data_exploration/topic_modeling_playground.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic modeling playground\n",
    "In this notebook, different concepts for topic modeling will be tested and evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\fabik\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# import libraries used in this notebook\n",
    "\n",
    "import re\n",
    "\n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# preprocessing\n",
    "import nltk\n",
    "\n",
    "nltk.download('stopwords')\n",
    "import spacy\n",
    "\n",
    "# Plotting\n",
    "import pyLDAvis.gensim_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from enum import Enum\n",
    "\n",
    "\n",
    "class Party(Enum):\n",
    "    AFD = 0\n",
    "    CDU = 1\n",
    "    FDP = 2\n",
    "    GRUENE = 3\n",
    "    LINKE = 4\n",
    "    SPD = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "nlp = spacy.load('de_core_news_md')\n",
    "from nltk.corpus import stopwords\n",
    "from spacy.lang.de.stop_words import STOP_WORDS\n",
    "\n",
    "# stopwords\n",
    "nltk_stopwords = stopwords.words('german')\n",
    "\n",
    "# build stopwords list\n",
    "all_stopwords = list(set(STOP_WORDS) | set(nltk_stopwords))\n",
    "with open('custom_stopwords.txt', 'r', encoding='utf-8') as f:\n",
    "    all_stopwords += [line.strip() for line in f.readlines()]\n",
    "\n",
    "# Load files\n",
    "party_text = {}\n",
    "for party in Party:\n",
    "    all_stopwords.extend(['{}'.format(party.name.lower())])\n",
    "    with open('../resources/' + party.name + '.txt', encoding='utf-8', errors='ignore') as txt:\n",
    "        file = \" \".join(l for l in txt)\n",
    "        # remove gender *\n",
    "        file = re.sub(r'\\*innen(\\w*)\\s', r'\\1 ', file)\n",
    "    party_text[party] = file\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def prepare_data(parties: [Party]):\n",
    "    # get sections\n",
    "    sections = []\n",
    "    for partie in parties:\n",
    "        sections_of_partie = re.split(r'\\n\\s*\\n', party_text[partie])\n",
    "        sections.extend(sections_of_partie)\n",
    "\n",
    "    partie_wordbags_mod = []\n",
    "\n",
    "    for section in sections:\n",
    "        partie_wordbag = gensim.utils.simple_preprocess(section)\n",
    "\n",
    "        #filter stopwords\n",
    "        partie_wordbag_spacy = [word for word in partie_wordbag if word not in all_stopwords]\n",
    "\n",
    "        partie_wordbags_mod.append(partie_wordbag_spacy)\n",
    "\n",
    "    # make bigrams / trigrams\n",
    "    bigram = gensim.models.Phrases(partie_wordbags_mod, min_count=5, threshold=100)\n",
    "    trigram = gensim.models.Phrases(bigram[partie_wordbags_mod], threshold=100)\n",
    "\n",
    "    bigram_phraser = gensim.models.phrases.Phraser(bigram)\n",
    "    trigram_phraser = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "    def make_bigrams(texts):\n",
    "        return [bigram_phraser[doc] for doc in texts]\n",
    "\n",
    "    def make_trigrams(texts):\n",
    "        return [trigram_phraser[bigram_phraser[doc]] for doc in texts]\n",
    "\n",
    "    def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "        texts_out = []\n",
    "        for sent in texts:\n",
    "            doc = nlp(\" \".join(sent))\n",
    "            texts_out.append(\n",
    "                [token.lemma_ for token in doc if token.pos_ in allowed_postags and token.lemma_ not in all_stopwords])\n",
    "        return texts_out\n",
    "\n",
    "    words_trigrams = make_trigrams(partie_wordbags_mod)\n",
    "    words_lematized = lemmatization(words_trigrams)\n",
    "\n",
    "    id2word = corpora.Dictionary(words_lematized)\n",
    "    texts = words_lematized\n",
    "    corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "    return corpus, id2word, words_lematized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity:  -8.728835422078108\n",
      "\n",
      "Coherence Score:  -11.763577496472735\n",
      "\n",
      "Perplexity:  -8.721225105782517\n",
      "\n",
      "Coherence Score:  -11.79115897457628\n"
     ]
    }
   ],
   "source": [
    "\n",
    "corpus, id2word, words_lematized = prepare_data([Party.FDP])\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                            id2word=id2word,\n",
    "                                            num_topics=10,\n",
    "                                            iterations=125\n",
    "                                            )\n",
    "\n",
    "lda_model.print_topics(num_topics=10, num_words=10)\n",
    "\n",
    "# Compute Perplexity\n",
    "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=words_lematized, dictionary=id2word, coherence='u_mass',\n",
    "                                     corpus=corpus)\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\fabik\\pycharmprojects\\textanalytics\\venv\\lib\\site-packages\\pyLDAvis\\_prepare.py:246: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  default_term_info = default_term_info.sort_values(\n"
     ]
    },
    {
     "data": {
