       1  0.013680    about\n",
       "403       2  0.312360    about\n",
       "403       3  0.034200    about\n",
       "...     ...       ...      ...\n",
       "845      11  0.007066    youth\n",
       "845      16  0.989309    youth\n",
       "1611      2  0.106580  zealand\n",
       "1611     14  0.159870  zealand\n",
       "1611     15  0.692772  zealand\n",
       "\n",
       "[4527 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualize the topics\n",
    "\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim_models.prepare(lda_model, corpus, id2word,sort_topics=False)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Interpretation of the topics and topic coherences.\n",
    "***\n",
    "\n",
    "For the interpretation of the topics, we inspect this visualization together:  \n",
    "* with the information on the decomposition of topics into its main keywords (i.e. the same information as in 8. \"The topics as a mix of keywords\"). In each topic, the main keywords are here sorted by descending importance and plots are produced.\n",
    "* with the information on the most representative article for each topic, see 7. \"Most representative document for each topic\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  1\n",
      "('popul', 0.1199923)\n",
      "('peopl', 0.053278167)\n",
      "('older', 0.049051236)\n",
      "('birth', 0.030970087)\n",
      "('project', 0.0269405)\n",
      "('chang', 0.02416508)\n",
      "('women', 0.023178034)\n",
      "('expect', 0.022042733)\n",
      "('sport', 0.013481895)\n",
      "('person', 0.012732544)\n"
     ]
    },
    {
     "data": {
      "image/png": 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56c8oxbtLukrS7X2jF0nrlfz110taLGnfUj5d0i1lKf+lZTn/dcu+WeXcV0v6oqQlw21nREQMz4CBpeSix/b6tjdoeK1ve4PhXEzSVsDHgb1KfvrDy65pwK7A64HPl7LHgDfZfiWwJ/BfKjmJgS2Ar9veCvgL8JZS/m3gUNs7M8gS/8l5HxHRPq08x7J3P2VDTU3cZy/gfNsPwGorJP/Q9qqSzOvZfZcBPluSev2Cam6nb98dtheV7YXAdEkbAuvbvqqUr5bbpZHtObZn2p45YfLUYXYlIiL608rk/TGSviFpiqRnS7oA+MdhXk/0n5/+8aY6AO8ANgG2tz0D+CMwqZ/6K6nmikRERHRdK4FlD+B3wCKqXPVn2x7ut7guAd4m6X/Bavnp+zMV+JPtJyXtCQz6tL/tB6m+Gr1TKXr7MNsYEREj0MpzLBsBO1IFl82AzSXJa0rk0g/bSyUdB8yTtBK4YZDqZ1ElGVtAFdRubeES7wFOkfQIcBmQCZSIiA5rJbDMBz5v+7Ty7avjgSuBVw3ngrbnAnMH2b9e+fkAsPMA1bZuqP+lhvKltrcFkHQkkAxeEREd1kpg2bs8u4LtvwKHSdq9vc0atteVVQLWplrT7KDuNiciYvxp5QHJ30vaiOorvpPWVL+bbJ8LnNvtdkREjGdrDCyS3kv1vMlmVHMdOwFXU311eNTbZtOpLEg+koiI2rTyrbDDgVnAnbb3BLYD7m9rqyIiYtRqJbA8ZvsxAEkTbd8KbNneZkVExGjVyuT93eWp9h8CF0t6ELi3nY2KiIjRS0N5HEXSHlQPLv7c9hNta1UHTZy2hae9+4RuN6Pjkuc+IkZC0kLbM/vb18qI5W9sz6unSRERMVa1MscSERHRsgSWiIioVSvL5n+oPCAZERGxRq2MWJ4DXCfpu5L2aUi2FRER8TRrDCy2j6ZazuVbVGtv3Sbps5Je1Oa2RUTEKNTSHEtZIv8P5fUU1VL650v6wkDHSPqYpMPK9lck/bJsv1rSmZIOKLnsl0g6vuG4hyUdL2mhpF9I2kHSZZJul/SGUmdCyWl/Xclx/y+lfHape76kWyWdlRFWRERntTLHcpikhcAXqJbL38b2+4Ht+Xuu+f5cDuxWtmcC60lahyq3/W1Uy+/vBcwAZkl6Y6k7BbjM9vbACuAzwP8G3gR8qtR5D7Dc9iyq5WbeJ+kFZd92wEeAlwMvBHbpp0/JeR8R0SatPMeyMfBm23c2FtpeJen1gxy3ENhe0vpUqYSvpwowuwEXUAWP+wEknQXsTvV0/xPAz8s5FgOPlyySi4Hppfw1wLaS+jJZTqW6XfcEcK3tu8t5F5VjftXU9jnAHKgekGzhzyAiIlrUyrL5xwyy75ZB9j0paRlwMHAVcBOwJ/Ai4PdUI57+PNmQnXIVJb99CWR97RXwYdsXNh4oaXZf/WIlQ3wINCIiRqbdz7FcDny0/LwCOJRq6f35wB6SNpY0ATgAGMpT/RcC7y+31pD0EklT6mx4REQMT7sDyxXANOBq238EHgOusH0fcBRwKXAjcL3tHw3hvKcCNwPXS1oCfJOMTCIiesKQFqEci7IIZUTE0A22CGWWdImIiFolsERERK3G/bxEct5HRNQrI5aIiKhVAktERNQqgSUiImo17udYFt+znOlH/qTbzeiIfMU4IjohI5aIiKhVAktERNQqgSUiImqVwBIREbUak4FF0gxJr+12OyIixqMxGVioslImsEREdEFHAoukAyVdK2mRpG9K2rHkqp8kaYqkpZK2LjnrL5f0A0k3SzpZ0lrlHK+RdLWk6yWdJ2m9Uj5L0lWSbizXmEqVwnj/cr39O9HHiIiotD2wSHoZsD+wi+0ZVFkdtwT+P1U++y8AZ9peUg7ZAfg3YBuqbJNvlrQxcDSwt+1XAguAIyQ9AzgXONz2K4C9gUeAY4Bzbc+wfW67+xgREX/XiQckX02Vhvg6SQDrAn+iGlVcR5X867CG+tfavh1A0jnArqXOy4EryzmeAVxNFaDus30dgO2HynGDNkjSIcAhABM22KSGLkZERJ9OBBYBc20ftVqh9BxgPWAdYBLVSAOgOfOYyzkutn1A0zm27af+GtmeA8yBKtHXUI+PiIiBdWKO5RJgP0nPApD0TEmbU32wfwI4Czi+of4Okl5Q5lb2B34FzAd2kfTico7Jkl4C3Ao8V9KsUr6+pLWBFcD6HehbREQ0afuIxfbNko4GLirB4kngR8BTts+WNAG4StJewCqqW1yfp5pjuRz4ge1Vkg4CzpE0sZz6aNu/KZPzJ0laF/gr1TzLpcCRkhYBn8s8S0RE53RkEcrywd7vh7vtlcCOAJJmA4/afto3uWz/EpjVT/l1wE79nPppdSMiov3G6nMsERHRJT21bL7ty4DLutyMiIgYgYxYIiKiVj01YumGbTadyoIkwIqIqE1GLBERUasEloiIqFUCS0RE1Grcz7Esvmc504/8Sbeb8TfLMt8TEaNcRiwREVGrBJaIiKhVAktERNQqgSUiImo1ZgNLSXP8qm63IyJivBmTgaXkZJkNJLBERHRYT3zdWNIU4LvAZsAE4NNUyb/OBfYs1f7J9m9LkrDTgE2A+4GDbf9e0unAn4Htys9dgJWSDgQ+bPuKDnYpImLc6pURyz7AvbZfYXtr4Oel/CHbOwBfA04oZV8DvmN7W6rskyc2nOclwN623wKcDHzF9ozmoCLpEEkLJC1Y+ejy9vUqImIc6pXAshjYW9Lxknaz3fdpf07Dz53L9s7A2WX7DGDXhvOcVxKHDcr2HNszbc+cMHlqDc2PiIg+PXErrKQY3h54LfA5SRf17WqsNtDhDduPtKN9ERHRup4YsUh6LlVK4jOBLwGvLLv2b/h5ddm+Cnh72X4H8KsBTrsCWL/+1kZExGB6YsQCbAN8UdIq4Eng/cD5wERJ11AFwANK3cOA0yT9H8rk/QDnvAA4X9K+ZPI+IqJjeiKw2L4QuLCxTBLA120f21R3GbBXP+c4qOn9b4Bta25qRESsQU/cCouIiLGjJ0Ys/bE9vdttiIiIoevZwNIpyXkfEVGv3AqLiIhaJbBEREStElgiIqJW436OpVdy3ifXfUSMFRmxRERErRJYIiKiVgksERFRqwSWiIioVQJLRETUqqcDS8ldHxERo0jbA4uk6ZJulTRX0k2Szpc0WdL2kuZJWijpQknTSv3LJH1W0jzgcElvlbRE0o2SLi91Jkn6tqTFkm6QtGcpP0jS9yX9XNJtkr7Q7v5FRMTqOjUi2BJ4j+0rJZ0GfBB4E7Cv7fsl7Q8cB/xzqb+h7T0AJC0G/sH2PZI2LPs/CGB7G0kvBS6S9JKybwawHfA48GtJJ9m+q7Exkg4BDgGYsMEmbelwRMR41albYXfZvrJsnwn8A7A1cLGkRcDRwGYN9c9t2L4SOF3S+4AJpWxXqnz32L4VuBPoCyyX2F5u+zHgZmDz5sYk531ERPt0asTSnK9+BbDU9s4D1P9b7nrbh0raEXgdsEjSDECDXOvxhu2VZHWBiIiO6tSI5fmS+oLIAcB8YJO+MknrSNqqvwMlvcj2NbaPAR4AngdcTpXvnnIL7PnAr9vch4iIaEGn/jd/C/BuSd8EbgNOokpFfKKkqaUdJwBL+zn2i5K2oBqlXALcCNwKnFzmX54CDrL9eElnHBERXdSpwLLK9qFNZYuA3Zsr2p7d9P7N/ZzvMeCgfo49HTi94f3rh9rQiIgYmZ5+jiUiIkafto9YbC+j+gZYRESMA+P+G1PJeR8RUa/cCouIiFolsERERK0SWCIiolYJLBERUasEloiIqFUCS0RE1CqBJSIiapXAEhERtUpgiYiIWsluTpUyvkhawdhccn9jqjQDY0n6NDqkT6PDSPu0ue1+U/CO+yVdgF/bntntRtRN0oKx1q/0aXRIn0aHdvYpt8IiIqJWCSwREVGrBBaY0+0GtMlY7Ff6NDqkT6ND2/o07ifvIyKiXhmxRERErRJYIiKiVmM6sEjaR9KvJf1W0pH97JekE8v+myS9stVju2W4fZL0PEmXSrpF0lJJh3e+9f0bye+p7J8g6QZJP+5cqwc3wr97G0o6X9Kt5fe1c2db378R9ulfy9+7JZLOkTSps60fWAv9eqmkqyU9LumjQzm2W4bbp9o+J2yPyRcwAfgd8ELgGcCNwMub6rwW+BkgYCfgmlaPHYV9mga8smyvD/xmtPepYf8RwNnAj7vdnzr6BMwF3lu2nwFsOJr7BGwK3AGsW95/Fzio230aQr+eBcwCjgM+OpRjR2GfavmcGMsjlh2A39q+3fYTwP8D9m2qsy/wHVfmAxtKmtbisd0w7D7Zvs/29QC2VwC3UP2D77aR/J6QtBnwOuDUTjZ6DYbdJ0kbALsD3wKw/YTtv3Sw7QMZ0e+J6mHsdSWtDUwG7u1Uw9dgjf2y/Sfb1wFPDvXYLhl2n+r6nBjLgWVT4K6G93fz9D+ggeq0cmw3jKRPfyNpOrAdcE39TRyykfbpBOBjwKo2tW84RtKnFwL3A98ut/dOlTSlnY1t0bD7ZPse4EvA74H7gOW2L2pjW4diJP/WR/PnxBqN5HNiLAcW9VPW/N3qgeq0cmw3jKRP1U5pPeB7wEdsP1Rj24Zr2H2S9HrgT7YX1t+sERnJ72lt4JXAN2xvBzwC9MK9+5H8njai+h/zC4DnAlMkHVhz+4ZrJP/WR/PnxOAnGOHnxFgOLHcDz2t4vxlPH34PVKeVY7thJH1C0jpUf1nOsv39NrZzKEbSp12AN0haRjXc30vSme1rastG+nfvbtt9/0s8nyrQdNtI+rQ3cIft+20/CXwfeFUb2zoUI/m3Ppo/JwZUy+dEtyea2vWi+p/f7VT/S+qbwNqqqc7rWH2y8dpWjx2FfRLwHeCEbvejrj411ZlN70zej6hPwBXAlmX7k8AXR3OfgB2BpVRzK6L6csKHu92nVvvVUPeTrD7RPWo/JwbpUy2fE13/xbb5D/i1VN9q+B3w8VJ2KHBowx/i18v+xcDMwY7thddw+wTsSjUcvglYVF6v7XZ/Rvp7ajjHbHoksNTwd28GsKD8rn4IbNTt/tTQp2OBW4ElwBnAxG73Zwj9eg7VKOAh4C9le4OBju2F13D7VNfnRJZ0iYiIWo3lOZaIiOiCBJaIiKhVAktERNQqgSUiImqVwBIREbVKYInoIklXdbsNEXXL140jIqJWGbFEtEDSrJJjZJKkKSVXxdb91PuhpIVl/yGlbHNJt0naWNJakq6Q9Jqy7+Hyc5qkyyUtKjlLduvn3MskHSvpekmLJb20lO8g6aqyaOVVkrYs5QeV9lwg6Q5JH5J0RKk3X9IzS70XSfp5afcVfeeNGK6MWCJaJOkzwCRgXar1vD7XT51n2v6zpHWB64A9bP+3pPcC+1CtFPti2/9S6j9sez1J/wZMsn2cpAnAZFfLljeeexnwX7ZPkvQBqrwZ7y1L7T9q+ylJewPvt/0WSQcBR1OtUDsJ+C3w77ZPlvQV4E7bJ0i6hOqJ7Nsk7Qh8zvZedf/5xfixdrcbEDGKfIoqWDwGHDZAncMkvalsPw/YAvhv26dKeivVshoz+jnuOuC0sgDgD20vGuD8fYsCLgTeXLanAnMlbUG1HMc6DfUvLQFqhaTlwAWlfDGwbVnF9lXAedLfFsWdOMC1I1qSW2ERrXsmsB5VZr2npdaVNJtqJd+dbb8CuKGvnqTJVKvMUs6xGtuXUyX4ugc4Q9K7BmjD4+XnSv7+H8NPUwWQrYF/bGrb4w3bqxreryrHrwX8xfaMhtfLBrh2REsSWCJaNwf4BHAWcHw/+6cCD9p+tMxT7NSw7/hy3DHAKc0HStqcKrfMKVTZI4eyVP5UqoAEcNAQjsNVro07ymiqL2/9K4ZyjohmCSwRLSgjiKdsnw18HpglqXke4ufA2pJuohpFzC/H7kGVX/x422cBT0g6uOnY2cAiSTcAbwG+OoTmfQH4nKQrqfKdD9U7gPdIupFqefteSK8bo1gm7yMiolYZsURERK0SWCIiolYJLBERUasEloiIqFUCS0RE1CqBJSIiapXAEhERtfofIncPbf4swk0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  2\n",
      "('product', 0.06328389)\n",
      "('price', 0.031337626)\n",
      "('million', 0.027639283)\n",
      "('quarter', 0.015288828)\n",
      "('agricultur', 0.014839729)\n",
      "('industri', 0.01414814)\n",
      "('produc', 0.013793156)\n",
      "('crisi', 0.013542772)\n",
      "('develop', 0.011397199)\n",
      "('declin', 0.011237359)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  3\n",
      "('energi', 0.09843536)\n",
      "('consumpt', 0.057481315)\n",
      "('product', 0.057006303)\n",
      "('electr', 0.033913475)\n",
      "('emiss', 0.033301983)\n",
      "('renew', 0.01670016)\n",
      "('gross', 0.016428646)\n",
      "('final', 0.015622027)\n",
      "('primari', 0.014941688)\n",
      "('environment', 0.014081053)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  4\n",
      "('billion', 0.10869561)\n",
      "('export', 0.10508282)\n",
      "('trade', 0.094464764)\n",
      "('china', 0.026533486)\n",
      "('partner', 0.025713332)\n",
      "('million', 0.02380786)\n",
      "('product', 0.022335319)\n",
      "('manufactur', 0.021680895)\n",
      "('world', 0.019824764)\n",
      "('extra', 0.017699137)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  5\n",
      "('household', 0.07934886)\n",
      "('cultur', 0.06139802)\n",
      "('internet', 0.059391033)\n",
      "('adult', 0.03159627)\n",
      "('onlin', 0.018811353)\n",
      "('point', 0.017496325)\n",
      "('servic', 0.017185723)\n",
      "('comput', 0.016955024)\n",
      "('proport', 0.01633198)\n",
      "('access', 0.015294363)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  6\n",
      "('region', 0.14750375)\n",
      "('sector', 0.035061363)\n",
      "('economi', 0.024352927)\n",
      "('employ', 0.022837462)\n",
      "('person', 0.022644738)\n",
      "('financi', 0.020130897)\n",
      "('emploi', 0.018332047)\n",
      "('activ', 0.018127277)\n",
      "('capit', 0.017824108)\n",
      "('labour', 0.017045908)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  7\n",
      "('person', 0.06212635)\n",
      "('group', 0.034454107)\n",
      "('emploi', 0.03192197)\n",
      "('health', 0.027776793)\n",
      "('children', 0.023897119)\n",
      "('women', 0.020339003)\n",
      "('peopl', 0.016517973)\n",
      "('unmet', 0.012261887)\n",
      "('limit', 0.011623737)\n",
      "('reason', 0.011372666)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  8\n",
      "('activ', 0.10193726)\n",
      "('accid', 0.05790873)\n",
      "('servic', 0.04594818)\n",
      "('fatal', 0.037995163)\n",
      "('sector', 0.01791504)\n",
      "('emploi', 0.01686255)\n",
      "('person', 0.015685331)\n",
      "('construct', 0.014892828)\n",
      "('inform', 0.01467567)\n",
      "('manufactur', 0.014367642)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  9\n",
      "('peopl', 0.07714528)\n",
      "('activ', 0.054337814)\n",
      "('popul', 0.04687358)\n",
      "('particip', 0.04657721)\n",
      "('adult', 0.04423926)\n",
      "('famili', 0.027598012)\n",
      "('least', 0.025051277)\n",
      "('incom', 0.017810546)\n",
      "('women', 0.016879423)\n",
      "('formal', 0.016766688)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  10\n",
      "('citizen', 0.09851242)\n",
      "('foreign', 0.05815489)\n",
      "('resid', 0.038828705)\n",
      "('nation', 0.027711393)\n",
      "('permit', 0.024276583)\n",
      "('first', 0.024226302)\n",
      "('citizenship', 0.024143104)\n",
      "('applic', 0.019651024)\n",
      "('languag', 0.016409328)\n",
      "('reliabl', 0.012184849)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  11\n",
      "('servic', 0.10991903)\n",
      "('tourist', 0.037516456)\n",
      "('night', 0.036777157)\n",
      "('tourism', 0.03675474)\n",
      "('spent', 0.036351573)\n",
      "('accommod', 0.032496896)\n",
      "('resid', 0.030615142)\n",
      "('travel', 0.01794718)\n",
      "('domest', 0.014994917)\n",
      "('million', 0.014592156)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  12\n",
      "('tertiari', 0.050157882)\n",
      "('student', 0.03960116)\n",
      "('secondari', 0.039298218)\n",
      "('graduat', 0.034160394)\n",
      "('pupil', 0.0168122)\n",
      "('studi', 0.016301828)\n",
      "('abroad', 0.01598793)\n",
      "('recent', 0.015829947)\n",
      "('proport', 0.015713323)\n",
      "('attain', 0.015412555)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  13\n",
      "('expenditur', 0.07048681)\n",
      "('benefit', 0.03284315)\n",
      "('govern', 0.030722959)\n",
      "('financi', 0.027757363)\n",
      "('social', 0.024657333)\n",
      "('ratio', 0.019841298)\n",
      "('incom', 0.015687248)\n",
      "('gross', 0.014270107)\n",
      "('asset', 0.014182936)\n",
      "('revenu', 0.012985299)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  14\n",
      "('water', 0.0155347055)\n",
      "('european', 0.015062109)\n",
      "('develop', 0.013354959)\n",
      "('target', 0.012563807)\n",
      "('measur', 0.011442561)\n",
      "('sustain', 0.010474404)\n",
      "('progress', 0.010291988)\n",
      "('toward', 0.009352174)\n",
      "('environment', 0.008976368)\n",
      "('trend', 0.008853229)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  15\n",
