{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
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   "name": "python392jvsc74a57bd0c58096af5c43523aa9ce7107a4c87aa7fb5b8470e79980c25b315d1bf23c97f7",
   "display_name": "Python 3.9.2 64-bit ('MasterThesis': conda)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# 03 - Topic Modelling\n",
    "This notebook serves as an initial test for the implementation of a topic model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt, pandas as pd\n",
    "import logging\n",
    "#logging.basicConfig(filename='logs/gensim.log', format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
    "\n",
    "import sys\n",
    "sys.path.append('../scripts/')\n",
    "import valueconflicts as vc\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data \n",
    "participation_processes, contributions_gdf = vc.DataLoader.load_data()"
   ]
  },
  {
   "source": [
    "## Gensim\n",
    "This section handles topic modelling with Gensim, from another preprocessing until the very end."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "contributions_gdf[\"Contributiontext_de_preprocessed\"] = contributions_gdf[\"Contributiontext_de_preprocessed\"].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                  0\n",
       "count  13906.000000\n",
       "mean       5.145045\n",
       "std       41.921241\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        1.000000\n",
       "75%        3.000000\n",
       "max     4452.000000"
      ],
      "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>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>13906.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.145045</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>41.921241</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>4452.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "list = [str(contribution).split(\" \") for contribution in contributions_gdf[\"Contributiontext_de_preprocessed\"].to_list()]\n",
    "\n",
    "# Count word frequencies\n",
    "\n",
    "frequency = defaultdict(int)\n",
    "for text in list:\n",
    "    for token in text:\n",
    "        frequency[token] += 1\n",
    "pd.DataFrame(frequency, index=[0]).transpose().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "sportbereiches              1\n",
       "ladeeinkaufssituation       1\n",
       "helmuthhübenergangs         1\n",
       "ansprechen                  1\n",
       "verhältnisse                1\n",
       "                         ... \n",
       "autos                     423\n",
       "mehr                      551\n",
       "straße                    639\n",
       "sollen                    701\n",
       "                         4456\n",
       "Name: 0, Length: 13905, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "pd.DataFrame(frequency, index=[0]).transpose()[0].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "processed_corpus = [[token for token in text if frequency[token] > 1] for text in list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/ricoherzog/opt/anaconda3/envs/MasterThesis/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "2021-04-06 20:42:48,910 : INFO : adding document #0 to Dictionary(0 unique tokens: [])\n",
      "2021-04-06 20:42:49,009 : INFO : built Dictionary(5306 unique tokens: ['altona', 'bahnhof', 'bushaltestelle', 'direkt', 'fahrradweg']...) from 2223 documents (total 62950 corpus positions)\n",