      "text/plain": "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\ntopic                                                \n1     -0.014473 -0.051447       1        1  13.904799\n4      0.101666 -0.134407       2        1  12.578873\n6     -0.104091 -0.019589       3        1  11.797722\n9     -0.007929  0.030817       4        1  11.570051\n0      0.096839  0.090323       5        1  10.969782\n5     -0.038959  0.011802       6        1  10.130961\n8      0.028221  0.027541       7        1   9.821970\n2     -0.041025 -0.007035       8        1   7.431939\n3      0.016781  0.068721       9        1   6.429670\n7     -0.037028 -0.016726      10        1   5.364233, topic_info=              Term       Freq      Total Category  logprob  loglift\n321    unternehmen  47.000000  47.000000  Default  30.0000  30.0000\n1371            co  18.000000  18.000000  Default  29.0000  29.0000\n74         bildung  24.000000  24.000000  Default  28.0000  28.0000\n359       kommunen   7.000000   7.000000  Default  27.0000  27.0000\n1115     beruflich  16.000000  16.000000  Default  26.0000  26.0000\n...            ...        ...        ...      ...      ...      ...\n1085    einführung   1.241846   7.030953  Topic10  -6.1654   1.1917\n314        künftig   1.237318  15.790500  Topic10  -6.1690   0.3790\n19          ändern   1.227483   4.848598  Topic10  -6.1770   1.5517\n1265       erasmus   1.227328   4.478422  Topic10  -6.1771   1.6310\n976   wahlfreiheit   1.227297   5.548520  Topic10  -6.1771   1.4167\n\n[706 rows x 6 columns], token_table=      Topic      Freq          Term\nterm                               \n640       9  0.516026  abgabenquote\n2796      5  0.921534    abgeordnet\n3265      7  0.707600       abhilfe\n3599      6  0.478040     abrüstung\n3873      4  0.926709   abschiebung\n...     ...       ...           ...\n2510      1  0.293878   überwachung\n2510      2  0.146939   überwachung\n2510      3  0.440816   überwachung\n253       5  0.726001   überzeugung\n253      10  0.363001   überzeugung\n\n[1582 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 5, 7, 10, 1, 6, 9, 3, 4, 8])",
      "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_el168416660518225284071328980\"></div>\n<script type=\"text/javascript\">\n\nvar ldavis_el168416660518225284071328980_data = {\"mdsDat\": {\"x\": [-0.014473348193515641, 0.1016663801739118, -0.1040914395793997, -0.00792914771605402, 0.09683857883577635, -0.03895948304785342, 0.028220736197684868, -0.041025322354142565, 0.016780673790572978, -0.03702762810698074], \"y\": [-0.05144679982760219, -0.13440697503329405, -0.019589142117548548, 0.03081710291092232, 0.0903233413212854, 0.011801679507602242, 0.02754107045470506, -0.007035107798925628, 0.06872092459062996, -0.016726094007774882], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.904798931341567, 12.578872659317724, 11.797722188949113, 11.570051310543237, 10.969781621379372, 10.130961016517642, 9.821970097006703, 7.431939398242374, 6.429669784342831, 5.364232992359439]}, \"tinfo\": {\"Term\": [\"unternehmen\", \"co\", \"bildung\", \"kommunen\", \"beruflich\", \"freiheit\", \"modernisierung\", \"m\\u00f6glichkeit\", \"land\", \"sch\\u00fctzen\", \"chancen\", \"beziehungen\", \"digitale\", \"frei\", \"altersvorsorge\", \"privat\", \"jugendlich\", \"zuk\\u00fcnftig\", \"hochschulen\", \"menschenrechte\", \"kraftstoffe\", \"digitalisierung\", \"staatliche\", \"pflegen\", \"national\", \"mobil\", \"wettbewerb\", \"gesetzlich\", \"klaren\", \"erasmus\", \"fortbildungen\", \"ambulant\", \"unterricht\", \"unverheiratet\", \"fischerei\", \"regionale\", \"zugreifen\", \"darlehen\", \"paare\", \"konkret\", \"elternteilen\", \"digitalpakt\", \"feminismus\", \"ersatz\", \"p\\u00e4dagogische\", \"pr\\u00e4ventions\", \"aufteilung\", \"abwanderung\", \"aufsicht\", \"individuen\", \"lieb\", \"klimadividende\", \"lasten\", \"afrikanisch\", \"sozialausgaben\", \"b\\u00fcrgerin\", \"islamistische\", \"k\\u00fcnstlich\", \"baf\\u00f6g\", \"virtuelle\", \"tierwohl\", \"landwirte\", \"raum\", \"l\\u00e4ndlich\", \"\\u00fcbernehmen\", \"schule\", \"schulen\", \"versorgung\", \"digital\", \"orientieren\", \"l\\u00e4ndern\", \"europaweit\", \"kooperation\", \"datenschutz\", \"einzeln\", \"verl\\u00e4ssliche\", \"kind\", \"steigend\", \"gesetzlich\", \"ber\\u00fccksichtigen\", \"international\", \"einfahren\", \"ma\\u00dfnahmen\", \"gerecht\", \"regionen\", \"euro\", \"daten\", \"unternehmen\", \"regelm\\u00e4\\u00dfig\", \"verwaltung\", \"modern\", \"beruflich\", \"entsprechend\", \"sozial\", \"wirtschaft\", \"bildung\", \"wettbewerb\", \"entwicklung\", \"staaten\", \"nachhaltig\", \"naturschutz\", \"negative\", \"volksrepublik\", \"diversity\", \"gewinnsteuer\", \"t\\u00e4glich\", \"masken\", \"digitalisiert\", \"gesamtsystem\", \"prozessen\", \"zulassung\", \"luftqualit\\u00e4t\", \"management\", \"kommunikation\", \"mutter\", \"l\\u00f6hnen\", \"bedenken\", \"nachholfaktor\", \"\\u00fcbergriffen\", \"beitragszahler\", \"fehlverhalten\", \"betrag\", \"verurteilen\", \"neutral\", \"energietr\\u00e4gern\", \"herstellung\", \"geb\\u00e4ude\", \"kurzfristig\", \"homeoffice\", \"ethnisch\", \"arzneimitteln\", \"verschl\\u00fcsselung\", \"beziehungen\", \"renten\", \"hilfen\", \"wasserstoff\", \"beamte\", \"kraftstoffe\", \"verschieden\", \"besteuerung\", \"beamtinnen\", \"langfristig\", \"daten\", \"staat\", \"zukunft\", \"zeigen\", \"rechtliche\", \"extremismus\", \"k\\u00fcnftig\", \"finanzieren\", \"erreichen\", \"nachhaltig\", \"international\", \"entwicklung\", \"altersvorsorge\", \"arbeit\", \"grundlage\", \"erfolgen\", \"digitalisierung\", \"mitgliedstaaten\", \"digitale\", \"unternehmen\", \"fall\", \"staatlich\", \"strategie\", \