Topic Modelling with Gensim_v5.ipynb

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   "source": [
    "# Topic modelling with Gensim : the LDA algorithm\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"
   ]
  },
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      "[!] As of spaCy v3.0, shortcuts like 'en' are deprecated. Pleaseuse the full\n",
      "pipeline package name 'en_core_web_sm' instead.\n",
      "[+] Download and installation successful\n",
      "You can now load the package via spacy.load('en_core_web_sm')\n"
     ]
    }
   ],
   "source": [
    "import gensim\n",
    "import pandas as pd\n",
    "\n",
    "import spacy\n",
    "import sys\n",
    "\n",
    "## Run once this chunk with the command below active. Then comment it out and run the notebook\n",
    "!{sys.executable} -m spacy download en\n",
    "\n",
    "nlp = spacy.load(\"en_core_web_sm\", disable=['parser', 'ner'])\n",
    "\n",
    "import nltk\n",
    "import re\n",
    "import pprint\n",
    "import os\n",
    "import sys\n",
    "\n",
    "\n",
    "from gensim import corpora\n",
    "\n",
    "#import nltk\n",
    "#from nltk import sent_tokenize\n",
    "#from nltk.corpus import stopwords\n",
    "\n",
    "##nltk.download('stopwords')\n",
    "##stop_words = stopwords.words('english')\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 two files exported from the database which contain the SE Glossary articles definitions and their titles.\n",
    "* In later versions, the **corresponding tables will be directly exported from the database**.\n",
    "* Merge by_id_ and discard records with duplicate titles and/or definitions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>definition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Arrival of tourist at a tourist accommodation ...</td>\n",
       "      <td>See      arrival disambiguation page     fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Nights spent</td>\n",
       "      <td>A     night spent    or     tourism night   ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Supply side tourism statistics</td>\n",
       "      <td>Regulation (EU) No 692/2011 of 6 July 2011 e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Tourist</td>\n",
       "      <td>For     tourism    statistics, a     travell...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Asia-Europe Meeting (ASEM)</td>\n",
       "      <td>The     Asia-Europe Meeting    , abbreviated...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>2879</td>\n",
       "      <td>Statistical territory</td>\n",
       "      <td>The     statistical territory    of the     ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>2881</td>\n",
       "      <td>Statistical indicator</td>\n",
       "      <td>A     statistical indicator    is the repres...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>2885</td>\n",
       "      <td>Tourism industries</td>\n",
       "      <td>For the article     Tourism industries - eco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>2891</td>\n",
       "      <td>Sawnwood</td>\n",
       "      <td>Sawnwood    is wood that has been produced ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>2896</td>\n",
       "      <td>LeaMickatest</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1284 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                              title  \\\n",
       "0        1  Arrival of tourist at a tourist accommodation ...   \n",
       "1        3                                    Nights spent      \n",
       "2        4                  Supply side tourism statistics      \n",
       "3        5                                         Tourist      \n",
       "4        7                         Asia-Europe Meeting (ASEM)   \n",
       "...    ...                                                ...   \n",
       "1279  2879                              Statistical territory   \n",
       "1280  2881                              Statistical indicator   \n",
       "1281  2885                                 Tourism industries   \n",
       "1282  2891                                           Sawnwood   \n",
       "1283  2896                                       LeaMickatest   \n",
       "\n",
       "                                             definition  \n",
       "0        See      arrival disambiguation page     fo...  \n",
       "1       A     night spent    or     tourism night   ...  \n",
       "2       Regulation (EU) No 692/2011 of 6 July 2011 e...  \n",
       "3       For     tourism    statistics, a     travell...  \n",
       "4       The     Asia-Europe Meeting    , abbreviated...  \n",
       "...                                                 ...  \n",
       "1279    The     statistical territory    of the     ...  \n",
       "1280    A     statistical indicator    is the repres...  \n",
       "1281    For the article     Tourism industries - eco...  \n",
       "1282     Sawnwood    is wood that has been produced ...  \n",
       "1283                                               test  \n",
       "\n",
       "[1284 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1= pd.read_csv(\"ESTAT_dat_concepts_2021_04_08.csv\",sep=\";\")\n",
    "dat2= pd.read_csv(\"ESTAT_dat_link_info_2021_04_08.csv\",sep=\";\")\n",
    "Gloss_concepts = pd.merge(dat1,dat2,on=['id'])\n",
    "del(dat1,dat2)\n",
    "\n",
    "Gloss_concepts = Gloss_concepts[['id','title','definition']]\n",
    "\n",
    "Gloss_concepts = Gloss_concepts.drop_duplicates(subset=[\"definition\"])\n",
    "Gloss_concepts = Gloss_concepts.dropna(axis=0,subset=[\"definition\"])\n",
    "Gloss_concepts = Gloss_concepts.drop_duplicates(subset=[\"title\"])\n",
    "Gloss_concepts = Gloss_concepts.dropna(axis=0,subset=[\"title\"])\n",
    "\n",
    "Gloss_concepts.reset_index(drop=True, inplace=True)\n",
    "Gloss_concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Pre-processing input data (cont).\n",
    "***\n",
    "\n",
    "Next we tokenize the texts - definitions in the articles, delete stop words, apply Porter's stemmer, select final tokens  \n",
    "with minimum length 5 and also convert everything to lowercase and drop accents. \n",
    "\n",
    "The result, _texts_ is a nested list with 1284 outer elements corresponding to the records in the dataframe _Gloss_concepts_.  \n",
    "The inner lists contain the tokens for each text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 10 texts: \n",
      " [['arriv', 'disambigu', 'arriv', 'within', 'context', 'european', 'union', 'tourism', 'statist', 'arriv', 'defin', 'tourist', 'arriv', 'tourist', 'accommod', 'establish', 'accommod', 'accommod', 'scope', 'tourism', 'regul', 'howev', 'collect', 'tourist', 'resid', 'resid', 'countri', 'establish', 'locat', 'limit', 'appli', 'children', 'count', 'adult', 'overnight', 'children', 'charg'], ['night', 'spent', 'tourism', 'night', 'overnight', 'night', 'guest', 'tourist', 'resid', 'resid', 'actual', 'spend', 'sleep', 'tourist', 'accommod', 'establish', 'accommod'], ['regul', 'establish', 'common', 'framework', 'systemat', 'develop', 'product', 'dissemin', 'european', 'statist', 'tourism', 'purpos', 'member', 'state', 'transmit', 'harmonis', 'statist', 'tourism', 'suppli', 'demand', 'tourism', 'suppli', 'statist', 'accommod', 'statist', 'accommod', 'capac', 'occup', 'tourist', 'accommod', 'establish', 'report', 'countri', 'resid', 'resid', 'guest', 'tourism', 'demand', 'statist', 'particip', 'tourism', 'resid', 'report', 'countri', 'characterist', 'tourism', 'resid', 'report', 'countri', 'characterist', 'visit', 'resid', 'report', 'countri', 'first', 'refer'], ['tourism', 'statist', 'travel', 'differ', 'geograph', 'locat', 'purpos', 'durat', 'visitor', 'travel', 'destin', 'outsid', 'usual', 'environ', 'purpos', 'leisur', 'person', 'purpos', 'emploi', 'resid', 'entiti', 'countri', 'place', 'visit', 'these', 'taken', 'visitor', 'qualifi', 'tourism', 'visitor', 'classifi', 'tourist', 'overnight', 'visitor', 'includ', 'overnight', 'visitor', 'excursionist', 'includ', 'overnight'], ['europ', 'abbrevi', 'inform', 'process', 'dialogu', 'bring', 'countri', 'europ', 'oceania', 'follow', 'partner', 'european', 'union', 'member', 'state', 'member', 'norwai', 'switzerland', 'asean', 'member', 'brunei', 'darussalam', 'cambodia', 'indonesia', 'malaysia', 'myanmar', 'philippin', 'singapor', 'thailand', 'vietnam', 'asean', 'secretariat', 'partner', 'australia', 'bangladesh', 'china', 'india', 'japan', 'kazakhstan', 'republ', 'korea', 'mongolia', 'zealand', 'pakistan', 'russian', 'feder'], ['associ', 'south', 