      "('enterpris', 0.078327276)\n",
      "('economi', 0.0206551)\n",
      "('pension', 0.014472414)\n",
      "('turnov', 0.0128967585)\n",
      "('invest', 0.012643897)\n",
      "('intern', 0.012378148)\n",
      "('econom', 0.01189193)\n",
      "('ukrain', 0.01157848)\n",
      "('belaru', 0.010589339)\n",
      "('financi', 0.010566308)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  16\n",
      "('unemploy', 0.06953573)\n",
      "('candid', 0.054883495)\n",
      "('potenti', 0.028984487)\n",
      "('turkei', 0.027757302)\n",
      "('outsid', 0.02540272)\n",
      "('labour', 0.025132159)\n",
      "('serbia', 0.020990707)\n",
      "('south', 0.020116316)\n",
      "('macedonia', 0.018882459)\n",
      "('north', 0.018614074)\n"
     ]
    },
    {
     "data": {
      "image/png": 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gCOA9wM7A54H1K9WNiCuAduCQiJgArA0cCGwTEdsB36/Q7HxgakTs0kMcUyS1S2pftbyzr5djZmYV5ElSL0laC3hQ0pckHQi8pcZx5bEb8LuIeCEingeuBHbP2fY54CXgXEkfoWyIfVoEcVREzEm7uu05RsSMiGiLiLYhI1t6fRFmZta9PEnqGGAkMBXYATgUmFzDmPJShX2jeP01jahQh4hYCewE/DfZyrvXVjh2YGZmDdVjkoqIuyLi+Yh4PCKOiIiPRMTt9QiuBzcDH5Y0Mq11dSDZ8vFvkbShpOHA/iX1lwHrAUhaF2iJiD+QJeEJpQeOiGeBTkm7pV2H1PA6zMysG3lG920BHA9sWlo/Ihr6XCoi7pZ0AXBn2nVuRNwl6bvAHWQrCC8qaXIBMF3Si8D7gP+RNIKs1/TVCqc4AjhP0nLgutpchZmZrYki1nxXS9I9wHRgLtnsEwBExNzahtZ8ho8ZF2Mmn9XoMKyKPMGsWe1JmhsRbZXK8sw4sTIizq5yTGZmZj3KM3DiaklHez0pMzOrtzy3+x6psDuacMaJmmtra4v29vZGh2Fm1lT6dbsvIjarfkhmZmY9y3O7z8zMrCGcpMzMrLDyjO6znDqWdNI6bVajw7Aq8fBzs8brsScladc0owOSPi3px5I2rX1oZmY22OW53Xc2sFzSu4CvAY+SLddhZmZWU3mS1MrIxqkfAPw0In5KmgPPzMyslvI8k1om6RvAp4E9JA0BhtU2LDMzs3w9qYOBFcBnI+IfwFjgjJpGlYOk53sof90qvWZm1nzy/Jj3H8CPSz7/nUH8TErS0LQelZmZ1Vi3PSlJf07vyyQ9V/JaJum5+oW4ZpLWlXSjpLsldUg6oKR4qKSZkhZIukLSyNRmH0nzUv3z0tpTSFosaXTabpM0O22fJGmGpOsZxAnazKzeuk1SEbFbel8vIt5c8lovIt5cvxB79BJwYES8G9gL+JGkrlV7twRmRMR2ZEvGH53WkLoAODgixpP1Jo/KcZ4dgAMi4lOlOyVNkdQuqX3V8s7qXJGZmQH5fic1qcK+Iiwf30XADyQtAP5E9szsranssYi4NW1fDOxGlrgeiYgH0v6ZwB45zvP7iHixfGdEzIiItohoGzKypT/XYWZmZfIMnDhR0tmS1pH0VklXAx+sdWC9cAiwEbBDREwA/gmMSGXlU7wHWVLrzkpe+05GlJW90L8wzcyst/IkqT2Bh4D5wJ+BX0fEQbUMqpdagKci4hVJe5Etc9/l7ZJ2SdufJIt/EdAq6Z1p/6HAnLS9mOy2HsBHaxq1mZn1KE+SWh94D1miWgFsWvLMpwguAdoktZP1qhaVlN0HTE63AjcAzo6Il4AjgMsldQCrgemp/snATyXdAqyq1wWYmVlleX7MeztwWkScJ2lt4HTgVuC9NY2sBxGxbnpfCuzSTbWtu2l7I7B9hf23AFtU2H9SnwM1M7M+y5OkJqXfRpEGDkyVlGeggZmZWb/k+THv3yWtD4zjjYMJzMzMaqbHJCXpc8BXgI3JBk/sDPwF2LumkTWh8WNbaPcaRGZmVZNn4MRXgB2BRyNiL7JnOU/XNCozMzPyJamX0og4JA2PiEVkP4g1MzOrqTwDJx6XNAq4CrhB0jPAE7UMyszMDEDZeoY5K0t7kv149tqIeLlmUTWp4WPGxZjJZzU6jEFtsZ8JmjUdSXMjoq1SWZ6e1KsiYk7PtczMzKojzzMpMzOzhnCSMjOzwsqzVMeX0o95zczM6ipPT+o/gLskXSZpv4JNLtsnki6Q9IaZ3NNqvD9rRExmZvZGPSapiDiBbEqkXwGHAw9K+oGkzWscW01I6nawSES0R8TUesZjZmbdy/VMKrJx6v9Ir5Vky3dcIemHNYxtjdIijLMk3SNpoaSDJe0gaY6kuZKukzQm1Z2dEuscshk0ACZJukXSA5L2T/UmSrombe8k6TZJ89K7f8BsZlZneebumwpMBpYC5wLHpwUG1wIeBL5W2xC7tR/wRER8IMXZAvwROCAinpZ0MHAK8JlUf1RE7JnqXgC0ki3ouDlwU8kiiF0WAXtExEpJk4AfUGEhRElTgCkAQ968UVUv0MxssMvzO6nRwEci4tHSnRGxuqsH0iAdwJmSTgeuAZ4BtiWbFQNgCPBkSf1Ly9pfFhGryW5fPgxsVVbeAsyUNI5s2flhlYKIiBnADMh+zNuvKzIzs9fJs1THiWsou6+64eQXEQ9I2gF4P3AqcANwb0R0twDiC+WH6OHz94CbIuJASa3A7P5FbGZmvdW0v5OS9DZgeURcDJxJtsT9RpJ2SeXDJG2zhkN8TNJaaQDIO4D7y8pbgCVp+/CqBm9mZrn0alqkghkPnCFpNfAKcBTZoI6fpedTQ4GzgHu7aX8/MAd4K3BkRLxUNrr+h2S3+44F/rcmV2BmZmvUqwlmbc08wWzjeYJZs+azpglmm/Z2n5mZDXxOUmZmVljN/EyqcMaPbaHdt5vMzKrGPSkzMyssJykzMyssJykzMyssP5Oqoo4lnbROm9XoMAYVDzk3G9jckzIzs8JykjIzs8JykjIzs8JykjIzs8JykkrSqrzvLfl8gaSDGhmTmdlg5yT1monAe3uqZGZm9TMgkpSkdSTNknSPpIWSDpa0j6R5kjoknSdpeKq7WNLotN0maXZa1PBI4KuS5kvaPR16D0m3SXrYvSozs/obEEkK2A94IiLeFRHbAtcCFwAHR8R4st+DHdVd44hYDEwHfhIREyLillQ0BtgN2B84rXbhm5lZJQMlSXUAkySdnnpBrcAjEfFAKp8J7NGH414VEasj4q9kiyO+gaQpktolta9a3tmX2M3MrBsDIkmlZLQDWbI6FThgDdVX8tp1j+jh0CtKtlWpQkTMiIi2iGgbMrIlZ8RmZpbHgEhSkt4GLI+Ii4EzyQZAtEp6Z6pyKNlS8QCLyRIawEdLDrMMWK/20ZqZWV4DZe6+8cAZklYDr5A9f2oBLpc0FLiL7JkTwMnAryR9E7ij5BhXA1dIOgD4ct0iNzOzbg2IJBUR1wHXVSjavkLdW4AtKux/ANiuZNctZeXr9jNMMzPrpQFxu8/MzAYmJykzMyssJykzMyusAfFMqijGj22h3YvwmZlVjXtSZmZWWE5SZmZWWE5SZmZWWH4mVUUdSzppnTar0WEMSIv9rM9sUHJPyszMCstJyszMCstJyszMCstJyszMCmvAJSlJrZIW9qP9HySNqmJIZmbWRx7dVyYi3t/oGMzMLFOznlTq0SySdK6khZIukTRJ0q2SHpS0U3rdJmleet8ytR0i6UxJHZIWSPpy2r+DpDmS5kq6TtKYkv33SPoL8MWSGEZIOj8dZ56kvdL+wyVdKenaFMsPS9osljQ6bV+VznWvpCm1+q7MzKyyWt/ueyfwU7J1mrYCPgXsBhwHfBNYBOwREdsDJwI/SO2mAJsB20fEdsAlkoYBPwcOiogdgPOAU1L984GpEbFL2fm/CBAR44FPAjMldS0ZPwE4mGzBxIMlbVIh/s+kc7UBUyVtWF5B0hRJ7ZLaVy3vzP/NmJlZj2p9u++RiOgAkHQvcGNEhKQOoJVs9dyZksYBAQxL7SYB0yNiJUBE/FvStsC2wA2SAIYAT0pqAUZFRNfy8BcB70vbu5ElNiJikaRHeW3BwxsjojPF9ldgU+CxsvinSjowbW8CjAP+VVohImYAMwCGjxkXvf+KzMysO7VOUitKtleXfF6dzv094KaIOFBSKzA7lYssaZUScG95bykNcuguOShnbKso+y4kTSRLlrtExHJJs4ERmJlZ3TR6dF8LsCRtH16y/3rgSElDASRtANwPbCRpl7RvmKRtIuJZoFPSbqntISXHubnrs6QtgLen4+SN7ZmUoLYCdu7ltZmZWT81Okn9EDhV0q1kt++6nAv8HVgg6R7gUxHxMnAQcHraNx94b6p/BPCLNHDixZLj/BIYkm4vXgocHhGlPag1uRYYKmkBWY/v9r5coJmZ9Z0i/BilWoaPGRdjJp/V6DAGJE8wazZwSZobEW2VyhrdkzIzM+uWk5SZmRWWZ5yoovFjW2j3bSkzs6pxT8rMzArLScrMzArLScrMzArLz6SqqGNJJ63TZjU6jAHHw8/NBi/3pMzMrLCcpMzMrLCcpMzMrLCcpMzMrLCcpBJJH5a0dcnn2ZIqziVlZmb14SQFpCVBPgxs3UNVMzOrowGTpCS1SrpP0jmS7pV0vaS1JU2QdLukBZJ+J2n9VH+2pB9ImgN8HfgQcIak+ZI2T4f9mKQ7JT0gafdGXZuZ2WA1YJJUMg74RURsAzwLfBS4EPh6RGwHdADfKak/KiL2jIhTgN8Dx0fEhIh4KJUPjYidgGPK2r1K0hRJ7ZLaVy3vrMlFmZkNVgMtST0SEfPT9lxgc7JENCftmwnsUVL/0h6Od2XJsVorVYiIGRHRFhFtQ0a29CloMzOrbKAlqdJVd1cBo3qo/0LO463Cs3OYmdXdQEtS5TqBZ0qeJx0KzOmm7jJgvbpEZWZmuQyG3sFkYLqkkcDDwBHd1PstcI6kqcBB9QrOzMy6N2CSVEQsBrYt+XxmSfHOFepPLPt8K68fgj6xpGwp3TyTMjOz2hnot/vMzKyJOUmZmVlhDZjbfUUwfmwL7V77yMysatyTMjOzwnKSMjOzwnKSMjOzwnKSMjOzwnKSMjOzwnKSMjOzwnKSMjOzwnKSMjOzwnKSMjOzwlJENDqGAUPSMuD+RsfRB6OBpY0Oog8cd3057vppxpih73FvGhEbVSrwtEjVdX9EtDU6iN6S1O6468dx11czxt2MMUNt4vbtPjMzKywnKTMzKywnqeqa0egA+shx15fjrq9mjLsZY4YaxO2BE2ZmVljuSZmZWWE5SZmZWWE5SeUkaT9J90v6m6RpFcol6WepfIGkd+dtW+C4z5P0lKSFzRCzpE0k3STpPkn3SvpKk8Q9QtKdku5JcZ/cDHGXlA+RNE/SNfWLut9/24sldUiaL6m9ieIeJekKSYvS3/kuRY9b0pbpe+56PSfpmNwnjgi/engBQ4CHgHcAbwLuAbYuq/N+4I+AgJ2BO/K2LWLcqWwP4N3Awib5rscA707b6wEPNMN3nT6vm7aHAXcAOxc97pLyY4FfA9c0w99JKlsMjK5XvFWMeybwubT9JmBUM8Rddpx/kP14N9e53ZPKZyfgbxHxcES8DPwWOKCszgHAhZG5HRglaUzOtkWMm4i4Gfh3nWLt0ueYI+LJiLgbICKWAfcBY5sg7oiI51OdYelVrxFN/fobkbQx8AHg3DrF26VfcTdQn+OW9Gayfzj+CiAiXo6IZ4sed1mdfYCHIuLRvCd2kspnLPBYyefHeeP//Lqrk6dtrfQn7kapSsySWoHtyXol9dCvuNMts/nAU8ANEdEUcQNnAV8DVtcovu70N+4Arpc0V9KUmkX5Rv2J+x3A08D56fbquZLWqWWwOWLqbZ1PAL/pzYmdpPJRhX3l/9Ltrk6etrXSn7gbpd8xS1oX+G/gmIh4roqxrUm/4o6IVRExAdgY2EnSttUNr1t9jlvS/sBTETG3+mH1qL9/J7tGxLuB9wFflLRHNYNbg/7EPZTs9vvZEbE98AJQr2fc1fjv8k3Ah4DLe3NiJ6l8Hgc2Kfm8MfBEzjp52tZKf+JulH7FLGkYWYK6JCKurGGc5aryXafbN7OB/aoeYWX9iXtX4EOSFpPd/tlb0sW1CzVXTLnqRETX+1PA78huZ9VDf/9f8nhJL/sKsqRVD9X4+34fcHdE/LNXZ67HQ7dmf5H9C+ZhYDNee2i4TVmdD/D6h4Z35m1bxLhLylup78CJ/nzXAi4Ezmqyv5GNSA/AgbWBW4D9ix53WZ2J1HfgRH++73WA9Uq2bwP2K3rcqewWYMu0fRJwRjPEncp/CxzR63PX64+q2V9kI1ceIBvh8q2070jgyLQt4BepvANoW1PbJon7N8CTwCtk/0r6bJFjBnYju72wAJifXu8v+ncNbAfMS3EvBE5slr+RkmNMpI5Jqp/f9zvS/2TvAe5tsv8mJwDt6W/lKmD9Jol7JPAvoKW35/W0SGZmVlh+JmVmZoXlJGVmZoXlJGVmZoXlJGVmZoXlJGVmZoXlJGU2AEi6rdExmNWCh6CbmVlhuSdlVkeSdkxr7YyQtE5aP+oN8/RJuipNfnpv1wSokjaV9KCk0ZLWknSLpH1T2fPpfYykm9O6PQsl7V7h2IslnSzp7rSm0lZp/06SbkuTl94macu0//AUz9WSHpH0JUnHpnq3S9og1dtc0rUp7lu6jmvWH+5JmdWZpO8DI8imQHo8Ik6tUGeDiPi3pLWBu4A9I+Jfkj5HNq/fHcA7I+ILqf7zEbGupP8ERkTEKZKGACMjW7ak9NiLgR9FxM8lHU22Btfn0lIQyyNipaRJwFER8VFJhwMnkM0qPwL4G/D1iJgu6SfAoxFxlqQbyWYfeFDSe4BTI2Lvan9/NrgMbXQAZoPQd8kSz0vA1G7qTJV0YNreBBgH/CsizpX0MbLpaCZUaHcXcF6aaPeqiJjfzfG7Jt+dC3wkbbcAMyWNI5tealhJ/ZtSslsmqRO4Ou3vALZLM8+/F7hcenUy7OHdnNssN9/uM6u/DYB1yVYPHlFeKGkiMAnYJSLeRTav34hUNpJsdmnSMV4nsoUq9wCWABdJOqybGFak91W89o/V75Elo22BD5bFtqJke3XJ59Wp/VrAsxExoeT1f7o5t1luTlJm9TcD+DZwCXB6hfIW4JmIWJ6e6+xcUnZ6ancicE55Q0mbkq3xdA7ZCq69WcqhhSy5ARzei3ZEtm7XI6mXhzLv6s0xzCpxkjKro9SzWRkRvwZOA3aUVP7c5lpgqKQFZL2b21PbPYEdgdMj4hLgZUlHlLWdCMyXNA/4KPDTXoT3Q+BUSbcCQ3p3ZQAcAnxWUtfs4uXLi5v1mgdOmJlZYbknZWZmheUkZWZmheUkZWZmheUkZWZmheUkZWZmheUkZWZmheUkZWZmhfX/ARgq3goDoAGiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  17\n",
      "('death', 0.06334814)\n",
      "('inhabit', 0.050538324)\n",
      "('immigr', 0.04218874)\n",
      "('cancer', 0.025374781)\n",
      "('diseas', 0.024253273)\n",
      "('nativ', 0.022466743)\n",
      "('origin', 0.019272799)\n",
      "('women', 0.01890955)\n",
      "('second', 0.018328192)\n",
      "('patient', 0.01770085)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  18\n",
      "('transport', 0.111576386)\n",
      "('passeng', 0.041463867)\n",
      "('freight', 0.02827678)\n",
      "('million', 0.02167414)\n",
      "('regist', 0.018444825)\n",
      "('perform', 0.017887432)\n",
      "('kilometr', 0.017839896)\n",
      "('vehicl', 0.014957526)\n",
      "('quarter', 0.013910949)\n",
      "('intern', 0.013702696)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  19\n",
      "('incom', 0.059053198)\n",
      "('household', 0.049192283)\n",
      "('poverti', 0.044515315)\n",
      "('peopl', 0.040413465)\n",
      "('social', 0.028069565)\n",
      "('popul', 0.027536705)\n",
      "('point', 0.023585496)\n",
      "('children', 0.022314927)\n",
      "('exclus', 0.015606834)\n",
      "('depriv', 0.012120484)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  20\n",
      "('employ', 0.09037193)\n",
      "('young', 0.08530594)\n",
      "('peopl', 0.051906627)\n",
      "('women', 0.038365778)\n",
      "('point', 0.029195422)\n",
      "('labour', 0.026885198)\n",
      "('quarter', 0.026284676)\n",
      "('emploi', 0.02584849)\n",
      "('employe', 0.022703765)\n",
      "('gender', 0.021219553)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "   \n",
    "for topic_id in range(num_topics):\n",
    "    print('\\nTopic id: ',topic_id+1 )\n",
    "    top_words = [(a,b) for (a,b) in sorted(lda_model.show_topic(topic_id,topn=10),key=lambda x: x[1], reverse=True)]\n",
    "    for t in range(len(top_words)):\n",
    "        print(top_words[t])\n",
    "    y_axis = [t[0] for t in top_words]\n",
    "    x_axis = [t[1] for t in top_words] \n",
    "    \n",
    "    plt.barh(y_axis,x_axis)\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.title('title name')\n",
    "    plt.ylabel('y axis name')\n",
    "    plt.xlabel('x axis name')\n",
    "    plt.title('Topic id: '+str(topic_id+1))\n",
    "    plt.show()        \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We end-up with the following interpretations of the 20 topics. Together with the topic names, we also give the coherence scores by topic. We can see that topic 14 \"Water resouces and sustainable development\" has the highest coherence (0.71) while topic 17 \"Health in the migrant population\" has the lowest (0.30).   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic  1. Coherence: 0.398878  Population projections.  \n",
      "Topic  2. Coherence: 0.495567  Products and prices.  \n",
      "Topic  3. Coherence: 0.693183  Energy production, consumption and the environment.  \n",
      "Topic  4. Coherence: 0.700233  EU exports to the world.  \n",
      "Topic  5. Coherence: 0.487620  Internet in households.  \n",
      "Topic  6. Coherence: 0.511891  Regions and economic sectors.  \n",
      "Topic  7. Coherence: 0.401009  Employment and health.  \n",
      "Topic  8. Coherence: 0.623845  Accidents at work.  \n",
      "Topic  9. Coherence: 0.550019  Participation in activities.  \n",
      "Topic 10. Coherence: 0.497090  Citizenship.  \n",
      "Topic 11. Coherence: 0.669195  Tourist spending and nights stay.  \n",
      "Topic 12. Coherence: 0.569557  Education.  \n",
      "Topic 13. Coherence: 0.598295  Social expenditure and social benefits.  \n",
      "Topic 14. Coherence: 0.730548  Water resouces and sustainable development.  \n",
      "Topic 15. Coherence: 0.376975  Enterprises and investments.  \n",
      "Topic 16. Coherence: 0.483232  Unemployment in candidate countries.  \n",
      "Topic 17. Coherence: 0.263420  Health in the migrant population.  \n",
      "Topic 18. Coherence: 0.552467  Transport freights and passengers.  \n",
      "Topic 19. Coherence: 0.620005  Households incomes and poverty.  \n",
      "Topic 20. Coherence: 0.535396  Employment in the young population.  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "topic_names = [\n",
    "'Population projections',    \n",
    "'Products and prices',    \n",
    "'Energy production, consumption and the environment',   \n",
    "'EU exports to the world',\n",
    "'Internet in households',\n",
    "'Regions and economic sectors',\n",
    "'Employment and health',\n",
    "'Accidents at work',\n",
    "'Participation in activities',\n",
    "'Citizenship',\n",
    "'Tourist spending and nights stay',\n",
    "'Education',\n",
    "'Social expenditure and social benefits',   \n",
    "'Water resouces and sustainable development',    \n",
    "'Enterprises and investments',\n",
    "'Unemployment in candidate countries',    \n",
    "'Health in the migrant population',\n",
    "'Transport freights and passengers',\n",
    "'Households incomes and poverty',\n",
    "'Employment in the young population']    \n",
    "\n",