"bargeld\", \"antisemitismus\", \"absicherung\", \"kryptow\\u00e4hrungen\", \"einzahlen\", \"rendite\", \"rundfunkbeitrag\", \"parlamente\", \"besetzen\", \"rundfunk\", \"l\\u00fccke\", \"ehrenamt\", \"riester\", \"grundsicherung\", \"unber\\u00fchrt\", \"antisemitisch\", \"potentiell\", \"beteiligungen\", \"j\\u00fcdisch\", \"seenotrettung\", \"austauschdienst\", \"au\\u00dfengrenzen\", \"frontex\", \"gr\\u00fcnder\", \"beauftragt\", \"bewerben\", \"zufallsauswahl\", \"gestalt\", \"entschlacken\", \"anzeigen\", \"akteure\", \"wahlen\", \"freiwillige\", \"staatliche\", \"demokratien\", \"inland\", \"jugendlich\", \"rente\", \"altern\", \"\\u00fcberwachung\", \"menschenrechte\", \"klaren\", \"wohnraum\", \"freiwillig\", \"einsatz\", \"vermeiden\", \"demokratisch\", \"m\\u00f6glichkeit\", \"betroffen\", \"gesetzlich\", \"basis\", \"behinderungen\", \"daten\", \"gering\", \"weltweit\", \"bieten\", \"digital\", \"altersvorsorge\", \"frei\", \"erfolgen\", \"umsetzen\", \"k\\u00fcnftig\", \"gesellschaft\", \"wettbewerb\", \"sch\\u00fctzen\", \"leben\", \"b\\u00fcrgerinnen\", \"privat\", \"unternehmen\", \"anlasslose\", \"seriell\", \"j\\u00e4gerinnen\", \"angler\", \"wirtschaftsraum\", \"regel\", \"emissionen\", \"beendigung\", \"krieges\", \"abschiebung\", \"mandat\", \"niederlegen\", \"absetzbarkeit\", \"speicherung\", \"elternzeit\", \"zeitraum\", \"erkrankung\", \"auszeit\", \"vorschl\\u00e4ge\", \"pr\\u00e4sidenten\", \"vorst\\u00e4nden\", \"begrenzen\", \"ruhen\", \"kommissarinnen\", \"kommissare\", \"kriminalit\\u00e4t\", \"l\\u00f6sung\", \"verfolgen\", \"zuschuss\", \"ratifizieren\", \"co\", \"pauschale\", \"zust\\u00e4ndig\", \"klimaschutz\", \"parlament\", \"l\\u00e4nder\", \"polizei\", \"preis\", \"hochschulen\", \"zertifikate\", \"voll\", \"einheitlich\", \"kommission\", \"technologien\", \"gewalt\", \"beziehungen\", \"mitglieder\", \"garantieren\", \"finanziell\", \"durchsetzen\", \"verbote\", \"organisieren\", \"kontrolle\", \"entlasten\", \"national\", \"finanzierung\", \"nachhaltig\", \"innovationen\", \"wettbewerb\", \"schutz\", \"unternehmen\", \"chancen\", \"grundlage\", \"standards\", \"land\", \"b\\u00fcrgerinnen\", \"staat\", \"kulturgut\", \"gleicherma\\u00dfen\", \"kulturelle\", \"landesbanken\", \"bewertung\", \"ausrichten\", \"abgeordnet\", \"enkelkindern\", \"altmodisch\", \"\\u00f6kobilanzen\", \"talentschulen\", \"hebammen\", \"alternativ\", \"fahrzeugbau\", \"mensch\", \"gro\\u00dfeltern\", \"konzept\", \"lehre\", \"engagieren\", \"zivilklauseln\", \"vielf\\u00e4ltig\", \"farbenspiele\", \"diplomatische\", \"erfolgsfaktor\", \"verb\\u00e4nden\", \"kanzler\", \"abspeichern\", \"kanzlerin\", \"komplizieren\", \"festgelegt\", \"\\u00fcberzeugung\", \"pr\\u00e4gen\", \"mietendeckel\", \"solide\", \"etablieren\", \"bedeutung\", \"kulturell\", \"landwirtschaft\", \"nachhaltigkeit\", \"bildung\", \"freiheit\", \"chancen\", \"beruflich\", \"illegal\", \"land\", \"pers\\u00f6nlich\", \"leben\", \"sozial\", \"chance\", \"grenzen\", \"bieten\", \"politik\", \"modern\", \"gr\\u00fcndung\", \"bildungssystem\", \"organisieren\", \"nachhaltig\", \"aufsteigen\", \"rechtsstaat\", \"regelungen\", \"forschung\", \"m\\u00f6glichkeiten\", \"wettbewerb\", \"versorgung\", \"sicherheit\", \"sch\\u00fctzen\", \"digital\", \"privat\", \"unternehmen\", \"arbeit\", \"gew\\u00e4hrleisten\", \"kommunen\", \"zeitarbeit\", \"gigabit\", \"bezahlbare\", \"chancenkarte\", \"opposition\", \"kanadisch\", \"m\\u00e4nnern\", \"leidtragend\", \"altersgrenzen\", \"belarussischen\", \"verteilung\", \"verkehrspolitik\", \"zeitalter\", \"bef\\u00f6rdern\", \"fachkr\\u00e4fte\", \"einwanderung\", \"ideologische\", \"menschheit\", \"urheberrecht\", \"staatsanleihen\", \"einwanderinnen\", \"einwanderer\", \"schulden\", \"lebendig\", \"vorenthalten\", \"tourismus\", \"abr\\u00fcstung\", \"technologisch\", \"lebensqualit\\u00e4t\", \"arbeitsmarkt\", \"kraft\", \"unternehmen\", \"bund_l\\u00e4nder\", \"besch\\u00e4ftigen\", \"frauen\", \"beruflich\", \"global\", \"m\\u00f6glichkeit\", \"digital\", \"freiheit\", \"vielfalt\", \"betreiben\", \"schutz\", \"banken\", \"staaten\", \"kriterien\", \"einzeln\", \"entwicklung\", \"nachhaltig\", \"gesellschaft\", \"weltweit\", \"bildung\", \"ausbau\", \"menschenrechte\", \"international\", \"l\\u00e4ndern\", \"sicherheit\", \"modern\", \"rechenzentren\", \"dachfonds\", \"meerespolitik\", \"investorinnen\", \"europol\", \"wunsch\", \"t\\u00e4tigkeit\", \"investition\", \"capital\", \"ausstatten\", \"gezielt\", \"kurs\", \"schaffend\", \"abhilfe\", \"wiederaufbaus\", \"g\\u00e4ngeln\", \"wirkend\", \"widerrufen\", \"erh\\u00f6hung\", \"fachkr\\u00e4ften\", \"straff\", \"memorial\", \"entschlossen\", \"alt\\u00f6l\", \"gkv\", \"operativ\", \"br\\u00fccken\", \"kreml\", \"k\\u00fcstenregionen\", \"bildungsarena\", \"umweltschutz\", \"souver\\u00e4nit\\u00e4t\", \"automatisierung\", \"steuer\", \"derzeitig\", \"grundlagen\", \"mobil\", \"fonds\", \"friedensordnung\", \"heben\", \"kulturf\\u00f6rderung\", \"kr\\u00e4fte\", \"partnern\", \"ausbauen\", \"bereichen\", \"bauen\", \"privat\", \"anwendungen\", \"energie\", \"digitalisierung\", \"wirtschaftlich\", 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\"technologien\", \"technologien\", \"technologien\", \"technologien\", \"technologien\", \"technologisch\", \"tierwohl\", \"tierwohl\", \"tourismus\", \"tragf\\u00e4hige\", \"traum\", \"trennung\", \"trennung\", \"t\\u00e4glich\", \"t\\u00e4tigkeit\", \"umfasst\", \"umfasst\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umweltschutz\", \"unber\\u00fchrt\", \"unrecht\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unterricht\", \"unterziehen\", \"unverheiratet\", \"urheberrecht\", \"urheberrecht\", \"verbote\", \"verbote\", \"verbote\", \"verbote\", \"verb\\u00e4nden\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinigen\", \"vereinigen\", \"vereinten_nationen\", \"vereinten_nationen\", \"vereinten_nationen\", 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      "c:\\users\\fabik\\pycharmprojects\\textanalytics\\venv\\lib\\site-packages\\pyLDAvis\\_prepare.py:246: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