'asian', 'nation', 'abbrevi', 'asean', 'establish', 'august', 'bangkok', 'thailand', 'acceler', 'econom', 'growth', 'social', 'progress', 'cultur', 'develop', 'promot', 'stabil', 'region', 'world', 'econom', 'region', 'outsid', 'asean', 'orang', 'member', 'state', 'asean', 'brunei', 'brunei', 'darussalam', 'cambodia', 'kingdom', 'cambodia', 'indonesia', 'republ', 'indonesia', 'peopl', 'democrat', 'republ', 'malaysia', 'myanmar', 'burma', 'myanmar', 'philippin', 'republ', 'philippin', 'singapor', 'republ', 'singapor', 'thailand', 'kingdom', 'thailand', 'vietnam', 'socialist', 'republ', 'vietnam'], ['european', 'trade', 'associ', 'abbrevi', 'intergovernment', 'organis', 'establish', 'seven', 'european', 'countri', 'promot', 'trade', 'econom', 'integr', 'benefit', 'member', 'state', 'origin', 'signatori', 'norwai', 'switzerland', 'withdrew', 'european', 'union', 'finland', 'associ', 'member', 'member', 'iceland', 'liechtenstein', 'current', 'member', 'state', 'iceland', 'liechtenstein', 'norwai', 'switzerland', 'associ', 'respons', 'manag', 'trade', 'countri', 'particip', 'european', 'econom', 'includ', 'european', 'union', 'countri', 'iceland', 'liechtentstein', 'norwai', 'switzerland', 'worldwid', 'network', 'trade', 'agreement'], ['european', 'union', 'abbrevi', 'econom', 'polit', 'union', 'european', 'countri', 'establish', 'novemb', 'treati', 'european', 'union', 'maastricht', 'treati', 'decemb', 'member', 'state', 'belgium', 'denmark', 'germani', 'ireland', 'greec', 'spain', 'franc', 'itali', 'luxembourg', 'netherland', 'portug', 'kingdom', 'januari', 'member', 'state', 'austria', 'finland', 'sweden', 'countri', 'cypru', 'czechia', 'estonia', 'hungari', 'latvia', 'lithuania', 'malta', 'poland', 'slovenia', 'slovakia', 'januari', 'bulgaria', 'romania', 'member', 'latest', 'countri', 'acced', 'croatia', 'januari', 'kingdom', 'member', 'state', 'follow', 'alphabet', 'order', 'second', 'austria', 'estonia', 'itali', 'portug', 'belgium', 'finland', 'latvia', 'romania', 'bulgaria', 'franc', 'lithuania', 'slovakia', 'croatia', 'germani', 'luxembourg', 'slovenia', 'cypru', 'greec', 'malta', 'spain', 'czechia', 'hungari', 'netherland', 'sweden', 'denmark', 'ireland', 'poland'], ['acronym', 'northeast', 'south', 'geograph', 'group', 'countri', 'partner', 'europ', 'partner', 'australia', 'bangladesh', 'china', 'india', 'japan', 'kazakhstan', 'republ', 'korea', 'mongolia', 'zealand', 'pakistan', 'russian', 'feder'], ['arrear', 'credit', 'repay', 'statist', 'indic', 'refer', 'arrear', 'payment', 'repay', 'credit', 'household', 'credit', 'encompass', 'commerci', 'credit', 'schedul', 'repay', 'except', 'mortgag', 'dwell', 'includ', 'overdraft', 'facil', 'credit', 'store', 'repay', 'borrow', 'friend', 'inform', 'credit', 'these', 'arrear', 'account', 'credit', 'mortgag', 'repay', 'schedul', 'month', 'financi', 'reason', 'total', 'current', 'arrear', 'includ', 'household', 'current', 'schedul', 'relat', 'payment', 'compris', 'mortgag', 'repay', 'dwell', 'water', 'electr', 'other', 'credit', 'repay', 'account', 'other', 'mortgag', 'repay', 'dwell', 'purchas', 'instal', 'similar', 'order', 'catalogu', 'financ', 'minimum', 'credit', 'store', 'repay', 'includ', 'other', 'household', 'health', 'cover', 'relat']]\n",
      "\n",
      "Total texts:  1284 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Tokenize texts and clean-up text.\n",
    "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 = remove_stopwords(sentence) ## remove stop words\n",
    "        \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",
    "        #p.stem_sentence(sentence)\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # to lower + deacc=True removes punctuations\n",
    "        \n",
    "texts = list(sent_to_words(Gloss_concepts['definition']))\n",
    "print('\\nFirst 10 texts: \\n',texts[:10])\n",
    "print('\\nTotal texts: ',len(texts),'\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Creation of corpus and terms frequencies.\n",
    "***\n",
    "\n",
    "Next:\n",
    "* we create a vocabulary from _texts_ with name _id2word_. This has 7258 unique tokens. \n",
    "* we filter-out words which appear in less than 2% of the texts or in more than 80% of the texts. The result has 352 tokens.\n",
    "* we create a mapping with name _corpus_ of the texts into lists with tuples: (word id, frequency in each text) - Bag of Words (BOW) format.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary(352 unique tokens: ['appli', 'charg', 'collect', 'context', 'count']...) \n",
      "\n",
      "First 10 texts in BOW :\n",
      "\n",
      "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 2), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 2), (14, 1), (15, 1)], [(7, 1), (13, 2), (16, 1)], [(5, 4), (7, 2), (8, 1), (12, 1), (13, 5), (14, 5), (17, 2), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 4), (27, 1), (28, 2)], [(5, 1), (11, 1), (13, 1), (14, 1), (24, 3), (29, 1), (30, 1), (31, 1), (32, 1), (33, 1), (34, 1), (35, 2), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1)], [(5, 1), (8, 1), (15, 1), (21, 3), (27, 1), (42, 1), (43, 2), (44, 1), (45, 1), (46, 1), (47, 1)], [(7, 1), (19, 1), (21, 1), (27, 1), (36, 1), (42, 1), (48, 1), (49, 2), (50, 1), (51, 1), (52, 1), (53, 2), (54, 1), (55, 1)], [(5, 3), (7, 1), (8, 5), (15, 2), (21, 4), (22, 1), (27, 2), (35, 1), (42, 1), (46, 3), (48, 3), (49, 2), (56, 1), (57, 1), (58, 1), (59, 1), (60, 1), (61, 1), (62, 1), (63, 1), (64, 1), (65, 4)], [(5, 3), (7, 1), (8, 3), (15, 3), (21, 4), (27, 3), (42, 1), (44, 1), (49, 1), (66, 3), (67, 1), (68, 1), (69, 2)], [(5, 1), (34, 1), (43, 1), (70, 1)], [(14, 1), (25, 1), (35, 3), (40, 1), (45, 1), (58, 2), (67, 1), (71, 2), (72, 1), (73, 1), (74, 1), (75, 1), (76, 1), (77, 1), (78, 1), (79, 1), (80, 3), (81, 1), (82, 1), (83, 3), (84, 2), (85, 1), (86, 1), (87, 2), (88, 1), (89, 1), (90, 1)]]\n",
      "\n",
      "Total texts:  1284\n"
     ]
    }
   ],
   "source": [
    "#Create Dictionary\n",
    "id2word = corpora.Dictionary(texts) #Gensim creates a unique id for each word in the document. \n",
    "id2word.filter_extremes(no_below=0.02*len(texts), no_above=0.8)\n",
    "\n",
    "print(id2word,'\\n')\n",
    "\n",
    "## The produced corpus shown above is a mapping of (word_id, word_frequency).\n",
    "\n",
    "#Alternatively:\n",
    "corpus = [id2word.doc2bow(text) for text in texts] #corpus package automatically creates a set of corpus reader instances that can be used to access the corpora in the NLTK data package.\n",
    "\n",
    "print('First 10 texts in BOW :\\n')\n",
    "print(corpus[:10])\n",
    "print('\\nTotal texts: ',len(corpus))\n",
    "\n",
    "#id2word.dfs\n",
    "\n",
    "#Human readable format of corpus (term-frequency)\n",
    "#[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Dominant topic in each document.\n",
    "***\n",
    "Αt this stage we build the  _lda_model_, through which the dominant topic for each document will be extracted and we show the weight of the topic and the keywords. We define a dataframe _df_topic_sents_keywords_  in order to store these dominant 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 (topic, contribution). We sort 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",
    "\n",
    "The parameters in the call of the function _ldamodel.LdaModel()_ and the number of topics were selected by trial and error.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#What is the Dominant topic and its percentage contribution in each document.\n",
    "\n",
    "def format_topics_sentences(ldamodel=None, corpus=corpus):\n",
    "    # Init output\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] if ldamodel.per_word_topics else row_list ## ldamodel.per_word_topics is False          \n",
    "        #print(row)\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, Gloss_concepts['id'],Gloss_concepts['title']], axis=1)\n",
    "    return(sent_topics_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=20, \n",
    "                                           random_state=100,\n",
    "                                           update_every=0,\n",
    "                                           passes=10,\n",
    "                                           alpha='asymmetric',\n",
    "                                           iterations=500,\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text id</th>\n",
       "      <th>Text title</th>\n",
       "      <th>Text tokenized definition</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>1</td>\n",
       "      <td>Arrival of tourist at a tourist accommodation ...</td>\n",