      "2021-04-06 20:42:49,010 : INFO : Dictionary lifecycle event {'msg': \"built Dictionary(5306 unique tokens: ['altona', 'bahnhof', 'bushaltestelle', 'direkt', 'fahrradweg']...) from 2223 documents (total 62950 corpus positions)\", 'datetime': '2021-04-06T20:42:49.010539', 'gensim': '4.0.1', 'python': '3.9.2 | packaged by conda-forge | (default, Feb 21 2021, 05:02:20) \\n[Clang 11.0.1 ]', 'platform': 'macOS-11.2.3-x86_64-i386-64bit', 'event': 'created'}\n"
     ]
    }
   ],
   "source": [
    "dictionary = corpora.Dictionary(processed_corpus)\n",
    "#dictionary.filter_extremes(no_below=5, no_above=0.9)\n",
    "corpus = [dictionary.doc2bow(text) for text in processed_corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Number of unique tokens: 5306\n",
      "Number of documents: 2223\n",
      "/Users/ricoherzog/opt/anaconda3/envs/MasterThesis/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "print('Number of unique tokens: %d' % len(dictionary))\n",
    "print('Number of documents: %d' % len(corpus))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "llen\" + 0.013*\"radfahrer\" + 0.011*\"kommen\" + 0.010*\"autos\" + 0.009*\"platz\" + 0.009*\"fahren\" + 0.008*\"radweg\"\n",
      "2021-04-06 20:47:29,693 : INFO : topic #11 (0.044): 0.089*\"\" + 0.008*\"sollen\" + 0.007*\"straße\" + 0.007*\"geben\" + 0.006*\"mehr\" + 0.005*\"radfahrer\" + 0.005*\"platz\" + 0.005*\"groß\" + 0.005*\"gut\" + 0.005*\"autos\"\n",
      "2021-04-06 20:47:29,694 : INFO : topic #7 (0.064): 0.156*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"groß\" + 0.004*\"schon\"\n",
      "2021-04-06 20:47:29,695 : INFO : topic diff=0.033510, rho=0.333333\n",
      "2021-04-06 20:47:30,747 : INFO : -7.679 per-word bound, 204.9 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:30,748 : INFO : PROGRESS: pass 8, at document #2223/2223\n",
      "2021-04-06 20:47:31,436 : INFO : optimized alpha [0.033762887, 0.034287374, 0.03987954, 0.03791832, 0.035321526, 0.033766426, 0.035752267, 0.06340683, 0.032537088, 0.0320524, 0.035993077, 0.04291131, 0.041848496, 0.031086672, 0.03453794]\n",
      "2021-04-06 20:47:31,447 : INFO : topic #13 (0.031): 0.010*\"fußgänger\" + 0.010*\"autos\" + 0.010*\"\" + 0.008*\"sollen\" + 0.006*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"radfahrer\" + 0.006*\"kommen\" + 0.005*\"fahrbahn\"\n",
      "2021-04-06 20:47:31,448 : INFO : topic #9 (0.032): 0.043*\"\" + 0.019*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.009*\"fahren\" + 0.008*\"autos\" + 0.008*\"gut\" + 0.008*\"deutlich\" + 0.005*\"fußgänger\" + 0.005*\"schon\"\n",
      "2021-04-06 20:47:31,450 : INFO : topic #12 (0.042): 0.018*\"fußgänger\" + 0.016*\"\" + 0.013*\"straße\" + 0.013*\"radfahrer\" + 0.013*\"sollen\" + 0.011*\"kommen\" + 0.010*\"autos\" + 0.009*\"platz\" + 0.009*\"fahren\" + 0.008*\"radweg\"\n",
      "2021-04-06 20:47:31,451 : INFO : topic #11 (0.043): 0.089*\"\" + 0.008*\"sollen\" + 0.007*\"straße\" + 0.007*\"geben\" + 0.006*\"mehr\" + 0.005*\"radfahrer\" + 0.005*\"platz\" + 0.005*\"groß\" + 0.005*\"gut\" + 0.004*\"autos\"\n",
      "2021-04-06 20:47:31,452 : INFO : topic #7 (0.063): 0.157*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"groß\" + 0.004*\"schon\"\n",
      "2021-04-06 20:47:31,452 : INFO : topic diff=0.031645, rho=0.316228\n",