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\"schutz\", \"schutz\", \"schutz\", \"schutz\", \"schutz\", \"schwach\", \"sch\\u00fctzen\", \"sch\\u00fctzen\", \"sch\\u00fctzen\", \"sch\\u00fctzen\", \"sch\\u00fctzen\", \"sch\\u00fctzen\", \"sch\\u00fctzen\", \"sch\\u00fctzen\", \"sch\\u00fctzen\", \"see\", \"seenotrettung\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"seiten\", \"selbstst\\u00e4ndig\", \"selbstst\\u00e4ndige\", \"selbstst\\u00e4ndige\", \"selbstst\\u00e4ndige\", \"selbstst\\u00e4ndige\", \"seriell\", \"sexuelle\", \"sexuelle\", \"sicher\", \"sicher\", \"sicher\", \"sicher\", \"sicher\", \"sicherheit\", \"sicherheit\", \"sicherheit\", \"sicherheit\", \"sicherheit\", \"sicherheit\", \"sicherheit\", \"sicherheitsrat\", \"sicherstellen\", \"sicherstellen\", \"sicherstellen\", \"sicherstellen\", \"sicherstellen\", \"sicherstellen\", \"sicherstellen\", \"sinnvoll\", \"sinnvoll\", \"sinnvoll\", \"situationen\", \"solarenergie\", \"sozial\", \"sozial\", \"sozial\", \"sozial\", \"sozial\", \"sozial\", \"sozial\", \"sozialausgaben\", \"spalten\", \"sparer\", \"sparerinnen\", \"sparerinnen\", \"speicher\", \"speichern\", \"speichern\", \"speicherung\", \"speicherung\", \"staat\", \"staat\", \"staat\", \"staat\", \"staat\", \"staat\", \"staat\", \"staat\", \"staaten\", \"staaten\", \"staaten\", \"staaten\", \"staaten\", \"staaten\", \"staaten\", \"staaten\", \"staaten\", \"staatenwelt\", \"staatlich\", \"staatlich\", \"staatlich\", \"staatlich\", \"staatlich\", \"staatlich\", \"staatlich\", \"staatlich\", \"staatsanleihen\", \"starke\", \"starke\", \"starke\", \"stecken\", \"stehend\", \"stelle\", \"stelle\", \"stelle\", \"stellung\", \"steuer\", \"steuer\", \"stipendiatinnen\", \"strafbar\", \"straff\", \"straftaten\", \"straftaten\", \"straftaten\", \"strategie\", \"strategie\", \"strategie\", \"strategie\", \"strategie\", \"strategien\", \"stream\", \"subventionen\", \"subventionen\", \"subventionen\", \"subventionen\", \"systeme\", \"systeme\", \"s\\u00e4ule\", \"s\\u00e4ule\", \"s\\u00e4ulen\", \"s\\u00e4ulen\", \"s\\u00e4ulen\", \"talentschulen\", \"technisch\", \"technologien\", \"technologien\", \"technologien\", \"technologien\", \"technologien\", \"technologisch\", \"teilweise\", \"teilweise\", \"tierwohl\", \"tierwohl\", \"tourismus\", \"trennung\", \"trennung\", \"t\\u00e4glich\", \"umbenennen\", \"umfeld\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umsetzen\", \"umsetzung\", \"umsetzung\", \"umsetzung\", \"unabh\\u00e4ngigkeit\", \"unabh\\u00e4ngigkeit\", \"unber\\u00fchrt\", \"unfall\", \"unrecht\", \"unrechts\", \"unterbringung\", \"unterbringung\", \"untereinander\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unternehmen\", \"unterricht\", \"unterricht\", \"unterst\\u00fctzung\", \"unterst\\u00fctzung\", \"unterst\\u00fctzung\", \"unterst\\u00fctzung\", \"unterst\\u00fctzung\", \"unterst\\u00fctzung\", \"unterst\\u00fctzung\", \"unverheiratet\", \"unverheiratet\", \"urheberrecht\", \"verbraucher\", \"verbraucher\", \"vereinbart\", \"vereinbart\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinfachen\", \"vereinigen\", \"vereinigen\", \"vereinten_nationen\", \"vereinten_nationen\", \"vereinten_nationen\", \"vereinten_nationen\", \"vereinten_nationen\", \"vereinten_nationen\", \"verfahren\", \"verfahren\", \"verfahren\", \"verfahren\", \"verfahren\", \"verfahren\", \"verfassten\", \"verfassung\", \"verfolgen\", \"verfolgen\", \"verfolgung\", \"vergleichbar\", \"vergleichbar\", \"verkehrspolitik\", \"verletzungen\", \"vermietung\", \"vernachl\\u00e4ssigen\", \"verschieden\", \"verschieden\", \"verschieden\", \"verschieden\", \"verschieden\", \"verschl\\u00fcsselung\", \"verschl\\u00fcsselung\", \"verschl\\u00fcsselung\", \"versch\\u00e4rfung\", \"versichern\", \"versichern\", \"versorgung\", \"versorgung\", \"versorgung\", \"versorgung\", \"versorgung\", \"versorgung\", \"versorgung\", \"verst\\u00e4ndigen\", \"versuchen\", \"verteidigung\", \"verteilen\", \"verteilung\", \"vertragsstaaten\", \"vertr\\u00e4gen\", \"verwaltung\", \"verwaltung\", \"verwaltung\", \"verwaltung\", \"verwaltung\", \"verzerren\", \"ver\\u00e4nderung\", \"vielfalt\", \"vielfalt\", \"vielfalt\", \"vielfalt\", \"vielfalt\", \"vielfalt\", \"vielmehr\", \"volksrepublik\", \"voraussetzung\", \"voraussetzung\", \"voraussetzung\", \"voraussetzung\", \"voraussetzung\", \"voraussetzung\", \"vorenthalten\", \"wachstum\", \"wachstum\", \"wachstum\", \"wachstum\", \"wahlrecht\", \"wahlrecht\", \"wahltermine\", \"wasserstoff\", \"wasserstoff\", \"wasserstoff\", \"weisungen\", \"weltbester\", \"weltern\\u00e4hrung\", \"weltweit\", \"weltweit\", \"weltweit\", \"weltweit\", \"weltweit\", \"weltweit\", \"weltweit\