       "      <td>[arriv, disambigu, arriv, within, context, eur...</td>\n",
       "      <td>13</td>\n",
       "      <td>0.6940</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Nights spent</td>\n",
       "      <td>[night, spent, tourism, night, overnight, nigh...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.4377</td>\n",
       "      <td>resid, account, nation, economi, countri, buil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Supply side tourism statistics</td>\n",
       "      <td>[regul, establish, common, framework, systemat...</td>\n",
       "      <td>16</td>\n",
       "      <td>0.5611</td>\n",
       "      <td>resid, account, nation, economi, countri, buil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Tourist</td>\n",
       "      <td>[tourism, statist, travel, differ, geograph, l...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.4549</td>\n",
       "      <td>includ, agricultur, water, cover, plant, exclu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Asia-Europe Meeting (ASEM)</td>\n",
       "      <td>[europ, abbrevi, inform, process, dialogu, bri...</td>\n",
       "      <td>13</td>\n",
       "      <td>0.9355</td>\n",
       "      <td>european, countri, state, member, union, stati...</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>1279</th>\n",
       "      <td>2879</td>\n",
       "      <td>Statistical territory</td>\n",
       "      <td>[statist, territori, european, union, context,...</td>\n",
       "      <td>13</td>\n",
       "      <td>0.8498</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>2881</td>\n",
       "      <td>Statistical indicator</td>\n",
       "      <td>[statist, indic, represent, statist, specifi, ...</td>\n",
       "      <td>17</td>\n",
       "      <td>0.3706</td>\n",
       "      <td>health, european, state, member, survei, regul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>2885</td>\n",
       "      <td>Tourism industries</td>\n",
       "      <td>[articl, tourism, industri, econom, analysi, t...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.5052</td>\n",
       "      <td>activ, statist, servic, product, econom, indus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>2891</td>\n",
       "      <td>Sawnwood</td>\n",
       "      <td>[sawnwood, produc, lengthwai, profil, process,...</td>\n",
       "      <td>15</td>\n",
       "      <td>0.7570</td>\n",
       "      <td>product, energi, innov, process, servic, inclu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>2896</td>\n",
       "      <td>LeaMickatest</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1246</td>\n",
       "      <td>govern, institut, public, sector, countri, ser...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1284 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Text id                                         Text title  \\\n",
       "0           1  Arrival of tourist at a tourist accommodation ...   \n",
       "1           3                                    Nights spent      \n",
       "2           4                  Supply side tourism statistics      \n",
       "3           5                                         Tourist      \n",
       "4           7                         Asia-Europe Meeting (ASEM)   \n",
       "...       ...                                                ...   \n",
       "1279     2879                              Statistical territory   \n",
       "1280     2881                              Statistical indicator   \n",
       "1281     2885                                 Tourism industries   \n",
       "1282     2891                                           Sawnwood   \n",
       "1283     2896                                       LeaMickatest   \n",
       "\n",
       "                              Text tokenized definition  Dominant_Topic  \\\n",
       "0     [arriv, disambigu, arriv, within, context, eur...              13   \n",
       "1     [night, spent, tourism, night, overnight, nigh...              16   \n",
       "2     [regul, establish, common, framework, systemat...              16   \n",
       "3     [tourism, statist, travel, differ, geograph, l...               3   \n",
       "4     [europ, abbrevi, inform, process, dialogu, bri...              13   \n",
       "...                                                 ...             ...   \n",
       "1279  [statist, territori, european, union, context,...              13   \n",
       "1280  [statist, indic, represent, statist, specifi, ...              17   \n",
       "1281  [articl, tourism, industri, econom, analysi, t...              10   \n",
       "1282  [sawnwood, produc, lengthwai, profil, process,...              15   \n",
       "1283                                                 []               0   \n",
       "\n",
       "      Perc_Contribution                                     Topic_Keywords  \n",
       "0                0.6940  european, countri, state, member, union, stati...  \n",
       "1                0.4377  resid, account, nation, economi, countri, buil...  \n",
       "2                0.5611  resid, account, nation, economi, countri, buil...  \n",
       "3                0.4549  includ, agricultur, water, cover, plant, exclu...  \n",
       "4                0.9355  european, countri, state, member, union, stati...  \n",
       "...                 ...                                                ...  \n",
       "1279             0.8498  european, countri, state, member, union, stati...  \n",
       "1280             0.3706  health, european, state, member, survei, regul...  \n",
       "1281             0.5052  activ, statist, servic, product, econom, indus...  \n",
       "1282             0.7570  product, energi, innov, process, servic, inclu...  \n",
       "1283             0.1246  govern, institut, public, sector, countri, ser...  \n",
       "\n",
       "[1284 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "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 = {'id':'Text id','title':'Text title'}, inplace = True)\n",
    "df_topic_sents_keywords = df_topic_sents_keywords[['Text id','Text title','Text tokenized definition','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": [
    "### 6. Most representative document for each topic.\n",
    "***\n",
    "\n",
    "We want to find the documents that best represent a given topic, to have a complete analysis.This code receives the most exemplary document for each topic. We create the _sent_topics_sorteddf_mallet_ and these Topic Keywords are presented below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "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 id','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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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 id</th>\n",
       "      <th>Text title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.9882</td>\n",
       "      <td>govern, institut, public, sector, countri, ser...</td>\n",
       "      <td>989</td>\n",
       "      <td>Central government</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.9743</td>\n",
       "      <td>includ, servic, refer, other, section, group, ...</td>\n",
       "      <td>1162</td>\n",
       "      <td>Hotels and similar accommodation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.9746</td>\n",
       "      <td>market, product, expenditur, protect, capit, p...</td>\n",
       "      <td>2806</td>\n",
       "      <td>Fixed capital</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.9822</td>\n",
       "      <td>includ, agricultur, water, cover, plant, exclu...</td>\n",
       "      <td>100</td>\n",
       "      <td>Cattle housing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.9641</td>\n",
       "      <td>enterpris, activ, refer, inform, agricultur, s...</td>\n",
       "      <td>413</td>\n",
       "      <td>Enterprise survival</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.9552</td>\n",
       "      <td>activ, technolog, research, develop, resourc, ...</td>\n",
       "      <td>574</td>\n",
       "      <td>International standard classification of ed...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.9969</td>\n",
       "      <td>person, applic, protect, countri, decis, proce...</td>\n",
       "      <td>613</td>\n",
       "      <td>Asylum decision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.9736</td>\n",
       "      <td>number, weight, averag, equival, measur, calcu...</td>\n",
       "      <td>1243</td>\n",