    "coh_topics = coherence_model_lda.get_coherence_per_topic(segmented_topics=None,with_std=False, with_support=False)\n",
    "\n",
    "for (i,tn) in enumerate(topic_names):\n",
    "    print('Topic {0:2d}. Coherence: {1:6f}  {2:s}.  '.format(i+1,coh_topics[i],tn))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "x=np.arange(num_topics)+1\n",
    "plt.bar(x=x,height=coh_topics)\n",
    "plt.xlabel(\"Topic\")\n",
    "plt.ylabel(\"Coherence\")\n",
    "plt.title(\"Coherence by topic\")\n",
    "ax.set_xticks(x)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we show the dataframe with the most representative document for each topic, completed with the topic intepretations. Many of the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic_Num</th>\n",
       "      <th>Topic interpretation</th>\n",
       "      <th>Topic Keywords</th>\n",
       "      <th>Most representative article</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Population projections</td>\n",
       "      <td>popul, peopl, older, birth, project, chang, wo...</td>\n",
       "      <td>Population projections in the EU</td>\n",
       "      <td>0.9114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Products and prices</td>\n",
       "      <td>product, price, million, quarter, agricultur, ...</td>\n",
       "      <td>Farms and farmland in the European Union - sta...</td>\n",
       "      <td>0.9971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Energy production, consumption and the environ...</td>\n",
       "      <td>energi, consumpt, product, electr, emiss, rene...</td>\n",
       "      <td>Production of lignite in the EU - statistics</td>\n",
       "      <td>0.9977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>EU exports to the world</td>\n",
       "      <td>billion, export, trade, china, partner, millio...</td>\n",
       "      <td>Norway-EU - international trade in goods stati...</td>\n",
       "      <td>0.9976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Internet in households</td>\n",
       "      <td>household, cultur, internet, adult, onlin, poi...</td>\n",
       "      <td>Digital economy and society statistics - house...</td>\n",
       "      <td>0.8063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Regions and economic sectors</td>\n",
       "      <td>region, sector, economi, employ, person, finan...</td>\n",
       "      <td>Water supply, sewerage, waste management and r...</td>\n",
       "      <td>0.9989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Employment and health</td>\n",
       "      <td>person, group, emploi, health, children, women...</td>\n",
       "      <td>Main place of work and commuting time - statis...</td>\n",
       "      <td>0.9493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Accidents at work</td>\n",
       "      <td>activ, accid, servic, fatal, sector, emploi, p...</td>\n",
       "      <td>Accidents at work - statistics by economic act...</td>\n",
       "      <td>0.9993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Participation in activities</td>\n",
       "      <td>peopl, activ, popul, particip, adult, famili, ...</td>\n",
       "      <td>Social participation and integration statistics</td>\n",
       "      <td>0.8624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Citizenship</td>\n",
       "      <td>citizen, foreign, resid, nation, permit, first...</td>\n",
       "      <td>Asylum quarterly report</td>\n",
       "      <td>0.9915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Tourist spending and nights stay</td>\n",
       "      <td>servic, tourist, night, tourism, spent, accomm...</td>\n",
       "      <td>Tourism statistics - winter season occupancy</td>\n",
       "      <td>0.9937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Education</td>\n",
       "      <td>tertiari, student, secondari, graduat, pupil, ...</td>\n",
       "      <td>Learning mobility statistics</td>\n",
       "      <td>0.9994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Social expenditure and social benefits</td>\n",
       "      <td>expenditur, benefit, govern, financi, social, ...</td>\n",
       "      <td>Government finance statistics - quarterly data</td>\n",
       "      <td>0.9792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Water resouces and sustainable development</td>\n",
       "      <td>water, european, develop, target, measur, sust...</td>\n",
       "      <td>SDG 15 - Life on land</td>\n",
       "      <td>0.9990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Enterprises and investments</td>\n",
       "      <td>enterpris, economi, pension, turnov, invest, i...</td>\n",
       "      <td>European Neighbourhood Policy - East - economi...</td>\n",
       "      <td>0.8798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Unemployment in candidate countries</td>\n",
       "      <td>unemploy, candid, potenti, turkei, outsid, lab...</td>\n",
       "      <td>Labour market slack – annual statistics on unm...</td>\n",
       "      <td>0.7435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Health in the migrant population</td>\n",
       "      <td>death, inhabit, immigr, cancer, diseas, nativ,...</td>\n",
       "      <td>Cancer statistics - specific cancers</td>\n",
       "      <td>0.9990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Transport freights and passengers</td>\n",
       "      <td>transport, passeng, freight, million, regist, ...</td>\n",
       "      <td>Maritime freight and vessels statistics</td>\n",
       "      <td>0.9985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Households incomes and poverty</td>\n",
       "      <td>incom, household, poverti, peopl, social, popu...</td>\n",
       "      <td>Quality of life indicators - economic security...</td>\n",
       "      <td>0.9991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Employment in the young population</td>\n",
       "      <td>employ, young, peopl, women, point, labour, qu...</td>\n",
       "      <td>Employment - quarterly statistics</td>\n",
       "      <td>0.9978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic_Num                               Topic interpretation  \\\n",
       "0           0                             Population projections   \n",
       "1           1                                Products and prices   \n",
       "2           2  Energy production, consumption and the environ...   \n",
       "3           3                            EU exports to the world   \n",