", \"weltweit\", \"werte\", \"werte\", \"werte\", \"wettbewerb\", \"wettbewerb\", \"wettbewerb\", \"wettbewerb\", \"wettbewerb\", \"wettbewerb\", \"wettbewerb\", \"wettbewerb\", \"wettbewerb\", \"wetterabh\\u00e4ngig\", \"wirkung\", \"wirtschaft\", \"wirtschaft\", \"wirtschaft\", \"wirtschaft\", \"wirtschaft\", \"wirtschaft\", \"wirtschaft\", \"wirtschaft\", \"wirtschaft\", \"wirtschaftlich\", \"wirtschaftlich\", \"wirtschaftlich\", \"wirtschaftlich\", \"wirtschaftlich\", \"wirtschaftlich\", \"wohnungen\", \"w\\u00e4lder\", \"w\\u00e4lder\", \"zahn\\u00e4rzte\", \"zeigen\", \"zeigen\", \"zeigen\", \"zeigen\", \"zeitalter\", \"zeitarbeit\", \"zeitdauer\", \"zeitnahen\", \"zentrale\", \"ziele\", \"ziele\", \"ziele\", \"ziele\", \"zugreifen\", \"zukunft\", \"zukunft\", \"zukunft\", \"zukunft\", \"zukunft\", \"zukunft\", \"zukunft\", \"zukunft\", \"zukunft\", \"zukunftssichere\", \"zusammenarbeit\", \"zusammenarbeit\", \"zusammenarbeit\", \"zusammenarbeit\", \"zusammenarbeit\", \"zusammenarbeit\", \"zusammenarbeit\", \"zuschuss\", \"zuschuss\", \"zuteilen\", \"z\\u00fcgig\", \"z\\u00fcgig\", \"\\u00e4mter\", \"\\u00e4rzte\", \"\\u00e4rzte\", \"\\u00e4rztinnen\", \"\\u00e4rztinnen\", \"\\u00f6ffnen\", \"\\u00f6ffnen\", \"\\u00f6ffnen\", \"\\u00f6ffnen\", \"\\u00f6ffnen\", \"\\u00f6kobilanzen\", \"\\u00f6kosysteme\", \"\\u00fcberzeugung\", \"\\u00fcberzeugung\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 10, 4, 6, 5, 1, 9, 2, 8, 7]};\n\nfunction LDAvis_load_lib(url, callback){\n  var s = document.createElement('script');\n  s.src = url;\n  s.async = true;\n  s.onreadystatechange = s.onload = callback;\n  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n  document.getElementsByTagName(\"head\")[0].appendChild(s);\n}\n\nif(typeof(LDAvis) !== \"undefined\"){\n   // already loaded: just create the visualization\n   !function(LDAvis){\n       new LDAvis(\"#\" + \"ldavis_el168416660399828966023041416\", ldavis_el168416660399828966023041416_data);\n   }(LDAvis);\n}else if(typeof define === \"function\" && define.amd){\n   // require.js is available: use it to load d3/LDAvis\n   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n   require([\"d3\"], function(d3){\n      window.d3 = d3;\n      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n        new LDAvis(\"#\" + \"ldavis_el168416660399828966023041416\", ldavis_el168416660399828966023041416_data);\n      });\n    });\n}else{\n    // require.js not available: dynamically load d3 & LDAvis\n    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n                 new LDAvis(\"#\" + \"ldavis_el168416660399828966023041416\", ldavis_el168416660399828966023041416_data);\n            })\n         });\n}\n</script>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim_models.prepare(lda_model, corpus, id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\fabik\\pycharmprojects\\textanalytics\\venv\\lib\\site-packages\\gensim\\models\\lsimodel.py:932: DeprecationWarning: Please use `csc_matvecs` from the `scipy.sparse` namespace, the `scipy.sparse.sparsetools` namespace is deprecated.\n",
      "  sparsetools.csc_matvecs(m, n, samples, corpus.indptr, corpus.indices,\n",
      "c:\\users\\fabik\\pycharmprojects\\textanalytics\\venv\\lib\\site-packages\\gensim\\models\\lsimodel.py:932: DeprecationWarning: Please use `csc_matvecs` from the `scipy.sparse` namespace, the `scipy.sparse.sparsetools` namespace is deprecated.\n",
      "  sparsetools.csc_matvecs(m, n, samples, corpus.indptr, corpus.indices,\n",
      "c:\\users\\fabik\\pycharmprojects\\textanalytics\\venv\\lib\\site-packages\\gensim\\models\\lsimodel.py:932: DeprecationWarning: Please use `csc_matvecs` from the `scipy.sparse` namespace, the `scipy.sparse.sparsetools` namespace is deprecated.\n",
      "  sparsetools.csc_matvecs(m, n, samples, corpus.indptr, corpus.indices,\n",
      "c:\\users\\fabik\\pycharmprojects\\textanalytics\\venv\\lib\\site-packages\\gensim\\models\\lsimodel.py:932: DeprecationWarning: Please use `csc_matvecs` from the `scipy.sparse` namespace, the `scipy.sparse.sparsetools` namespace is deprecated.\n",
      "  sparsetools.csc_matvecs(m, n, samples, corpus.indptr, corpus.indices,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score:  -9.593086051851852\n"
     ]
    },
    {
     "data": {
      "text/plain": "[(0,\n  '0.368*\"unternehmen\" + 0.212*\"freiheit\" + 0.186*\"entwicklung\" + 0.179*\"nachhaltig\" + 0.174*\"international\" + 0.134*\"global\" + 0.132*\"wettbewerb\" + 0.127*\"digital\" + 0.126*\"co\" + 0.125*\"menschenrechte\"'),\n (1,\n  '-0.701*\"unternehmen\" + 0.286*\"freiheit\" + 0.153*\"land\" + 0.150*\"entwicklung\" + 0.118*\"global\" + 0.112*\"nachhaltig\" + 0.101*\"co\" + -0.084*\"wettbewerb\" + -0.083*\"mittler\" + 0.076*\"bürgerinnen\"'),\n (2,\n  '-0.343*\"freiheit\" + 0.294*\"beruflich\" + 0.270*\"bildung\" + 0.241*\"schulen\" + -0.190*\"global\" + -0.187*\"unternehmen\" + 0.157*\"digital\" + -0.152*\"land\" + -0.151*\"entwicklung\" + -0.130*\"nachhaltig\"'),\n (3,\n  '-0.541*\"co\" + 0.188*\"beruflich\" + -0.187*\"wasserstoff\" + -0.186*\"klimaschutz\" + 0.177*\"bildung\" + 0.141*\"schulen\" + 0.130*\"digital\" + -0.126*\"erreichen\" + -0.120*\"zertifikate\" + -0.103*\"direkt\"'),\n (4,\n  '0.308*\"beruflich\" + 0.281*\"bildung\" + -0.280*\"international\" + 0.217*\"schulen\" + -0.140*\"antisemitismus\" + 0.135*\"freiheit\" + 0.121*\"co\" + 