       "      <td>Weighted average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.9159</td>\n",
       "      <td>product, gross, consumpt, input, output, accou...</td>\n",
       "      <td>1233</td>\n",
       "      <td>Goods and services account</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.9845</td>\n",
       "      <td>price, index, person, emploi, period, labour, ...</td>\n",
       "      <td>474</td>\n",
       "      <td>Employee - SBS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>activ, statist, servic, product, econom, indus...</td>\n",
       "      <td>1218</td>\n",
       "      <td>Agricultural holding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>household, materi, survei, activ, includ, pers...</td>\n",
       "      <td>280</td>\n",
       "      <td>Material flow indicators</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.9849</td>\n",
       "      <td>transport, vehicl, countri, enterpris, place, ...</td>\n",
       "      <td>1761</td>\n",
       "      <td>Road freight transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.9833</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "      <td>436</td>\n",
       "      <td>Country codes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.9880</td>\n",
       "      <td>incom, financi, social, account, asset, contri...</td>\n",
       "      <td>872</td>\n",
       "      <td>Social contributions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.9848</td>\n",
       "      <td>product, energi, innov, process, servic, inclu...</td>\n",
       "      <td>2127</td>\n",
       "      <td>Process innovation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.9546</td>\n",
       "      <td>resid, account, nation, economi, countri, buil...</td>\n",
       "      <td>1563</td>\n",
       "      <td>Satellite account</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.9875</td>\n",
       "      <td>health, european, state, member, survei, regul...</td>\n",
       "      <td>2236</td>\n",
       "      <td>Open method of coordination (OMC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.9729</td>\n",
       "      <td>popul, death, region, urban, group, level, peo...</td>\n",
       "      <td>1699</td>\n",
       "      <td>Urban-rural typology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.9685</td>\n",
       "      <td>indic, econom, environment, employ, social, la...</td>\n",
       "      <td>1313</td>\n",
       "      <td>Coincident indicator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic_Num  Topic_Perc_Contrib  \\\n",
       "0           0              0.9882   \n",
       "1           1              0.9743   \n",
       "2           2              0.9746   \n",
       "3           3              0.9822   \n",
       "4           4              0.9641   \n",
       "5           5              0.9552   \n",
       "6           6              0.9969   \n",
       "7           7              0.9736   \n",
       "8           8              0.9159   \n",
       "9           9              0.9845   \n",
       "10         10              0.9955   \n",
       "11         11              0.9940   \n",
       "12         12              0.9849   \n",
       "13         13              0.9833   \n",
       "14         14              0.9880   \n",
       "15         15              0.9848   \n",
       "16         16              0.9546   \n",
       "17         17              0.9875   \n",
       "18         18              0.9729   \n",
       "19         19              0.9685   \n",
       "\n",
       "                                       Topic Keywords  Text id  \\\n",
       "0   govern, institut, public, sector, countri, ser...      989   \n",
       "1   includ, servic, refer, other, section, group, ...     1162   \n",
       "2   market, product, expenditur, protect, capit, p...     2806   \n",
       "3   includ, agricultur, water, cover, plant, exclu...      100   \n",
       "4   enterpris, activ, refer, inform, agricultur, s...      413   \n",
       "5   activ, technolog, research, develop, resourc, ...      574   \n",
       "6   person, applic, protect, countri, decis, proce...      613   \n",
       "7   number, weight, averag, equival, measur, calcu...     1243   \n",
       "8   product, gross, consumpt, input, output, accou...     1233   \n",
       "9   price, index, person, emploi, period, labour, ...      474   \n",
       "10  activ, statist, servic, product, econom, indus...     1218   \n",
       "11  household, materi, survei, activ, includ, pers...      280   \n",
       "12  transport, vehicl, countri, enterpris, place, ...     1761   \n",
       "13  european, countri, state, member, union, stati...      436   \n",
       "14  incom, financi, social, account, asset, contri...      872   \n",
       "15  product, energi, innov, process, servic, inclu...     2127   \n",
       "16  resid, account, nation, economi, countri, buil...     1563   \n",
       "17  health, european, state, member, survei, regul...     2236   \n",
       "18  popul, death, region, urban, group, level, peo...     1699   \n",
       "19  indic, econom, environment, employ, social, la...     1313   \n",
       "\n",
       "                                           Text title  \n",
       "0                                  Central government  \n",
       "1                 Hotels and similar accommodation     \n",
       "2                                    Fixed capital     \n",
       "3                                      Cattle housing  \n",
       "4                                 Enterprise survival  \n",
       "5      International standard classification of ed...  \n",
       "6                                  Asylum decision     \n",
       "7                                 Weighted average     \n",
       "8                       Goods and services account     \n",
       "9                                      Employee - SBS  \n",
       "10                               Agricultural holding  \n",
       "11                        Material flow indicators     \n",
       "12                          Road freight transport     \n",
       "13                                   Country codes     \n",
       "14                            Social contributions     \n",
       "15                              Process innovation     \n",
       "16                               Satellite account     \n",
       "17                  Open method of coordination (OMC)  \n",
       "18                               Urban-rural typology  \n",
       "19                               Coincident indicator  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the most representative document for each topic\n",
    "# Reset Index    \n",
    "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Format\n",
    "sent_topics_sorteddf_mallet.columns = ['Text id','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 id','Text title']]\n",
    "\n",
    "# Show\n",
    "sent_topics_sorteddf_mallet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. 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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.063*\"govern\" + 0.039*\"institut\" + 0.024*\"public\" + 0.022*\"sector\" + '\n",
      "  '0.022*\"countri\" + 0.020*\"servic\" + 0.017*\"central\" + 0.017*\"gener\" + '\n",
      "  '0.016*\"nation\" + 0.015*\"provid\"'),\n",
      " (1,\n",
      "  '0.062*\"includ\" + 0.059*\"servic\" + 0.038*\"refer\" + 0.033*\"other\" + '\n",
      "  '0.031*\"section\" + 0.031*\"group\" + 0.029*\"classif\" + 0.025*\"class\" + '\n",
      "  '0.025*\"offic\" + 0.024*\"statist\"'),\n",
      " (2,\n",
      "  '0.072*\"market\" + 0.043*\"product\" + 0.039*\"expenditur\" + 0.034*\"protect\" + '\n",
      "  '0.034*\"capit\" + 0.034*\"produc\" + 0.029*\"output\" + 0.027*\"current\" + '\n",
      "  '0.026*\"environment\" + 0.025*\"transfer\"'),\n",
      " (3,\n",
      "  '0.043*\"includ\" + 0.038*\"agricultur\" + 0.037*\"water\" + 0.036*\"cover\" + '\n",
      "  '0.030*\"plant\" + 0.028*\"exclud\" + 0.027*\"normal\" + 0.026*\"refer\" + '\n",
      "  '0.021*\"perman\" + 0.018*\"surfac\"'),\n",
      " (4,\n",
      "  '0.087*\"enterpris\" + 0.052*\"activ\" + 0.050*\"refer\" + 0.037*\"inform\" + '\n",
      "  '0.022*\"agricultur\" + 0.021*\"standard\" + 0.020*\"survei\" + 0.018*\"product\" + '\n",
      "  '0.017*\"period\" + 0.016*\"statist\"'),\n",
      " (5,\n",
      "  '0.037*\"activ\" + 0.035*\"technolog\" + 0.034*\"research\" + 0.031*\"develop\" + '\n",
      "  '0.029*\"resourc\" + 0.024*\"knowledg\" + 0.023*\"human\" + 0.021*\"perform\" + '\n",