       "4           4                             Internet in households   \n",
       "5           5                       Regions and economic sectors   \n",
       "6           6                              Employment and health   \n",
       "7           7                                  Accidents at work   \n",
       "8           8                        Participation in activities   \n",
       "9           9                                        Citizenship   \n",
       "10         10                   Tourist spending and nights stay   \n",
       "11         11                                          Education   \n",
       "12         12             Social expenditure and social benefits   \n",
       "13         13         Water resouces and sustainable development   \n",
       "14         14                        Enterprises and investments   \n",
       "15         15                Unemployment in candidate countries   \n",
       "16         16                   Health in the migrant population   \n",
       "17         17                  Transport freights and passengers   \n",
       "18         18                     Households incomes and poverty   \n",
       "19         19                 Employment in the young population   \n",
       "\n",
       "                                       Topic Keywords  \\\n",
       "0   popul, peopl, older, birth, project, chang, wo...   \n",
       "1   product, price, million, quarter, agricultur, ...   \n",
       "2   energi, consumpt, product, electr, emiss, rene...   \n",
       "3   billion, export, trade, china, partner, millio...   \n",
       "4   household, cultur, internet, adult, onlin, poi...   \n",
       "5   region, sector, economi, employ, person, finan...   \n",
       "6   person, group, emploi, health, children, women...   \n",
       "7   activ, accid, servic, fatal, sector, emploi, p...   \n",
       "8   peopl, activ, popul, particip, adult, famili, ...   \n",
       "9   citizen, foreign, resid, nation, permit, first...   \n",
       "10  servic, tourist, night, tourism, spent, accomm...   \n",
       "11  tertiari, student, secondari, graduat, pupil, ...   \n",
       "12  expenditur, benefit, govern, financi, social, ...   \n",
       "13  water, european, develop, target, measur, sust...   \n",
       "14  enterpris, economi, pension, turnov, invest, i...   \n",
       "15  unemploy, candid, potenti, turkei, outsid, lab...   \n",
       "16  death, inhabit, immigr, cancer, diseas, nativ,...   \n",
       "17  transport, passeng, freight, million, regist, ...   \n",
       "18  incom, household, poverti, peopl, social, popu...   \n",
       "19  employ, young, peopl, women, point, labour, qu...   \n",
       "\n",
       "                          Most representative article  Topic_Perc_Contrib  \n",
       "0                    Population projections in the EU              0.9114  \n",
       "1   Farms and farmland in the European Union - sta...              0.9971  \n",
       "2        Production of lignite in the EU - statistics              0.9977  \n",
       "3   Norway-EU - international trade in goods stati...              0.9976  \n",
       "4   Digital economy and society statistics - house...              0.8063  \n",
       "5   Water supply, sewerage, waste management and r...              0.9989  \n",
       "6   Main place of work and commuting time - statis...              0.9493  \n",
       "7   Accidents at work - statistics by economic act...              0.9993  \n",
       "8     Social participation and integration statistics              0.8624  \n",
       "9                             Asylum quarterly report              0.9915  \n",
       "10       Tourism statistics - winter season occupancy              0.9937  \n",
       "11                       Learning mobility statistics              0.9994  \n",
       "12     Government finance statistics - quarterly data              0.9792  \n",
       "13                              SDG 15 - Life on land              0.9990  \n",
       "14  European Neighbourhood Policy - East - economi...              0.8798  \n",
       "15  Labour market slack – annual statistics on unm...              0.7435  \n",
       "16               Cancer statistics - specific cancers              0.9990  \n",
       "17            Maritime freight and vessels statistics              0.9985  \n",
       "18  Quality of life indicators - economic security...              0.9991  \n",
       "19                  Employment - quarterly statistics              0.9978  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_repr_document = sent_topics_sorteddf_mallet\n",
    "most_repr_document = pd.concat([pd.Series(topic_names,name='Topic interpretation'), most_repr_document],axis=1)\n",
    "most_repr_document.rename(columns={'Text title':'Most representative article'},inplace=True) \n",
    "most_repr_document = most_repr_document[['Topic_Num','Topic interpretation','Topic Keywords','Most representative article','Topic_Perc_Contrib']]\n",
    "most_repr_document"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Outputs.\n",
    "***\n",
    "\n",
    "A first output in dataframe _topics_terms_df_ is for inspection only: a 20 x 1827 matrix with the distribution of terms per topic. The row sums of this matrix are ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 1827)\n",
      "                                           Topic Name     accid    actual  \\\n",
      "0                              Population projections  0.000004  0.000290   \n",
      "1                                 Products and prices  0.000002  0.002294   \n",
      "2   Energy production, consumption and the environ...  0.000005  0.000604   \n",
      "3                             EU exports to the world  0.000003  0.000003   \n",
      "4                              Internet in households  0.000006  0.000385   \n",
      "5                        Regions and economic sectors  0.000003  0.000054   \n",
      "6                               Employment and health  0.000003  0.000003   \n",
      "7                                   Accidents at work  0.057909  0.000133   \n",
      "8                         Participation in activities  0.000005  0.000005   \n",
      "9                                         Citizenship  0.000005  0.000482   \n",
      "10                   Tourist spending and nights stay  0.000005  0.000005   \n",
      "11                                          Education  0.000004  0.000004   \n",