0.117*\"unternehmen\" + -0.100*\"klaren\" + -0.099*\"demokratisch\"'),\n (5,\n  '0.383*\"daten\" + -0.361*\"international\" + -0.231*\"bildung\" + -0.212*\"beruflich\" + 0.169*\"bürgerinnen\" + 0.127*\"verwaltung\" + 0.120*\"staat\" + 0.113*\"altersvorsorge\" + 0.110*\"digital\" + 0.104*\"anbieterinnen\"'),\n (6,\n  '-0.292*\"schulen\" + 0.257*\"daten\" + 0.226*\"international\" + 0.169*\"bildung\" + -0.156*\"schule\" + 0.141*\"beruflich\" + -0.134*\"antisemitismus\" + 0.126*\"digital\" + 0.102*\"wasserstoff\" + -0.099*\"verfolgen\"'),\n (7,\n  '-0.379*\"schulen\" + -0.265*\"daten\" + -0.187*\"international\" + 0.178*\"altersvorsorge\" + -0.155*\"schule\" + 0.154*\"bildung\" + 0.141*\"beruflich\" + -0.121*\"schülerinnen_schüler\" + -0.114*\"unterricht\" + 0.111*\"digitale\"'),\n (8,\n  '-0.281*\"wasserstoff\" + 0.187*\"altersvorsorge\" + -0.154*\"sicher\" + -0.137*\"strom\" + -0.135*\"verfolgen\" + -0.129*\"politisch\" + -0.122*\"kraftstoffe\" + -0.117*\"neutral\" + -0.117*\"energieträgern\" + 0.114*\"international\"'),\n (9,\n  '0.249*\"wasserstoff\" + -0.176*\"daten\" + -0.173*\"co\" + -0.172*\"bildung\" + -0.161*\"beruflich\" + 0.155*\"schulen\" + 0.126*\"strom\" + 0.124*\"altersvorsorge\" + -0.116*\"ausdrücklich\" + 0.106*\"kraftstoffe\"')]"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsi_model = gensim.models.lsimodel.LsiModel(corpus=corpus, id2word=id2word, num_topics=10, chunksize=100)\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lsi = CoherenceModel(model=lsi_model, texts=words_lematized, dictionary=id2word, coherence='u_mass',\n",
    "                                     corpus=corpus)\n",
    "coherence_lsi = coherence_model_lsi.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lsi)\n",
    "\n",
    "lsi_model.print_topics(num_topics=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.002*inklusion + 0.002*jahrelang + 0.001*klassen + 0.001*antriebe + '\n",
      "  '0.001*tausender + 0.001*praktiken + 0.001*abschiebung + 0.001*illegale + '\n",
      "  '0.001*sport + 0.001*widerspruch + 0.001*föderation + 0.001*auszubildenden + '\n",
      "  '0.001*zurückkehren + 0.001*ausrichten + 0.001*abzuziehenden + '\n",
      "  '0.001*ausweitung + 0.001*beschäftigung + 0.001*zeitdauer + 0.001*begleiten '\n",
      "  '+ 0.001*prävention'),\n",
      " (1,\n",
      "  '0.002*erkenntnisse + 0.002*position + 0.002*kritisieren + 0.001*mechanismen '\n",
      "  '+ 0.001*forschung + 0.001*bestandsflotten + 0.001*schnellstens + '\n",
      "  '0.001*befugnissen + 0.001*ausstoßen + 0.001*verstetigt + 0.001*werkzeug + '\n",
      "  '0.001*diensten + 0.001*priorität + 0.001*fischarten + 0.001*vorsorgeprinzip '\n",
      "  '+ 0.001*fälle + 0.001*sozialpolitik + 0.001*schädliche + 0.001*bedingt + '\n",
      "  '0.001*hass'),\n",
      " (2,\n",
      "  '0.002*bagatell + 0.002*finanzbehörden + 0.002*auktionsdesign + '\n",
      "  '0.001*waffenrechts + 0.001*wachsend + 0.001*talentpool + 0.001*asylbewerber '\n",
      "  '+ 0.001*mehrstaatigkeit + 0.001*elterngeldplus + 0.001*erfolge + '\n",
      "  '0.001*intensiv + 0.001*autoritär + 0.001*vielfalt + 0.001*wohnen + '\n",
      "  '0.001*geburtshilfe + 0.001*kriminalisieren + 0.001*aufenthaltes + '\n",
      "  '0.001*beteiligung + 0.001*parlamenten + 0.001*gelebt'),\n",
      " (3,\n",
      "  '0.002*konzentrieren + 0.002*lizenzierten + 0.002*nachhaltig + '\n",
      "  '0.002*mietwagen + 0.002*erträgen + 0.002*kurzstationären + '\n",
      "  '0.002*landwirtinnen + 0.002*nahezu + 0.001*tarifvertrag + 0.001*einschulung '\n",
      "  '+ 0.001*mensch + 0.001*assistenz + 0.001*parallel + 0.001*flexibilisieren + '\n",
      "  '0.001*integrieren + 0.001*abgetan + 0.001*strategie + 0.001*gesetzen + '\n",
      "  '0.001*planeten + 0.001*teilen'),\n",
      " (4,\n",
      "  '0.002*kreative + 0.002*privathaushalte + 0.002*geraten + 0.001*prozesse + '\n",
      "  '0.001*verbindung + 0.001*fit + 0.001*aufheben + 0.001*häuslich + '\n",
      "  '0.001*problems + 0.001*förderprogramme + 0.001*neuaufstellung + '\n",
      "  '0.001*marktverzerrung + 0.001*verhältnissen + 0.001*politikfelder + '\n",
      "  '0.001*ausprobieren + 0.001*verkehrsmix + 0.001*selbstreflexion + '\n",
      "  '0.001*geldern + 0.001*versuch + 0.001*präventive'),\n",
      " (5,\n",
      "  '0.002*modernste + 0.002*zukunftsfähigen + 0.001*einzug + 0.001*fahrt + '\n",
      "  '0.001*atomsprengkopf + 0.001*riester + 0.001*selbsttötung + '\n",
      "  '0.001*klumpenrisiken + 0.001*bürgernäher + 0.001*finanzierend + '\n",
      "  '0.001*vorliegen + 0.001*tüv + 0.001*fachfremd + 0.001*abtippen + '\n",
      "  '0.001*aufforstung + 0.001*planen + 0.001*verwenden + 0.001*effizient + '\n",
      "  '0.001*erläutern + 0.001*marktwirtschaft'),\n",
      " (6,\n",
      "  '0.002*freisetzen + 0.002*zulassen + 0.002*teilhabe + 0.002*hinterlassen + '\n",
      "  '0.002*letztlich + 0.002*produktiv + 0.001*bilanz + 0.001*alleine + '\n",
      "  '0.001*krankenhaus + 0.001*vorne + 0.001*praxis + 0.001*datenschutz + '\n",
      "  '0.001*projekten + 0.001*bedrohung + 0.001*bauen + 0.001*bereichen + '\n",
      "  '0.001*gläubigerinnen + 0.001*prosperität + 0.001*sozial + 0.001*loslösen'),\n",
      " (7,\n",
      "  '0.002*blaupause + 0.002*basen + 0.002*inbetriebnahme + 0.002*ordnung + '\n",
      "  '0.002*ersetzen + 0.002*erderwärmung + 0.002*patientin + 0.002*rentnerinnen '\n",
      "  '+ 0.002*anbieten + 0.002*quotierungen + 0.001*berechnungen + 0.001*politik '\n",