      "  '0.018*\"manag\" + 0.017*\"standard\"'),\n",
      " (6,\n",
      "  '0.089*\"person\" + 0.085*\"applic\" + 0.047*\"protect\" + 0.042*\"countri\" + '\n",
      "  '0.039*\"decis\" + 0.031*\"procedur\" + 0.028*\"statu\" + 0.023*\"intern\" + '\n",
      "  '0.023*\"refer\" + 0.021*\"legal\"'),\n",
      " (7,\n",
      "  '0.098*\"number\" + 0.084*\"weight\" + 0.049*\"averag\" + 0.036*\"equival\" + '\n",
      "  '0.035*\"measur\" + 0.035*\"calcul\" + 0.035*\"total\" + 0.029*\"period\" + '\n",
      "  '0.025*\"ratio\" + 0.022*\"indic\"'),\n",
      " (8,\n",
      "  '0.079*\"product\" + 0.076*\"gross\" + 0.046*\"consumpt\" + 0.032*\"input\" + '\n",
      "  '0.031*\"output\" + 0.031*\"account\" + 0.029*\"balanc\" + 0.024*\"capit\" + '\n",
      "  '0.023*\"total\" + 0.022*\"measur\"'),\n",
      " (9,\n",
      "  '0.094*\"price\" + 0.061*\"index\" + 0.046*\"person\" + 0.040*\"emploi\" + '\n",
      "  '0.030*\"period\" + 0.029*\"labour\" + 0.027*\"employe\" + 0.025*\"employ\" + '\n",
      "  '0.025*\"chang\" + 0.022*\"consum\"'),\n",
      " (10,\n",
      "  '0.107*\"activ\" + 0.052*\"statist\" + 0.051*\"servic\" + 0.041*\"product\" + '\n",
      "  '0.029*\"econom\" + 0.028*\"industri\" + 0.025*\"agricultur\" + 0.020*\"classif\" + '\n",
      "  '0.017*\"level\" + 0.014*\"european\"'),\n",
      " (11,\n",
      "  '0.081*\"household\" + 0.055*\"materi\" + 0.028*\"survei\" + 0.027*\"activ\" + '\n",
      "  '0.027*\"includ\" + 0.027*\"person\" + 0.026*\"agricultur\" + 0.026*\"famili\" + '\n",
      "  '0.025*\"organ\" + 0.021*\"product\"'),\n",
      " (12,\n",
      "  '0.113*\"transport\" + 0.088*\"vehicl\" + 0.067*\"countri\" + 0.048*\"enterpris\" + '\n",
      "  '0.045*\"place\" + 0.040*\"legal\" + 0.032*\"includ\" + 0.028*\"nation\" + '\n",
      "  '0.021*\"report\" + 0.020*\"power\"'),\n",
      " (13,\n",
      "  '0.097*\"european\" + 0.075*\"countri\" + 0.064*\"state\" + 0.063*\"member\" + '\n",
      "  '0.054*\"union\" + 0.031*\"statist\" + 0.019*\"trade\" + 0.019*\"januari\" + '\n",
      "  '0.018*\"region\" + 0.017*\"territori\"'),\n",
      " (14,\n",
      "  '0.094*\"incom\" + 0.061*\"financi\" + 0.060*\"social\" + 0.041*\"account\" + '\n",
      "  '0.034*\"asset\" + 0.026*\"contribut\" + 0.024*\"household\" + 0.020*\"dispos\" + '\n",
      "  '0.019*\"employe\" + 0.019*\"transfer\"'),\n",
      " (15,\n",
      "  '0.108*\"product\" + 0.076*\"energi\" + 0.063*\"innov\" + 0.063*\"process\" + '\n",
      "  '0.035*\"servic\" + 0.031*\"includ\" + 0.024*\"improv\" + 0.021*\"technolog\" + '\n",
      "  '0.020*\"industri\" + 0.017*\"electr\"'),\n",
      " (16,\n",
      "  '0.078*\"resid\" + 0.068*\"account\" + 0.040*\"nation\" + 0.034*\"economi\" + '\n",
      "  '0.030*\"countri\" + 0.029*\"build\" + 0.025*\"direct\" + 0.022*\"econom\" + '\n",
      "  '0.017*\"state\" + 0.016*\"framework\"'),\n",
      " (17,\n",
      "  '0.062*\"health\" + 0.048*\"european\" + 0.041*\"state\" + 0.038*\"member\" + '\n",
      "  '0.038*\"survei\" + 0.030*\"regul\" + 0.022*\"council\" + 0.022*\"union\" + '\n",
      "  '0.017*\"method\" + 0.015*\"polici\"'),\n",
      " (18,\n",
      "  '0.120*\"popul\" + 0.078*\"death\" + 0.059*\"region\" + 0.033*\"urban\" + '\n",
      "  '0.033*\"group\" + 0.031*\"level\" + 0.026*\"peopl\" + 0.026*\"definit\" + '\n",
      "  '0.022*\"number\" + 0.021*\"defin\"'),\n",
      " (19,\n",
      "  '0.090*\"indic\" + 0.078*\"econom\" + 0.046*\"environment\" + 0.027*\"employ\" + '\n",
      "  '0.026*\"social\" + 0.026*\"labour\" + 0.025*\"chang\" + 0.021*\"economi\" + '\n",
      "  '0.019*\"increas\" + 0.017*\"total\"')]\n"
     ]
    }
   ],
   "source": [
    "#Print the Keyword in the 20 topics\n",
    "\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Dominant topics.\n",
    "***\n",
    "\n",
    "\n",
    "We want to understand the volume and distribution of topics in order to judge how widely it was discussed,so we define the _df_dominant_topics_.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>13</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>resid, account, nation, economi, countri, buil...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>resid, account, nation, economi, countri, buil...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>includ, agricultur, water, cover, plant, exclu...</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.1051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0561</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>1279</th>\n",
       "      <td>13</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>17</td>\n",
       "      <td>health, european, state, member, survei, regul...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>10</td>\n",
       "      <td>activ, statist, servic, product, econom, indus...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>15</td>\n",
       "      <td>product, energi, innov, process, servic, inclu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>0</td>\n",
       "      <td>govern, institut, public, sector, countri, ser...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1284 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Dominant_Topic                                     Topic_Keywords  \\\n",
       "0                 13  european, countri, state, member, union, stati...   \n",
       "1                 16  resid, account, nation, economi, countri, buil...   \n",
       "2                 16  resid, account, nation, economi, countri, buil...   \n",
       "3                  3  includ, agricultur, water, cover, plant, exclu...   \n",
       "4                 13  european, countri, state, member, union, stati...   \n",
       "...              ...                                                ...   \n",
       "1279              13  european, countri, state, member, union, stati...   \n",
       "1280              17  health, european, state, member, survei, regul...   \n",
       "1281              10  activ, statist, servic, product, econom, indus...   \n",
       "1282              15  product, energi, innov, process, servic, inclu...   \n",
       "1283               0  govern, institut, public, sector, countri, ser...   \n",
       "\n",
       "      Num_Documents  Perc_Documents  \n",
       "0              76.0          0.0592  \n",
       "1              65.0          0.0506  \n",
       "2              50.0          0.0389  \n",
       "3             135.0          0.1051  \n",
       "4              72.0          0.0561  \n",
       "...             ...             ...  \n",
       "1279            NaN             NaN  \n",
       "1280            NaN             NaN  \n",
       "1281            NaN             NaN  \n",
       "1282            NaN             NaN  \n",
       "1283            NaN             NaN  \n",
       "\n",
       "[1284 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "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": [
    "### 9. 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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_el1284013781978278723591965381\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1284013781978278723591965381_data = {\"mdsDat\": {\"x\": [-0.03474385592074425, -0.0005706551245568544, 0.18596809032365427, -0.00040811559671277426, -0.030227577244374762, -0.027758997044601153, -0.13053551038037484, 0.0235501956567288, 0.24665264179461158, 0.042772004065659876, 0.012945063437505589, 0.04780553017571341, -0.09553475114144294, -0.21787985528966095, 0.16279282092862848, 0.15218620633728483, -0.07192030519619642, -0.17082701394626526, -0.09233362688585657, -0.0019322889489992985], \"y\": [-0.17686747174746373, -0.020662334084232722, -0.13099930374669383, 0.08702367831918019, 0.06600944766775928, -0.02865199775130952, 0.02228457059362344, 0.26221042829281077, 0.033148656754044076, 0.18121988571340256, -0.08487901501413589, 0.0073977462829802115, -0.04941424161475408, -0.11489972460583563, -0.08867465162519429, -0.060562772301045974, -0.06505304737949272, -0.039722758180302936, 0.1790524437471076, 0.022040460679552724], \"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\": [7.216136513280344, 3.324178311293214, 4.902127301237826, 6.705980882795975, 4.694539973340761, 5.131207057105482, 3.75966654288358, 3.4173103839959995, 4.771231704874548, 5.15946350349807, 5.66917779750522, 4.137049336154767, 2.7241006079271752, 8.287532094030398, 7.715771920486192, 4.599594140455694, 4.229657862538195, 5.935983255287298, 4.272050884199735, 3.347239927109528]}, \"tinfo\": {\"Term\": [\"popul\", \"product\", \"incom\", \"activ\", \"countri\", \"european\", \"enterpris\", \"person\", \"household\", \"price\", \"number\", \"transport\", \"state\", \"account\", \"servic\", \"govern\", \"gross\", \"applic\", \"death\", \"energi\", \"indic\", \"member\", \"resid\", \"financi\", \"weight\", \"social\", \"health\", \"econom\", \"market\", \"region\", \"central\", \"govern\", \"control\", \"entiti\", \"financ\", \"corpor\", \"local\", 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      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