      "12             Social expenditure and social benefits  0.000003  0.000989   \n",
      "13         Water resouces and sustainable development  0.000002  0.000525   \n",
      "14                        Enterprises and investments  0.000003  0.001251   \n",
      "15                Unemployment in candidate countries  0.000004  0.000004   \n",
      "16                   Health in the migrant population  0.000003  0.000003   \n",
      "17                  Transport freights and passengers  0.000005  0.000005   \n",
      "18                     Households incomes and poverty  0.000002  0.000183   \n",
      "19                 Employment in the young population  0.000003  0.000003   \n",
      "\n",
      "       addit  advertis     after     agenc  agricultur   airport  although  \\\n",
      "0   0.001547  0.000004  0.000970  0.000004    0.000004  0.000004  0.002291   \n",
      "1   0.001066  0.000002  0.003888  0.000002    0.014840  0.000002  0.002434   \n",
      "2   0.000548  0.000005  0.001240  0.000661    0.000140  0.000005  0.000942   \n",
      "3   0.000003  0.000003  0.000962  0.000003    0.000189  0.000003  0.000254   \n",
      "4   0.001437  0.004419  0.000006  0.000006    0.000006  0.000006  0.001813   \n",
      "5   0.000154  0.000003  0.000288  0.000003    0.000202  0.000003  0.001028   \n",
      "6   0.001471  0.000167  0.000295  0.000003    0.000682  0.000003  0.001266   \n",
      "7   0.001485  0.000005  0.000005  0.000781    0.010321  0.000005  0.001383   \n",
      "8   0.000005  0.000005  0.000005  0.000005    0.000005  0.000005  0.000860   \n",
      "9   0.000499  0.000005  0.000397  0.000005    0.000005  0.000099  0.000934   \n",
      "10  0.000676  0.001773  0.000005  0.002590    0.000005  0.000005  0.001381   \n",
      "11  0.000597  0.000004  0.000342  0.000004    0.000642  0.000004  0.002347   \n",
      "12  0.000657  0.000003  0.001502  0.000003    0.000003  0.000003  0.000932   \n",
      "13  0.003704  0.000002  0.000829  0.001018    0.005700  0.000002  0.001757   \n",
      "14  0.000353  0.000003  0.001755  0.000003    0.000003  0.000003  0.005076   \n",
      "15  0.002146  0.000004  0.001535  0.000004    0.000004  0.000004  0.004012   \n",
      "16  0.001056  0.000003  0.001101  0.000003    0.000003  0.000003  0.003032   \n",
      "17  0.000303  0.000005  0.002844  0.000005    0.000555  0.006361  0.001454   \n",
      "18  0.000305  0.000002  0.003880  0.000002    0.000002  0.000002  0.001613   \n",
      "19  0.001090  0.000003  0.003849  0.000003    0.000003  0.000003  0.000199   \n",
      "\n",
      "    ...   commerc  footwear  accessori  broadcast   newspap  occasion  \\\n",
      "0   ...  0.000004  0.001214   0.000004   0.000004  0.000004  0.000004   \n",
      "1   ...  0.000002  0.000002   0.000002   0.000002  0.000002  0.000110   \n",
      "2   ...  0.000005  0.000005   0.000005   0.000005  0.000005  0.000005   \n",
      "3   ...  0.000003  0.000003   0.000583   0.000003  0.000058  0.000003   \n",
      "4   ...  0.009376  0.000006   0.001533   0.001875  0.005464  0.000006   \n",
      "5   ...  0.000003  0.000003   0.000003   0.000351  0.000003  0.000003   \n",
      "6   ...  0.000003  0.000003   0.000003   0.000003  0.000003  0.000255   \n",
      "7   ...  0.000005  0.000005   0.000005   0.000216  0.000005  0.000797   \n",
      "8   ...  0.000005  0.000005   0.000005   0.000005  0.000005  0.000005   \n",
      "9   ...  0.000005  0.000005   0.000005   0.000005  0.000005  0.000005   \n",
      "10  ...  0.000005  0.001397   0.000005   0.000005  0.000005  0.000005   \n",
      "11  ...  0.000004  0.000004   0.000004   0.000004  0.000004  0.000004   \n",
      "12  ...  0.000003  0.000003   0.000003   0.000003  0.000003  0.000149   \n",
      "13  ...  0.000114  0.000002   0.000002   0.000002  0.000002  0.000002   \n",
      "14  ...  0.000003  0.000003   0.000003   0.000003  0.000003  0.000003   \n",
      "15  ...  0.000004  0.000004   0.000004   0.000004  0.000004  0.000004   \n",
      "16  ...  0.000003  0.000003   0.000003   0.000003  0.000003  0.000003   \n",
      "17  ...  0.000005  0.000005   0.000005   0.000005  0.000005  0.000005   \n",
      "18  ...  0.000002  0.000002   0.000002   0.000002  0.000002  0.000002   \n",
      "19  ...  0.000003  0.000003   0.000003   0.000003  0.000003  0.000003   \n",
      "\n",
      "      bought     music  darussalam    tablet  \n",
      "0   0.000004  0.000004    0.000004  0.000004  \n",
      "1   0.000102  0.000002    0.000002  0.000002  \n",
      "2   0.000005  0.000005    0.000005  0.000005  \n",
      "3   0.000003  0.000003    0.001731  0.000003  \n",
      "4   0.001746  0.006586    0.000006  0.001168  \n",
      "5   0.000003  0.000173    0.000003  0.000003  \n",
      "6   0.000003  0.000003    0.000003  0.000003  \n",
      "7   0.000005  0.000157    0.000005  0.000005  \n",
      "8   0.000005  0.000005    0.000005  0.000005  \n",
      "9   0.000005  0.000005    0.000005  0.000005  \n",
      "10  0.000005  0.000005    0.000005  0.000005  \n",
      "11  0.000004  0.000004    0.000004  0.000004  \n",
      "12  0.000003  0.000003    0.000003  0.000003  \n",
      "13  0.000002  0.000002    0.000002  0.000002  \n",
      "14  0.000003  0.000003    0.000249  0.000003  \n",
      "15  0.000004  0.000004    0.000004  0.000004  \n",
      "16  0.000003  0.000003    0.000003  0.000003  \n",
      "17  0.000005  0.000005    0.000005  0.000005  \n",
      "18  0.000002  0.000002    0.000002  0.000002  \n",
      "19  0.000003  0.000003    0.000003  0.000003  \n",
      "\n",
      "[20 rows x 1828 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "topics_terms_mat = lda_model.get_topics()\n",
    "print(topics_terms_mat.shape)\n",
    "topics_terms_df = pd.DataFrame()\n",
    "for topic_id in range(num_topics):\n",
    "    topics_terms_df = topics_terms_df.append(pd.Series(topics_terms_mat[topic_id,:].transpose()), ignore_index=True)           \n",
    "topics_terms_df.rename(columns=id2word, inplace=True)\n",
    "topics_terms_df.insert(0,'Topic Name',topic_names)\n",
    "print(topics_terms_df)\n",
    "topics_terms_df.to_csv('topics_terms_df_SE.write_csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.0000001 , 0.99999994, 1.0000001 , 0.9999999 ,\n",
       "       0.99999994, 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 0.9999999 , 0.9999999 , 1.0000001 , 1.        ,\n",