      "  '+ 0.001*beziehungen + 0.001*wasserstoff + 0.001*bewirtschaften + '\n",
      "  '0.001*markt + 0.001*patentrecht + 0.001*dar + 0.001*ergreifen + '\n",
      "  '0.001*versuchen'),\n",
      " (8,\n",
      "  '0.002*unschädlich + 0.002*entschuldung + 0.002*grundhaltung + '\n",
      "  '0.002*pauschbeträge + 0.002*brücke + 0.002*verlagerung + 0.001*lebensmittel '\n",
      "  '+ 0.001*entschulden + 0.001*beziehungen + 0.001*ideologie + 0.001*gebiet + '\n",
      "  '0.001*mittelstand + 0.001*arbeitsalltag + 0.001*behandeln + '\n",
      "  '0.001*bürgerkonten + 0.001*hilfsleistungen + 0.001*lehrerinnen + '\n",
      "  '0.001*alternative + 0.001*stete + 0.001*abholen'),\n",
      " (9,\n",
      "  '0.002*kleinstbeträge + 0.002*beitrag + 0.002*regelfall + 0.001*geprüft + '\n",
      "  '0.001*hebel + 0.001*ausdruck + 0.001*brief + 0.001*tagungsort + '\n",
      "  '0.001*qualitativ_hochwertige + 0.001*rückforderungen + 0.001*shenzhen + '\n",
      "  '0.001*aufbereitung + 0.001*bürgerinnen_bürgern + 0.001*potentiale + '\n",
      "  '0.001*zeitalter + 0.001*untersuchung + 0.001*transport + 0.001*hinterlassen '\n",
      "  '+ 0.001*fortentwicklung + 0.001*summe'),\n",
      " (10,\n",
      "  '0.002*grundrechte + 0.002*staatliche + 0.002*umgangen + 0.002*systematische '\n",
      "  '+ 0.001*datenpakete + 0.001*ansprüchen + 0.001*finanzen + 0.001*verspielen '\n",
      "  '+ 0.001*zielführende + 0.001*ökologie + 0.001*familienpolitik + '\n",
      "  '0.001*ansatzes + 0.001*islamistische + 0.001*datenzugriff + '\n",
      "  '0.001*rechnungen + 0.001*begleitung + 0.001*schutzsuchende + 0.001*frauen + '\n",
      "  '0.001*konzeption + 0.001*beitragszahler'),\n",
      " (11,\n",
      "  '0.002*partnerinnen + 0.002*begleiten + 0.002*genau + 0.002*würdewahrender + '\n",
      "  '0.001*treten + 0.001*aufgreifen + 0.001*wettstreit + 0.001*vorschlagsrecht '\n",
      "  '+ 0.001*anstrengungen + 0.001*antrag + 0.001*nachdrücklich + '\n",
      "  '0.001*forstwirtschaft + 0.001*herstellerinnen + 0.001*wahlfälschungen + '\n",
      "  '0.001*untermauern + 0.001*regierungen + 0.001*sportlich + '\n",
      "  '0.001*euministerrat + 0.001*dominierend + 0.001*leistungsfähige'),\n",
      " (12,\n",
      "  '0.002*schulbildung + 0.002*meerespolitik + 0.002*zurückbezahlt + '\n",
      "  '0.002*überwindung + 0.002*erfolgreich + 0.002*speichern + 0.001*änderungen '\n",
      "  '+ 0.001*kurs + 0.001*pseudonyme + 0.001*afcfta + 0.001*bergbau + '\n",
      "  '0.001*netzwerke + 0.001*ausdehnen + 0.001*hervorragend + 0.001*abholen + '\n",
      "  '0.001*respektieren + 0.001*kurzstationären + 0.001*investitionen + '\n",
      "  '0.001*ort + 0.001*partnermonate'),\n",
      " (13,\n",
      "  '0.002*instrumenten + 0.002*arbeitsfähige + 0.002*hochschule + '\n",
      "  '0.002*ausstattung + 0.002*beschäftigung + 0.002*drängen + 0.002*einmischung '\n",
      "  '+ 0.001*bildungssektor + 0.001*abkommen + 0.001*partnerschaften + '\n",
      "  '0.001*transportieren + 0.001*transformation + 0.001*tierwohllabel + '\n",
      "  '0.001*zeugnissen + 0.001*bejagung + 0.001*subventionen + 0.001*einsatz + '\n",
      "  '0.001*bewirtschaftung + 0.001*stehend + 0.001*beachtung'),\n",
      " (14,\n",
      "  '0.002*nötigen + 0.002*entgegenwirken + 0.002*pepp + 0.002*bevormundung + '\n",
      "  '0.002*gesteuert + 0.002*möglichkeiten + 0.002*abgaben + 0.001*investition + '\n",
      "  '0.001*lock + 0.001*lichte + 0.001*sozialwohnungen + 0.001*platz + '\n",
      "  '0.001*agieren + 0.001*sondersteuer + 0.001*lebenssituation + 0.001*zuvor + '\n",
      "  '0.001*lösung + 0.001*wesentlich + 0.001*kindes + 0.001*völkermordes'),\n",
      " (15,\n",
      "  '0.002*zusammenarbeit + 0.002*weltbeste + 0.002*konstruktion + '\n",
      "  '0.002*verhängung + 0.001*inzwischen + 0.001*verbracht + 0.001*programme + '\n",
      "  '0.001*ambulant + 0.001*arbeitsfähige + 0.001*linie + 0.001*euinstitutionen '\n",
      "  '+ 0.001*arbeitsmarktes + 0.001*gestellt + 0.001*amtsgänge + '\n",
      "  '0.001*strukturreform + 0.001*kriegs + 0.001*bewerten + 0.001*pflanzen + '\n",
      "  '0.001*abtippen + 0.001*widerspiegeln'),\n",
      " (16,\n",
      "  '0.002*rohstoff + 0.002*abhängen + 0.002*staaten + 0.002*mehrheit + '\n",
      "  '0.002*pandemie + 0.002*analog + 0.002*förderbedarf + 0.002*grundstein + '\n",
      "  '0.002*tagungsort + 0.002*vergrößerung + 0.001*überziehen + '\n",
      "  '0.001*freundschaft + 0.001*reaktiviert + 0.001*mütter + 0.001*regionen + '\n",
      "  '0.001*wohngeld + 0.001*hälfte + 0.001*stabilitäts + 0.001*stete + '\n",
      "  '0.001*vermögensaufbau'),\n",
      " (17,\n",
      "  '0.002*nachfragen + 0.002*kommunal + 0.002*schulgeldfrei + 0.002*linear + '\n",
      "  '0.002*aufwendig + 0.002*erzieher + 0.002*gesetzgebung + 0.002*prüfung + '\n",
      "  '0.001*generelle + 0.001*liberal + 0.001*gebären + 0.001*gleichheit + '\n",
      "  '0.001*freiwillige + 0.001*zwingen + 0.001*beirats + 0.001*schematisch + '\n",
      "  '0.001*punkt + 0.001*zulassen + 0.001*steuerpolitik + 0.001*unlauter'),\n",
      " (18,\n",
      "  '0.002*verweis + 0.002*enkelfitte + 0.002*agilität + 0.001*jüdische + '\n",
      "  '0.001*jahres + 0.001*rentenkassen + 0.001*experimentell + '\n",
      "  '0.001*international + 0.001*nirgendwo + 0.001*benennen + '\n",
      "  '0.001*bewirtschaften + 0.001*bürgernahe + 0.001*wahlfälschungen + '\n",