       "topic                                               \n",
       "0     -0.034744 -0.176867       1        1  7.216137\n",
       "1     -0.000571 -0.020662       2        1  3.324178\n",
       "2      0.185968 -0.130999       3        1  4.902127\n",
       "3     -0.000408  0.087024       4        1  6.705981\n",
       "4     -0.030228  0.066009       5        1  4.694540\n",
       "5     -0.027759 -0.028652       6        1  5.131207\n",
       "6     -0.130536  0.022285       7        1  3.759667\n",
       "7      0.023550  0.262210       8        1  3.417310\n",
       "8      0.246653  0.033149       9        1  4.771232\n",
       "9      0.042772  0.181220      10        1  5.159464\n",
       "10     0.012945 -0.084879      11        1  5.669178\n",
       "11     0.047806  0.007398      12        1  4.137049\n",
       "12    -0.095535 -0.049414      13        1  2.724101\n",
       "13    -0.217880 -0.114900      14        1  8.287532\n",
       "14     0.162793 -0.088675      15        1  7.715772\n",
       "15     0.152186 -0.060563      16        1  4.599594\n",
       "16    -0.071920 -0.065053      17        1  4.229658\n",
       "17    -0.170827 -0.039723      18        1  5.935983\n",
       "18    -0.092334  0.179052      19        1  4.272051\n",
       "19    -0.001932  0.022040      20        1  3.347240, topic_info=        Term        Freq       Total Category  logprob  loglift\n",
       "160    popul  226.000000  226.000000  Default  30.0000  30.0000\n",
       "23   product  720.000000  720.000000  Default  29.0000  29.0000\n",
       "307    incom  311.000000  311.000000  Default  28.0000  28.0000\n",
       "192    activ  530.000000  530.000000  Default  27.0000  27.0000\n",
       "5    countri  537.000000  537.000000  Default  26.0000  26.0000\n",
       "..       ...         ...         ...      ...      ...      ...\n",
       "110   direct    9.982079  154.592302  Topic20  -4.8371   0.6570\n",
       "14   statist   14.033567  513.171415  Topic20  -4.4964  -0.2021\n",
       "35    includ   11.762565  603.458001  Topic20  -4.6729  -0.5407\n",
       "5    countri   11.018795  537.829080  Topic20  -4.7383  -0.4909\n",
       "190    gener    9.422038  219.604495  Topic20  -4.8948   0.2483\n",
       "\n",
       "[882 rows x 6 columns], token_table=      Topic      Freq     Term\n",
       "term                          \n",
       "42        1  0.107262  abbrevi\n",
       "42        2  0.014627  abbrevi\n",
       "42        3  0.024378  abbrevi\n",
       "42        4  0.024378  abbrevi\n",
       "42        5  0.039004  abbrevi\n",
       "...     ...       ...      ...\n",
       "55       15  0.075775    world\n",
       "55       17  0.151551    world\n",
       "55       18  0.075775    world\n",
       "55       19  0.045465    world\n",
       "55       20  0.075775    world\n",
       "\n",
       "[3563 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": 12,
     "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": [
    "Based on this visualization, we give the following interpretations to the 20 topics:\n",
    "* Topic 1: Public sector / central governmnent institutions.\n",
    "* Topic 2: Services and official statistics.\n",
    "* Topic 3: Capital and production.\n",
    "* Topic 4: Agriculture and water resources.\n",
    "* Topic 5: Enterprise activities.\n",
    "* Topic 6: R&D and technological activities.\n",
    "* Topic 7: Protection application and asylum grants.\n",
    "* Topic 8: Indicators.\n",
    "* Topic 9: Production, consumption and gross accounts.\n",
    "* Topic 10: Price indices and labour employment.\n",
    "* Topic 11: Activities statistics.\n",
    "* Topic 12: Activities and materials.\n",
    "* Topic 13: Transport.\n",
    "* Topic 14: EU trade and treaties.\n",
    "* Topic 15: Social expenditure and households disposable income.\n",
    "* Topic 16: Innovation in energy production.\n",
    "* Topic 17: Economies and national accounts.\n",
    "* Topic 18: Health surveillance in the EU Member States.\n",
    "* Topic 19: Regions and demographics.\n",
    "* Topic 20: Environmental economomics indicators.\n",
    "\n"
   ]
  },
  {
   "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>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>13</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>resid, account, nation, economi, countri, buil...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>resid, account, nation, economi, countri, buil...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>includ, agricultur, water, cover, plant, exclu...</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.1051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0561</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>1279</th>\n",
       "      <td>13</td>\n",
       "      <td>european, countri, state, member, union, stati...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>17</td>\n",
       "      <td>health, european, state, member, survei, regul...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>10</td>\n",
       "      <td>activ, statist, servic, product, econom, indus...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>15</td>\n",
       "      <td>product, energi, innov, process, servic, inclu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>0</td>\n",
       "      <td>govern, institut, public, sector, countri, ser...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1284 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Dominant_Topic                                     Topic_Keywords  \\\n",
       "0                 13  european, countri, state, member, union, stati...   \n",
       "1                 16  resid, account, nation, economi, countri, buil...   \n",
       "2                 16  resid, account, nation, economi, countri, buil...   \n",
       "3                  3  includ, agricultur, water, cover, plant, exclu...   \n",
       "4                 13  european, countri, state, member, union, stati...   \n",
       "...              ...                                                ...   \n",
       "1279              13  european, countri, state, member, union, stati...   \n",
       "1280              17  health, european, state, member, survei, regul...   \n",
       "1281              10  activ, statist, servic, product, econom, indus...   \n",
       "1282              15  product, energi, innov, process, servic, inclu...   \n",
       "1283               0  govern, institut, public, sector, countri, ser...   \n",
       "\n",
       "      Num_Documents  Perc_Documents  \n",
       "0              76.0          0.0592  \n",
       "1              65.0          0.0506  \n",
       "2              50.0          0.0389  \n",
       "3             135.0          0.1051  \n",
       "4              72.0          0.0561  \n",
       "...             ...             ...  \n",
       "1279            NaN             NaN  \n",
       "1280            NaN             NaN  \n",
       "1281            NaN             NaN  \n",
       "1282            NaN             NaN  \n",
       "1283            NaN             NaN  \n",
       "\n",
       "[1284 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "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": [
    "### 9. The model's coherence score.\n",
    "***\n",
    "\n",
    "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)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score:  0.4353862037516623\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import CoherenceModel\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1284"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_topics = lda_model.get_document_topics(corpus,minimum_probability=0.3)\n",
    "res  = [d for d in docs_topics]\n",
    "res\n",
    "len(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic id:  1\n",
      "('govern', 0.06312214)\n",
      "('institut', 0.039047536)\n",
      "('public', 0.024157144)\n",
      "('sector', 0.022230962)\n",
      "('countri', 0.022155376)\n",
      "('servic', 0.020090593)\n",