       "       1.0000001 , 1.0000001 , 0.9999999 , 0.9999999 , 0.99999994],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.sum(lda_model.get_topics(),axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next output is for **insertion into the database**. It is the previous matrix in (row,column,value) format, in dataframe _tm_values_, exported to file **_tm_values.csv_** (delimiter=semicolon). All indices are zero-based."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Topic id Term id Probability\n",
      "0            0       0  0.00000379\n",
      "1            0       1  0.00029020\n",
      "2            0       2  0.00154713\n",
      "3            0       3  0.00000379\n",
      "4            0       4  0.00097013\n",
      "...        ...     ...         ...\n",
      "36535       19    1822  0.00000325\n",
      "36536       19    1823  0.00000325\n",
      "36537       19    1824  0.00000325\n",
      "36538       19    1825  0.00000325\n",
      "36539       19    1826  0.00000325\n",
      "\n",
      "[36540 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "pd.options.display.float_format = '{:.8f}'.format\n",
    "tm_values = pd.DataFrame(columns=['Topic id','Term id','Probability'])\n",
    "count = -1\n",
    "for i in range(topics_terms_mat.shape[0]):\n",
    "    for j in range(topics_terms_mat.shape[1]):\n",
    "        count += 1\n",
    "        tm_values.loc[count,'Topic id']=i\n",
    "        tm_values.loc[count,'Term id']=j\n",
    "        tm_values.loc[count,'Probability']=topics_terms_mat[i,j]\n",
    "\n",
    "print(tm_values)\n",
    "#pd.options.display.float_format = None\n",
    "tm_values.to_csv('tm_values_SE.csv',sep=';',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframe _tm_topics_, exported into file **_tm_topics.csv_** (delimiter=semicolon) links the _tm_values_ with the topics information, with the zero-based _Topic id_ key. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Topic id                                     Topic keywords  \\\n",
      "0          0  popul, peopl, older, birth, project, chang, wo...   \n",
      "1          1  product, price, million, quarter, agricultur, ...   \n",
      "2          2  energi, consumpt, product, electr, emiss, rene...   \n",
      "3          3  billion, export, trade, china, partner, millio...   \n",
      "4          4  household, cultur, internet, adult, onlin, poi...   \n",
      "5          5  region, sector, economi, employ, person, finan...   \n",
      "6          6  person, group, emploi, health, children, women...   \n",
      "7          7  activ, accid, servic, fatal, sector, emploi, p...   \n",
      "8          8  peopl, activ, popul, particip, adult, famili, ...   \n",
      "9          9  citizen, foreign, resid, nation, permit, first...   \n",
      "10        10  servic, tourist, night, tourism, spent, accomm...   \n",
      "11        11  tertiari, student, secondari, graduat, pupil, ...   \n",
      "12        12  expenditur, benefit, govern, financi, social, ...   \n",
      "13        13  water, european, develop, target, measur, sust...   \n",
      "14        14  enterpris, economi, pension, turnov, invest, i...   \n",
      "15        15  unemploy, candid, potenti, turkei, outsid, lab...   \n",
      "16        16  death, inhabit, immigr, cancer, diseas, nativ,...   \n",
      "17        17  transport, passeng, freight, million, regist, ...   \n",
      "18        18  incom, household, poverti, peopl, social, popu...   \n",
      "19        19  employ, young, peopl, women, point, labour, qu...   \n",
      "\n",
      "                                           Topic name  \n",
      "0                              Population projections  \n",
      "1                                 Products and prices  \n",
      "2   Energy production, consumption and the environ...  \n",
      "3                             EU exports to the world  \n",
      "4                              Internet in households  \n",
      "5                        Regions and economic sectors  \n",
      "6                               Employment and health  \n",
      "7                                   Accidents at work  \n",
      "8                         Participation in activities  \n",
      "9                                         Citizenship  \n",
      "10                   Tourist spending and nights stay  \n",
      "11                                          Education  \n",
      "12             Social expenditure and social benefits  \n",
      "13         Water resouces and sustainable development  \n",
      "14                        Enterprises and investments  \n",
      "15                Unemployment in candidate countries  \n",
      "16                   Health in the migrant population  \n",
      "17                  Transport freights and passengers  \n",
      "18                     Households incomes and poverty  \n",
      "19                 Employment in the young population  \n"
     ]
    }
   ],
   "source": [
    "tm_topics = sent_topics_sorteddf_mallet.copy()\n",
    "tm_topics.rename(columns={'Topic_Num':'Topic id'},inplace=True)\n",
    "tm_topics.drop(columns=['Topic_Perc_Contrib','Text title'],inplace=True)\n",
    "tm_topics = pd.concat([tm_topics, pd.Series(topic_names)],ignore_index=True,axis=1)\n",
    "tm_topics.columns = ['Topic id', 'Topic keywords', 'Topic name']\n",
    "print(tm_topics)\n",
    "tm_topics.to_csv('tm_topics_SE.csv',sep=';',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, dataframe _tm_terms_, exported into file **_tm_terms.csv_** (delimiter=semicolon) which links the _tm_values_ with the terms information, with the zero-based _Term id_ key. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Term id   Term name\n",
      "0           0       accid\n",
      "1           1      actual\n",
      "2           2       addit\n",
      "3           3    advertis\n",
      "4           4       after\n",
      "...       ...         ...\n",
      "1822     1822    occasion\n",
      "1823     1823      bought\n",
      "1824     1824       music\n",
      "1825     1825  darussalam\n",
      "1826     1826      tablet\n",
      "\n",
      "[1827 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "tm_terms = pd.DataFrame([id2word[t_id] for t_id in id2word],columns=['Term name'])\n",
    "tm_terms.insert(0,value=range(len(id2word)),column='Term id') \n",
    "print(tm_terms)\n",
    "tm_terms.to_csv('tm_terms_SE.csv',sep=';',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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