      "2021-04-06 20:47:32,389 : INFO : -7.674 per-word bound, 204.2 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:32,389 : INFO : PROGRESS: pass 9, at document #2223/2223\n",
      "2021-04-06 20:47:33,089 : INFO : optimized alpha [0.032732204, 0.033271037, 0.039049584, 0.036923207, 0.034325875, 0.032726925, 0.034739334, 0.06318025, 0.031527724, 0.030996049, 0.035046764, 0.041943703, 0.041365635, 0.030025436, 0.03350463]\n",
      "2021-04-06 20:47:33,099 : INFO : topic #13 (0.030): 0.010*\"autos\" + 0.010*\"fußgänger\" + 0.009*\"\" + 0.008*\"sollen\" + 0.006*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"radfahrer\" + 0.006*\"kommen\" + 0.005*\"fahrbahn\"\n",
      "2021-04-06 20:47:33,100 : INFO : topic #9 (0.031): 0.043*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.009*\"fahren\" + 0.008*\"autos\" + 0.008*\"gut\" + 0.008*\"deutlich\" + 0.005*\"fußgänger\" + 0.005*\"schon\"\n",
      "2021-04-06 20:47:33,101 : INFO : topic #12 (0.041): 0.019*\"fußgänger\" + 0.016*\"\" + 0.014*\"straße\" + 0.014*\"radfahrer\" + 0.013*\"sollen\" + 0.011*\"kommen\" + 0.010*\"autos\" + 0.009*\"platz\" + 0.009*\"fahren\" + 0.009*\"radweg\"\n",
      "2021-04-06 20:47:33,102 : INFO : topic #11 (0.042): 0.089*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.007*\"straße\" + 0.006*\"mehr\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.005*\"radfahrer\" + 0.004*\"gut\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:33,103 : INFO : topic #7 (0.063): 0.158*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"groß\" + 0.004*\"schon\"\n",
      "2021-04-06 20:47:33,104 : INFO : topic diff=0.029637, rho=0.301511\n",
      "2021-04-06 20:47:34,102 : INFO : -7.671 per-word bound, 203.8 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:34,103 : INFO : PROGRESS: pass 10, at document #2223/2223\n",
      "2021-04-06 20:47:34,761 : INFO : optimized alpha [0.031814646, 0.03237465, 0.03828663, 0.036049485, 0.033437856, 0.03179304, 0.033826936, 0.0629242, 0.030625058, 0.030053167, 0.03419595, 0.041064736, 0.04097565, 0.02907491, 0.032570597]\n",
      "2021-04-06 20:47:34,770 : INFO : topic #13 (0.029): 0.010*\"autos\" + 0.010*\"fußgänger\" + 0.009*\"\" + 0.008*\"sollen\" + 0.006*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"kommen\" + 0.006*\"radfahrer\" + 0.005*\"fahrbahn\"\n",
      "2021-04-06 20:47:34,771 : INFO : topic #9 (0.030): 0.043*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.009*\"fahren\" + 0.008*\"autos\" + 0.008*\"gut\" + 0.008*\"deutlich\" + 0.005*\"fußgänger\" + 0.005*\"schon\"\n",
      "2021-04-06 20:47:34,773 : INFO : topic #12 (0.041): 0.019*\"fußgänger\" + 0.016*\"\" + 0.014*\"straße\" + 0.014*\"radfahrer\" + 0.013*\"sollen\" + 0.011*\"kommen\" + 0.011*\"autos\" + 0.009*\"platz\" + 0.009*\"radweg\" + 0.009*\"fahren\"\n",
      "2021-04-06 20:47:34,774 : INFO : topic #11 (0.041): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.007*\"straße\" + 0.006*\"mehr\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"gut\" + 0.004*\"radfahrer\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:34,776 : INFO : topic #7 (0.063): 0.159*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"groß\" + 0.004*\"tempo\"\n",
      "2021-04-06 20:47:34,776 : INFO : topic diff=0.027635, rho=0.288675\n",
      "2021-04-06 20:47:35,783 : INFO : -7.668 per-word bound, 203.4 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:35,784 : INFO : PROGRESS: pass 11, at document #2223/2223\n",