      "('central', 0.01696279)\n",
      "('gener', 0.016707817)\n",
      "('nation', 0.01586535)\n",
      "('provid', 0.015074597)\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:  2\n",
      "('includ', 0.062067676)\n",
      "('servic', 0.05873867)\n",
      "('refer', 0.038266435)\n",
      "('other', 0.033289205)\n",
      "('section', 0.031166408)\n",
      "('group', 0.030733617)\n",
      "('classif', 0.029262666)\n",
      "('class', 0.025429307)\n",
      "('offic', 0.024998615)\n",
      "('statist', 0.0239433)\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",
      "('market', 0.071567886)\n",
      "('product', 0.042697817)\n",
      "('expenditur', 0.039091595)\n",
      "('protect', 0.03432346)\n",
      "('capit', 0.03430665)\n",
      "('produc', 0.034118336)\n",
      "('output', 0.02944988)\n",
      "('current', 0.027218716)\n",
      "('environment', 0.025636744)\n",
      "('transfer', 0.024948357)\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",
      "('includ', 0.0427207)\n",
      "('agricultur', 0.038368754)\n",
      "('water', 0.037478797)\n",
      "('cover', 0.035760812)\n",
      "('plant', 0.029547378)\n",
      "('exclud', 0.027528688)\n",
      "('normal', 0.027380647)\n",
      "('refer', 0.026258953)\n",
      "('perman', 0.021366293)\n",
      "('surfac', 0.018489042)\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",
      "('enterpris', 0.087286994)\n",
      "('activ', 0.052016675)\n",
      "('refer', 0.04982976)\n",
      "('inform', 0.037381265)\n",
      "('agricultur', 0.022280065)\n",
      "('standard', 0.021110762)\n",
      "('survei', 0.020402135)\n",
      "('product', 0.017599123)\n",
      "('period', 0.017133595)\n",
      "('statist', 0.016289182)\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",
      "('activ', 0.036775734)\n",
      "('technolog', 0.03461781)\n",
      "('research', 0.03421304)\n",
      "('develop', 0.031074405)\n",
      "('resourc', 0.028810672)\n",
      "('knowledg', 0.024364933)\n",
      "('human', 0.022533957)\n",
      "('perform', 0.021298045)\n",
      "('manag', 0.018201292)\n",
      "('standard', 0.0173393)\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', 0.08867133)\n",
      "('applic', 0.08537985)\n",
      "('protect', 0.047104873)\n",
      "('countri', 0.042024825)\n",
      "('decis', 0.038944177)\n",
      "('procedur', 0.031150505)\n",
      "('statu', 0.027864018)\n",
      "('intern', 0.023329815)\n",
      "('refer', 0.023099463)\n",
      "('legal', 0.020574328)\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",
      "('number', 0.09786763)\n",
      "('weight', 0.0837885)\n",
      "('averag', 0.04894719)\n",
      "('equival', 0.036435567)\n",
      "('measur', 0.03544626)\n",
      "('calcul', 0.035160955)\n",
      "('total', 0.034559663)\n",
      "('period', 0.028560743)\n",
      "('ratio', 0.025346436)\n",
      "('indic', 0.022418577)\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",
      "('product', 0.07898468)\n",
      "('gross', 0.075723365)\n",
      "('consumpt', 0.045825712)\n",
      "('input', 0.032483257)\n",
      "('output', 0.030829238)\n",
      "('account', 0.030566199)\n",
      "('balanc', 0.028858716)\n",
      "('capit', 0.023707967)\n",
      "('total', 0.023238992)\n",
      "('measur', 0.021930842)\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",
      "('price', 0.09417058)\n",
      "('index', 0.06079852)\n",
      "('person', 0.046334155)\n",
      "('emploi', 0.040364288)\n",
      "('period', 0.029917074)\n",
      "('labour', 0.028822012)\n",
      "('employe', 0.026586344)\n",
      "('employ', 0.025498303)\n",
      "('chang', 0.024571007)\n",
      "('consum', 0.022236466)\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",
      "('activ', 0.107406676)\n",
      "('statist', 0.05182723)\n",
      "('servic', 0.05139819)\n",
      "('product', 0.0409105)\n",
      "('econom', 0.028650088)\n",
      "('industri', 0.028303571)\n",
      "('agricultur', 0.025041096)\n",
      "('classif', 0.019662037)\n",
      "('level', 0.016609244)\n",
      "('european', 0.013899712)\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",
      "('household', 0.081498295)\n",
      "('materi', 0.0554886)\n",
      "('survei', 0.027605247)\n",
      "('activ', 0.027374594)\n",
      "('includ', 0.027374158)\n",
      "('person', 0.026848175)\n",
      "('agricultur', 0.026211623)\n",
      "('famili', 0.026183197)\n",
      "('organ', 0.024849692)\n",
      "('product', 0.021408746)\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",
      "('transport', 0.11262511)\n",
      "('vehicl', 0.0884795)\n",
      "('countri', 0.06695117)\n",
      "('enterpris', 0.04838406)\n",
      "('place', 0.044886306)\n",
      "('legal', 0.039700206)\n",
      "('includ', 0.031772)\n",
      "('nation', 0.028218264)\n",
      "('report', 0.021074448)\n",
      "('power', 0.020408824)\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",
      "('european', 0.09688828)\n",
      "('countri', 0.074721076)\n",
      "('state', 0.06356602)\n",
      "('member', 0.06318716)\n",
      "('union', 0.053895835)\n",
      "('statist', 0.030907081)\n",
      "('trade', 0.0188179)\n",
      "('januari', 0.018797962)\n",
      "('region', 0.018388622)\n",
      "('territori', 0.01655701)\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",
      "('incom', 0.09428247)\n",
      "('financi', 0.061429497)\n",
      "('social', 0.05973378)\n",
      "('account', 0.041038774)\n",
      "('asset', 0.03428864)\n",
      "('contribut', 0.025511924)\n",
      "('household', 0.023625776)\n",
      "('dispos', 0.020319758)\n",
      "('employe', 0.01946569)\n",
      "('transfer', 0.018709438)\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",
      "('product', 0.107903585)\n",
      "('energi', 0.076249585)\n",
      "('innov', 0.0628849)\n",
      "('process', 0.06269504)\n",
      "('servic', 0.034541734)\n",
      "('includ', 0.030864187)\n",
      "('improv', 0.023831936)\n",
      "('technolog', 0.021315824)\n",
      "('industri', 0.019695248)\n",
      "('electr', 0.016673053)\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:  17\n",
      "('resid', 0.07835984)\n",
      "('account', 0.06848371)\n",
      "('nation', 0.039581534)\n",
      "('economi', 0.033522803)\n",
      "('countri', 0.029951682)\n",
      "('build', 0.028900525)\n",
      "('direct', 0.024706403)\n",
      "('econom', 0.021941053)\n",
      "('state', 0.016779367)\n",
      "('framework', 0.015753947)\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",
      "('health', 0.06232165)\n",
      "('european', 0.047754873)\n",
      "('state', 0.04087112)\n",
      "('member', 0.038255263)\n",
      "('survei', 0.037564296)\n",
      "('regul', 0.03017989)\n",
      "('council', 0.022030337)\n",
      "('union', 0.021553528)\n",
      "('method', 0.016824652)\n",
      "('polici', 0.014598398)\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",
      "('popul', 0.12040051)\n",
      "('death', 0.07759218)\n",
      "('region', 0.059362747)\n",
      "('urban', 0.03273642)\n",
      "('group', 0.03272998)\n",
      "('level', 0.030901287)\n",
      "('peopl', 0.026286745)\n",
      "('definit', 0.025579445)\n",
      "('number', 0.022298243)\n",
      "('defin', 0.021254491)\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",
      "('indic', 0.089501135)\n",
      "('econom', 0.07788543)\n",
      "('environment', 0.04568424)\n",
      "('employ', 0.026708603)\n",
      "('social', 0.02595485)\n",
      "('labour', 0.02582781)\n",
      "('chang', 0.024777489)\n",
      "('economi', 0.02086127)\n",
      "('increas', 0.019478058)\n",
      "('total', 0.016562888)\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": [
      "(20, 352)\n",
      "                                           Topic Name     appli     charg  \\\n",
      "0   Public sector / central governmnent institutions.  0.001448  0.000052   \n",
      "1                   Services and official statistics.  0.008028  0.005389   \n",
      "2                             Capital and production.  0.000045  0.003452   \n",
      "3                    Agriculture and water resources.  0.002724  0.000042   \n",
      "4                              Enterprise activities.  0.000109  0.000031   \n",
      "5                   R&D and technological activities.  0.000327  0.000043   \n",
      "6           Protection application and asylum grants.  0.011352  0.000036   \n",
      "7                                         Indicators.  0.001099  0.000041   \n",