      "  '0.001*säule + 0.001*rohstoffen + 0.001*außerschulische + '\n",
      "  '0.001*verabschieden + 0.001*dringend + 0.001*bestrafung + '\n",
      "  '0.001*möglichkeiten'),\n",
      " (19,\n",
      "  '0.002*steuertarifs + 0.002*ehefrau + 0.001*selbsttragenden + 0.001*gremium '\n",
      "  '+ 0.001*startchancen + 0.001*wandelnd + 0.001*vorankommen + 0.001*vielmehr '\n",
      "  '+ 0.001*digitalisieren + 0.001*liberalste + 0.001*erneuern + 0.001*erhöhung '\n",
      "  '+ 0.001*abwehren + 0.001*baukasten + 0.001*europaweite + 0.001*außengrenze '\n",
      "  '+ 0.001*lehrmethoden + 0.001*staatsbahn + 0.001*cyberwaffen + '\n",
      "  '0.001*erbgänge')]\n",
      "\n",
      "Coherence Score:  -21.80966448130853\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "hdp_model = gensim.models.hdpmodel.HdpModel(corpus=corpus, id2word=id2word, )\n",
    "pprint(hdp_model.show_topics())\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_hdp = CoherenceModel(model=hdp_model, texts=words_lematized, dictionary=id2word, coherence='u_mass',\n",
    "                                     corpus=corpus)\n",
    "coherence_hdp = coherence_model_hdp.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_hdp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot coherence score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_coherence_lda(coherence, max_topics, max_iterations, parties, iteration_intervall=25, save=False):\n",
    "    corpus, id2word, words_lematized = prepare_data(parties)\n",
    "\n",
    "    data = {}\n",
    "    for iterations in range(iteration_intervall, max_iterations, iteration_intervall):\n",
    "        statistics = {}\n",
    "        for topics in range(1, max_topics):\n",
    "            lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                                        id2word=id2word,\n",
    "                                                        num_topics=topics,\n",
    "                                                        iterations=iterations\n",
    "                                                        )\n",
    "            coherence_model_lda = CoherenceModel(model=lda_model, texts=words_lematized, dictionary=id2word,\n",
    "                                                 coherence=coherence, corpus=corpus)\n",
    "            coherence_lda = coherence_model_lda.get_coherence()\n",
    "            statistics[topics] = coherence_lda\n",
    "        data[iterations] = statistics\n",
    "\n",
    "    for key, value in data.items():\n",
    "        plt.plot(list(value.keys()), list(value.values()), label=key)\n",
    "    plt.legend()\n",
    "    plt.xlabel('Number of topics')\n",
    "    plt.ylabel('Coherence score')\n",
    "    plt.title('Coherence score for different number of topics')\n",
    "\n",
    "    if save:\n",
    "        plt.savefig(\n",
    "            f'../diagrams/coherence_score/lda/{datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")}_coherence_score_{coherence}_for_{parties[0]}_{len(parties)}.png')\n",
    "  \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def plot_coherence_lsi(coherence, max_topics, parties, save=False):\n",
    "    corpus, id2word, words_lematized = prepare_data(parties)\n",
    "\n",
    "    statistics = {}\n",
    "    for topics in range(1, max_topics):\n",
    "        lsi_model = gensim.models.lsimodel.LsiModel(corpus=corpus,\n",
    "                                                    id2word=id2word,\n",
    "                                                    num_topics=topics,\n",
    "                                                    )\n",
    "        coherence_model_lsi = CoherenceModel(model=lsi_model, texts=words_lematized, dictionary=id2word,\n",
    "                                             coherence=coherence, corpus=corpus)\n",
    "        coherence_lsi = coherence_model_lsi.get_coherence()\n",
    "        statistics[topics] = coherence_lsi\n",
    "\n",
    "    plt.plot(list(statistics.keys()), list(statistics.values()))\n",
    "    plt.xlabel('Number of topics')\n",
    "    plt.ylabel('Coherence score')\n",
    "    plt.title('Coherence score for different number of topics')\n",
    "\n",
    "    if save:\n",
    "        plt.savefig(\n",
    "            f'../diagrams/coherence_score/lsi/{datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")}_coherence_score_{coherence}_for_{parties[0]}_{len(parties)}.png')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plot coherence for all parties"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "all_parties = [p for p in Party]\n",
    "cohernce_scores = ['u_mass', 'c_v', 'c_uci', 'c_npmi']\n",
    "max_topics = 20\n",
    "max_iterations = 225\n",
    "for coherence in cohernce_scores:\n",
    "    # for all parties\n",
    "    plot_coherence_lda(coherence, max_iterations=max_iterations, max_topics=max_topics, parties=all_parties)\n",
    "    plot_coherence_lsi(coherence, max_topics=max_topics, parties=all_parties)\n",
    "\n",
    "    # for specific parties\n",
    "    for party in all_parties:\n",
    "        plot_coherence_lda(coherence, max_iterations=max_iterations, max_topics=max_topics, parties=[party])\n",
    "        plot_coherence_lsi(coherence, max_topics=max_topics, parties=[party])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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