      "2021-04-06 20:47:36,509 : INFO : optimized alpha [0.030987231, 0.03157396, 0.03759631, 0.035253827, 0.03263131, 0.030947492, 0.0329989, 0.06268057, 0.029807504, 0.029204778, 0.033425413, 0.04026113, 0.040654536, 0.028216816, 0.03172429]\n",
      "2021-04-06 20:47:36,519 : INFO : topic #13 (0.028): 0.010*\"autos\" + 0.010*\"fußgänger\" + 0.009*\"\" + 0.008*\"sollen\" + 0.006*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"kommen\" + 0.006*\"radfahrer\" + 0.005*\"fahrbahn\"\n",
      "2021-04-06 20:47:36,520 : INFO : topic #9 (0.029): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.009*\"fahren\" + 0.008*\"deutlich\" + 0.008*\"gut\" + 0.008*\"autos\" + 0.005*\"schon\" + 0.005*\"fußgänger\"\n",
      "2021-04-06 20:47:36,522 : INFO : topic #11 (0.040): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"straße\" + 0.006*\"mehr\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"gut\" + 0.004*\"radfahrer\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:36,523 : INFO : topic #12 (0.041): 0.019*\"fußgänger\" + 0.016*\"\" + 0.014*\"straße\" + 0.014*\"radfahrer\" + 0.013*\"sollen\" + 0.011*\"kommen\" + 0.011*\"autos\" + 0.010*\"platz\" + 0.009*\"radweg\" + 0.009*\"fahren\"\n",
      "2021-04-06 20:47:36,526 : INFO : topic #7 (0.063): 0.160*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"groß\" + 0.004*\"tempo\"\n",
      "2021-04-06 20:47:36,527 : INFO : topic diff=0.025701, rho=0.277350\n",
      "2021-04-06 20:47:37,531 : INFO : -7.666 per-word bound, 203.2 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:37,532 : INFO : PROGRESS: pass 12, at document #2223/2223\n",
      "2021-04-06 20:47:38,261 : INFO : optimized alpha [0.0302327, 0.030846696, 0.036978267, 0.034529734, 0.031902224, 0.030177122, 0.032259323, 0.062459826, 0.029073317, 0.02843638, 0.032711383, 0.03952751, 0.040424164, 0.027436946, 0.03094879]\n",
      "2021-04-06 20:47:38,271 : INFO : topic #13 (0.027): 0.010*\"autos\" + 0.010*\"fußgänger\" + 0.009*\"\" + 0.008*\"sollen\" + 0.006*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"kommen\" + 0.005*\"radfahrer\" + 0.005*\"weit\"\n",
      "2021-04-06 20:47:38,272 : INFO : topic #9 (0.028): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.009*\"fahren\" + 0.008*\"deutlich\" + 0.008*\"gut\" + 0.008*\"autos\" + 0.005*\"schon\" + 0.005*\"fußgänger\"\n",
      "2021-04-06 20:47:38,273 : INFO : topic #11 (0.040): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"straße\" + 0.006*\"mehr\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"gut\" + 0.004*\"kommen\" + 0.004*\"direkt\"\n",
      "2021-04-06 20:47:38,274 : INFO : topic #12 (0.040): 0.020*\"fußgänger\" + 0.016*\"\" + 0.015*\"straße\" + 0.015*\"radfahrer\" + 0.013*\"sollen\" + 0.011*\"autos\" + 0.011*\"kommen\" + 0.010*\"platz\" + 0.010*\"radweg\" + 0.009*\"fahren\"\n",
      "2021-04-06 20:47:38,275 : INFO : topic #7 (0.062): 0.161*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"tempo\" + 0.004*\"groß\"\n",
      "2021-04-06 20:47:38,276 : INFO : topic diff=0.024130, rho=0.267261\n",
      "2021-04-06 20:47:39,195 : INFO : -7.665 per-word bound, 202.9 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:39,195 : INFO : PROGRESS: pass 13, at document #2223/2223\n",