      "8         Production, consumption and gross accounts.  0.001649  0.000028   \n",
      "9                Price indices and labour employment.  0.000044  0.000027   \n",
      "10                             Activities statistics.  0.001932  0.000431   \n",
      "11                          Activities and materials.  0.000697  0.000037   \n",
      "12                                         Transport.  0.002248  0.000209   \n",
      "13                             EU trade and treaties.  0.001000  0.000873   \n",
      "14  Social expenditure and households disposable i...  0.000026  0.005029   \n",
      "15                   Innovation in energy production.  0.000774  0.001043   \n",
      "16                   Economies and national accounts.  0.000057  0.000034   \n",
      "17       Health surveillance in the EU Member States.  0.005082  0.000022   \n",
      "18                          Regions and demographics.  0.004796  0.000071   \n",
      "19              Environmental economomics indicators.  0.000826  0.001205   \n",
      "\n",
      "     collect   context     count   countri     defin  establish  european  \\\n",
      "0   0.007050  0.007401  0.000039  0.022155  0.007845   0.003774  0.005501   \n",
      "1   0.000142  0.000044  0.000053  0.000545  0.008990   0.001895  0.000377   \n",
      "2   0.000053  0.000415  0.000760  0.000520  0.007422   0.000792  0.003787   \n",
      "3   0.017834  0.000275  0.003665  0.000094  0.008588   0.000474  0.000785   \n",
      "4   0.012509  0.000141  0.007609  0.000202  0.002977   0.009529  0.003167   \n",
      "5   0.000078  0.000036  0.001429  0.000920  0.006299   0.003748  0.006562   \n",
      "6   0.000952  0.000053  0.001597  0.042025  0.014428   0.000044  0.000239   \n",
      "7   0.000041  0.000041  0.000041  0.006565  0.010565   0.000041  0.000385   \n",
      "8   0.000028  0.002364  0.000028  0.000187  0.008532   0.000037  0.000747   \n",
      "9   0.000027  0.005961  0.000027  0.010492  0.007077   0.000027  0.001751   \n",
      "10  0.006034  0.000056  0.000031  0.000101  0.006756   0.000168  0.013900   \n",
      "11  0.012315  0.001598  0.000030  0.002090  0.009208   0.000036  0.000986   \n",
      "12  0.003189  0.000048  0.001484  0.066951  0.007659   0.000102  0.008350   \n",
      "13  0.010214  0.003648  0.000370  0.074721  0.002117   0.014959  0.096888   \n",
      "14  0.002867  0.000048  0.000016  0.000573  0.010690   0.000020  0.001622   \n",
      "15  0.000577  0.000036  0.000033  0.001909  0.002648   0.000177  0.000998   \n",
      "16  0.003988  0.004010  0.000035  0.029952  0.001983   0.000160  0.014061   \n",
      "17  0.007529  0.001545  0.000171  0.005190  0.004442   0.005122  0.047755   \n",
      "18  0.000118  0.002837  0.000030  0.003127  0.021254   0.000289  0.001396   \n",
      "19  0.003275  0.000046  0.005052  0.008754  0.001543   0.002188  0.016522   \n",
      "\n",
      "    ...  intermedi  percentag    govern     offic    formal     relev  \\\n",
      "0   ...   0.000036   0.000053  0.063122  0.001914  0.000731  0.000275   \n",
      "1   ...   0.001499   0.000101  0.000066  0.024999  0.000328  0.000236   \n",
      "2   ...   0.006358   0.000027  0.013525  0.000038  0.000027  0.000027   \n",
      "3   ...   0.001180   0.000478  0.000019  0.000033  0.000019  0.000312   \n",
      "4   ...   0.000053   0.003737  0.000125  0.001342  0.000059  0.000688   \n",
      "5   ...   0.000038   0.000621  0.003153  0.001443  0.000025  0.000667   \n",
      "6   ...   0.000036   0.000167  0.000069  0.002857  0.000036  0.000053   \n",
      "7   ...   0.000050   0.006451  0.000070  0.000049  0.000049  0.000041   \n",
      "8   ...   0.008426   0.000037  0.001385  0.000028  0.000033  0.000028   \n",
      "9   ...   0.000075   0.011013  0.000032  0.000027  0.001195  0.000895   \n",
      "10  ...   0.002136   0.000032  0.000177  0.001723  0.000346  0.000167   \n",
      "11  ...   0.000030   0.000088  0.000063  0.000040  0.013822  0.000786   \n",
      "12  ...   0.000048   0.000374  0.000063  0.000048  0.000063  0.000057   \n",
      "13  ...   0.000059   0.000019  0.000024  0.002031  0.002431  0.000016   \n",
      "14  ...   0.002525   0.000016  0.012879  0.000019  0.000447  0.001420   \n",
      "15  ...   0.000330   0.000834  0.000299  0.000102  0.000026  0.000124   \n",
      "16  ...   0.000040   0.000051  0.000072  0.000268  0.000036  0.005639   \n",
      "17  ...   0.000033   0.001263  0.000044  0.000022  0.000022  0.000756   \n",
      "18  ...   0.006955   0.007235  0.000083  0.000030  0.002655  0.002888   \n",
      "19  ...   0.000904   0.006117  0.000162  0.000047  0.001267  0.004458   \n",
      "\n",
      "       index   exchang    regard    format  \n",
      "0   0.000045  0.010578  0.000527  0.003383  \n",
      "1   0.000062  0.003465  0.002216  0.000044  \n",
      "2   0.000109  0.000076  0.000037  0.008028  \n",
      "3   0.001570  0.000071  0.000022  0.000425  \n",
      "4   0.000057  0.000035  0.000036  0.000399  \n",
      "5   0.000337  0.000652  0.001991  0.001336  \n",
      "6   0.000044  0.000044  0.000575  0.000043  \n",
      "7   0.010740  0.000041  0.000041  0.000041  \n",
      "8   0.006149  0.000028  0.000028  0.006858  \n",
      "9   0.060799  0.004332  0.001074  0.000027  \n",
      "10  0.000087  0.000076  0.000025  0.000363  \n",
      "11  0.000030  0.000030  0.001274  0.000042  \n",
      "12  0.000048  0.005683  0.008267  0.000048  \n",
      "13  0.000051  0.002613  0.000016  0.000595  \n",
      "14  0.000039  0.000019  0.000091  0.000025  \n",
      "15  0.000503  0.000038  0.000064  0.000625  \n",
      "16  0.000519  0.000050  0.003265  0.000031  \n",
      "17  0.000083  0.000506  0.001488  0.000022  \n",
      "18  0.000050  0.000107  0.000030  0.000030  \n",
      "19  0.000898  0.000116  0.000837  0.000046  \n",
      "\n",
      "[20 rows x 353 columns]\n",
      "(20, 353)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "for topic_id in range(20):\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",
    "    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.show()        \n",
    "        \n",
    "topic_names = ['Public sector / central governmnent institutions.',\n",
    "'Services and official statistics.',\n",
    "'Capital and production.',\n",
    "'Agriculture and water resources.',\n",
    "'Enterprise activities.',\n",
    "'R&D and technological activities.',\n",
    "'Protection application and asylum grants.',\n",
    "'Indicators.',\n",
    "'Production, consumption and gross accounts.',\n",
    "'Price indices and labour employment.',\n",
    "'Activities statistics.',\n",
    "'Activities and materials.',\n",
    "'Transport.',\n",
    "'EU trade and treaties.',\n",
    "'Social expenditure and households disposable income.',\n",
    "'Innovation in energy production.',\n",
    "'Economies and national accounts.',               \n",
    "'Health surveillance in the EU Member States.',\n",
    "'Regions and demographics.',\n",
    "'Environmental economomics indicators.']\n",
    "\n",
    "\n",
    "\n",
    "topics_terms_mat = lda_model.get_topics()\n",
    "print(topics_terms_mat.shape)\n",
    "#topics_terms_df = pd.DataFrame(columns=id2word.values())\n",
    "topics_terms_df = pd.DataFrame()\n",
    "for topic_id in range(20):\n",
    "    topics_terms_df = topics_terms_df.append(pd.Series(topics_terms_mat[topic_id,:].transpose()), ignore_index=True)           \n",
    "#    print('Topic ',topic_id)    \n",
    "#   for t in lda_model.get_topic_terms(topic_id, topn=10):\n",
    "#    print(topics_terms[topic_id,:])\n",
    "    #print(id2word[t[0]],t[1])\n",
    "#sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)  \n",
    "topics_terms_df.rename(columns=id2word, inplace=True)\n",
    "#for topic_id in range(20):\n",
    "#    topics_terms_df.loc[topic_id,'Top 5 topic keywords'] = [(a,b) for (a,b) in sorted(lda_model.show_topic(0,topn=5),key=lambda x: x[1], reverse=True)]\n",
    "topics_terms_df.insert(0,'Topic Name',topic_names)\n",
    "print(topics_terms_df)\n",
    "print(topics_terms_df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.0000001 , 1.        , 0.9999999 , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 0.99999994,\n",
       "       1.        , 1.        , 0.9999999 , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 0.99999994],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.sum(lda_model.get_topics(),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "topics_terms_df.to_csv('topics_terms_df.write_csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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