      "2021-04-06 20:47:39,928 : INFO : optimized alpha [0.02954041, 0.030178322, 0.036417782, 0.03386691, 0.031231726, 0.029470786, 0.03157159, 0.062237646, 0.028410146, 0.027732711, 0.03205376, 0.038854603, 0.040238544, 0.026723629, 0.030245058]\n",
      "2021-04-06 20:47:39,938 : INFO : topic #13 (0.027): 0.010*\"autos\" + 0.010*\"fußgänger\" + 0.009*\"\" + 0.008*\"sollen\" + 0.007*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"kommen\" + 0.005*\"radfahrer\" + 0.005*\"weit\"\n",
      "2021-04-06 20:47:39,939 : INFO : topic #9 (0.028): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.009*\"fahren\" + 0.008*\"gut\" + 0.008*\"deutlich\" + 0.008*\"autos\" + 0.005*\"schon\" + 0.005*\"markieren\"\n",
      "2021-04-06 20:47:39,940 : INFO : topic #11 (0.039): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"straße\" + 0.006*\"mehr\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"gut\" + 0.004*\"direkt\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:39,942 : INFO : topic #12 (0.040): 0.020*\"fußgänger\" + 0.016*\"\" + 0.015*\"radfahrer\" + 0.015*\"straße\" + 0.013*\"sollen\" + 0.011*\"autos\" + 0.011*\"kommen\" + 0.010*\"radweg\" + 0.010*\"platz\" + 0.009*\"fahren\"\n",
      "2021-04-06 20:47:39,943 : INFO : topic #7 (0.062): 0.161*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"tempo\" + 0.004*\"groß\"\n",
      "2021-04-06 20:47:39,944 : INFO : topic diff=0.022632, rho=0.258199\n",
      "2021-04-06 20:47:40,921 : INFO : -7.664 per-word bound, 202.8 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:40,922 : INFO : PROGRESS: pass 14, at document #2223/2223\n",
      "2021-04-06 20:47:41,544 : INFO : optimized alpha [0.028908981, 0.029564619, 0.035911523, 0.033256847, 0.030619621, 0.02882004, 0.030929357, 0.062008988, 0.027801637, 0.027084937, 0.031449217, 0.038234666, 0.04010804, 0.026067767, 0.029596325]\n",
      "2021-04-06 20:47:41,554 : INFO : topic #13 (0.026): 0.010*\"autos\" + 0.009*\"fußgänger\" + 0.008*\"sollen\" + 0.008*\"\" + 0.007*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.005*\"kommen\" + 0.005*\"radfahrer\" + 0.005*\"weit\"\n",
      "2021-04-06 20:47:41,555 : INFO : topic #9 (0.027): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.009*\"fahren\" + 0.008*\"gut\" + 0.008*\"deutlich\" + 0.007*\"autos\" + 0.005*\"schon\" + 0.005*\"markieren\"\n",
      "2021-04-06 20:47:41,556 : INFO : topic #11 (0.038): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"mehr\" + 0.006*\"straße\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"gut\" + 0.004*\"direkt\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:41,557 : INFO : topic #12 (0.040): 0.020*\"fußgänger\" + 0.016*\"\" + 0.015*\"radfahrer\" + 0.015*\"straße\" + 0.013*\"sollen\" + 0.011*\"autos\" + 0.011*\"kommen\" + 0.010*\"radweg\" + 0.010*\"platz\" + 0.009*\"fahren\"\n",
      "2021-04-06 20:47:41,558 : INFO : topic #7 (0.062): 0.162*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"tempo\" + 0.004*\"groß\"\n",
      "2021-04-06 20:47:41,559 : INFO : topic diff=0.021424, rho=0.250000\n",
      "2021-04-06 20:47:42,567 : INFO : -7.662 per-word bound, 202.6 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:42,568 : INFO : PROGRESS: pass 15, at document #2223/2223\n",
      "2021-04-06 20:47:43,214 : INFO : optimized alpha [0.02832749, 0.028999012, 0.035445385, 0.032697666, 0.030051474, 0.02821798, 0.030344637, 0.061806034, 0.027237747, 0.026486035, 0.030884262, 0.037665915, 0.040031362, 0.025464807, 0.028992265]\n",
      "2021-04-06 20:47:43,224 : INFO : topic #13 (0.025): 0.010*\"autos\" + 0.009*\"fußgänger\" + 0.008*\"sollen\" + 0.008*\"\" + 0.007*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.005*\"weit\" + 0.005*\"kommen\" + 0.005*\"radfahrer\"\n",
      "2021-04-06 20:47:43,225 : INFO : topic #9 (0.026): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.008*\"fahren\" + 0.008*\"gut\" + 0.008*\"deutlich\" + 0.007*\"autos\" + 0.005*\"markieren\" + 0.005*\"schon\"\n",
      "2021-04-06 20:47:43,226 : INFO : topic #11 (0.038): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"mehr\" + 0.006*\"straße\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"gut\" + 0.004*\"direkt\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:43,230 : INFO : topic #12 (0.040): 0.021*\"fußgänger\" + 0.016*\"\" + 0.016*\"radfahrer\" + 0.016*\"straße\" + 0.013*\"sollen\" + 0.011*\"autos\" + 0.011*\"kommen\" + 0.010*\"radweg\" + 0.010*\"platz\" + 0.009*\"fahren\"\n",
      "2021-04-06 20:47:43,230 : INFO : topic #7 (0.062): 0.163*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"tempo\" + 0.004*\"parkplätze\"\n",
      "2021-04-06 20:47:43,231 : INFO : topic diff=0.020336, rho=0.242536\n",
      "2021-04-06 20:47:44,154 : INFO : -7.661 per-word bound, 202.4 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:44,155 : INFO : PROGRESS: pass 16, at document #2223/2223\n",
      "2021-04-06 20:47:44,834 : INFO : optimized alpha [0.027789703, 0.028475447, 0.035014577, 0.03217961, 0.029522182, 0.027658602, 0.029796949, 0.061591696, 0.026718948, 0.025929827, 0.030357625, 0.037138205, 0.040003736, 0.024908165, 0.028433952]\n",
      "2021-04-06 20:47:44,844 : INFO : topic #13 (0.025): 0.010*\"autos\" + 0.009*\"fußgänger\" + 0.008*\"sollen\" + 0.007*\"\" + 0.007*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"weit\" + 0.005*\"kommen\" + 0.005*\"radfahrer\"\n",
      "2021-04-06 20:47:44,845 : INFO : topic #9 (0.026): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.008*\"fahren\" + 0.008*\"deutlich\" + 0.008*\"gut\" + 0.007*\"autos\" + 0.005*\"markieren\" + 0.005*\"schon\"\n",
      "2021-04-06 20:47:44,846 : INFO : topic #11 (0.037): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"mehr\" + 0.006*\"straße\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"gut\" + 0.004*\"direkt\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:44,847 : INFO : topic #12 (0.040): 0.021*\"fußgänger\" + 0.016*\"\" + 0.016*\"radfahrer\" + 0.016*\"straße\" + 0.013*\"sollen\" + 0.012*\"autos\" + 0.011*\"kommen\" + 0.010*\"radweg\" + 0.010*\"fahren\" + 0.009*\"platz\"\n",
      "2021-04-06 20:47:44,848 : INFO : topic #7 (0.062): 0.163*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"tempo\" + 0.004*\"parkplätze\"\n",
      "2021-04-06 20:47:44,849 : INFO : topic diff=0.019311, rho=0.235702\n",
      "2021-04-06 20:47:45,795 : INFO : -7.660 per-word bound, 202.2 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:45,795 : INFO : PROGRESS: pass 17, at document #2223/2223\n",
      "2021-04-06 20:47:46,416 : INFO : optimized alpha [0.02728474, 0.027986126, 0.03461973, 0.031698007, 0.02902749, 0.027142946, 0.029285494, 0.061403986, 0.026237283, 0.02541161, 0.02985879, 0.03663897, 0.040016048, 0.024389796, 0.02791021]\n",
      "2021-04-06 20:47:46,426 : INFO : topic #13 (0.024): 0.010*\"autos\" + 0.009*\"fußgänger\" + 0.008*\"sollen\" + 0.007*\"\" + 0.007*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"weit\" + 0.005*\"kommen\" + 0.005*\"radfahrer\"\n",
      "2021-04-06 20:47:46,427 : INFO : topic #9 (0.025): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.008*\"fahren\" + 0.008*\"deutlich\" + 0.008*\"gut\" + 0.007*\"autos\" + 0.005*\"markieren\" + 0.005*\"schon\"\n",
      "2021-04-06 20:47:46,428 : INFO : topic #11 (0.037): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"mehr\" + 0.006*\"straße\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"gut\" + 0.004*\"direkt\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:46,428 : INFO : topic #12 (0.040): 0.021*\"fußgänger\" + 0.017*\"radfahrer\" + 0.016*\"\" + 0.016*\"straße\" + 0.012*\"sollen\" + 0.012*\"autos\" + 0.012*\"kommen\" + 0.010*\"radweg\" + 0.010*\"fahren\" + 0.009*\"platz\"\n",
      "2021-04-06 20:47:46,429 : INFO : topic #7 (0.061): 0.164*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"tempo\" + 0.004*\"parkplätze\"\n",
      "2021-04-06 20:47:46,430 : INFO : topic diff=0.018368, rho=0.229416\n",
      "2021-04-06 20:47:47,332 : INFO : -7.659 per-word bound, 202.1 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:47,333 : INFO : PROGRESS: pass 18, at document #2223/2223\n",
      "2021-04-06 20:47:47,945 : INFO : optimized alpha [0.026814647, 0.027527351, 0.034245767, 0.03124199, 0.028566647, 0.026660364, 0.028809361, 0.06118697, 0.025785755, 0.024927076, 0.029390823, 0.036177084, 0.04006521, 0.023907796, 0.027425759]\n",
      "2021-04-06 20:47:47,955 : INFO : topic #13 (0.024): 0.010*\"autos\" + 0.009*\"fußgänger\" + 0.008*\"sollen\" + 0.007*\"\" + 0.007*\"radverkehr\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"weit\" + 0.005*\"kommen\" + 0.005*\"radfahrer\"\n",
      "2021-04-06 20:47:47,956 : INFO : topic #9 (0.025): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.008*\"fahren\" + 0.008*\"deutlich\" + 0.008*\"gut\" + 0.007*\"autos\" + 0.005*\"markieren\" + 0.005*\"schon\"\n",
      "2021-04-06 20:47:47,957 : INFO : topic #11 (0.036): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"mehr\" + 0.006*\"straße\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"direkt\" + 0.004*\"gut\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:47,958 : INFO : topic #12 (0.040): 0.021*\"fußgänger\" + 0.017*\"radfahrer\" + 0.017*\"\" + 0.016*\"straße\" + 0.012*\"sollen\" + 0.012*\"autos\" + 0.012*\"kommen\" + 0.011*\"radweg\" + 0.010*\"fahren\" + 0.009*\"platz\"\n",
      "2021-04-06 20:47:47,959 : INFO : topic #7 (0.061): 0.165*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"tempo\" + 0.004*\"parkplätze\"\n",
      "2021-04-06 20:47:47,959 : INFO : topic diff=0.017443, rho=0.223607\n",
      "2021-04-06 20:47:48,862 : INFO : -7.658 per-word bound, 202.0 perplexity estimate based on a held-out corpus of 2223 documents with 62950 words\n",
      "2021-04-06 20:47:48,863 : INFO : PROGRESS: pass 19, at document #2223/2223\n",
      "2021-04-06 20:47:49,538 : INFO : optimized alpha [0.026373187, 0.027101604, 0.03389819, 0.030815866, 0.028130364, 0.02621026, 0.02835606, 0.061011057, 0.025369378, 0.024475293, 0.028947951, 0.035744768, 0.04011548, 0.023460805, 0.026968278]\n",
      "2021-04-06 20:47:49,548 : INFO : topic #13 (0.023): 0.010*\"autos\" + 0.008*\"sollen\" + 0.008*\"fußgänger\" + 0.007*\"radverkehr\" + 0.007*\"\" + 0.006*\"geben\" + 0.006*\"ganz\" + 0.006*\"weit\" + 0.005*\"kommen\" + 0.005*\"wandsbeker\"\n",
      "2021-04-06 20:47:49,550 : INFO : topic #9 (0.024): 0.042*\"\" + 0.018*\"straße\" + 0.010*\"mehr\" + 0.009*\"sollen\" + 0.008*\"fahren\" + 0.008*\"deutlich\" + 0.008*\"gut\" + 0.007*\"autos\" + 0.005*\"markieren\" + 0.004*\"schon\"\n",
      "2021-04-06 20:47:49,550 : INFO : topic #11 (0.036): 0.090*\"\" + 0.008*\"sollen\" + 0.007*\"geben\" + 0.006*\"mehr\" + 0.006*\"straße\" + 0.005*\"groß\" + 0.005*\"platz\" + 0.004*\"direkt\" + 0.004*\"gut\" + 0.004*\"kommen\"\n",
      "2021-04-06 20:47:49,551 : INFO : topic #12 (0.040): 0.021*\"fußgänger\" + 0.017*\"radfahrer\" + 0.017*\"\" + 0.017*\"straße\" + 0.012*\"sollen\" + 0.012*\"autos\" + 0.012*\"kommen\" + 0.011*\"radweg\" + 0.010*\"fahren\" + 0.009*\"platz\"\n",
      "2021-04-06 20:47:49,553 : INFO : topic #7 (0.061): 0.165*\"\" + 0.010*\"straße\" + 0.008*\"sollen\" + 0.006*\"mehr\" + 0.005*\"geben\" + 0.004*\"nutzen\" + 0.004*\"wenig\" + 0.004*\"gut\" + 0.004*\"tempo\" + 0.004*\"parkplätze\"\n",
      "2021-04-06 20:47:49,554 : INFO : topic diff=0.016688, rho=0.218218\n",
      "2021-04-06 20:47:49,564 : INFO : LdaModel lifecycle event {'msg': 'trained LdaModel(num_terms=5306, num_topics=15, decay=0.5, chunksize=5000) in 36.34s', 'datetime': '2021-04-06T20:47:49.564118', 'gensim': '4.0.1', 'python': '3.9.2 | packaged by conda-forge | (default, Feb 21 2021, 05:02:20) \\n[Clang 11.0.1 ]', 'platform': 'macOS-11.2.3-x86_64-i386-64bit', 'event': 'created'}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Set training parameters.\n",
    "num_topics = 15\n",
    "chunksize = 5000\n",
    "passes = 20\n",
    "iterations = 15000\n",
    "eval_every = 1500  # Don't evaluate model perplexity, takes too much time.\n",
    "\n",
    "# Make a index to word dictionary.\n",
    "temp = dictionary[0]  # This is only to \"load\" the dictionary.\n",
    "id2word = dictionary.id2token\n",
    "\n",
    "model = LdaModel(\n",
    "    corpus=corpus,\n",
    "    id2word=id2word,\n",
    "    chunksize=chunksize,\n",
    "    alpha='auto',\n",
    "    eta='auto',\n",
    "    iterations=iterations,\n",
    "    num_topics=num_topics,\n",
    "    passes=passes,\n",
    "    eval_every=eval_every\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/ricoherzog/opt/anaconda3/envs/MasterThesis/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n  and should_run_async(code)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 1. Wordcloud of Top N words in each topic\n",
    "from matplotlib import pyplot as plt\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]  # more colors: 'mcolors.XKCD_COLORS'\n",
    "\n",
    "cloud = WordCloud(background_color='white',\n",
    "                  width=2500,\n",
    "                  height=1800,\n",
    "                  max_words=10,\n",
    "                  colormap='tab10',\n",
    "                  color_func=lambda *args, **kwargs: cols[i],\n",
    "                  prefer_horizontal=1.0)\n",
    "\n",
    "topics = topic_model.show_topics(formatted=False)\n",
    "\n",
    "fig, axes = plt.subplots(5, 2, figsize=(10,10), sharex=True, sharey=True)\n",
    "\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    fig.add_subplot(ax)\n",
    "    topic_words = dict(topics[i][1])\n",
    "    cloud.generate_from_frequencies(topic_words, max_font_size=300)\n",
    "    plt.gca().imshow(cloud)\n",
    "    plt.gca().set_title('Topic ' + str(i), fontdict=dict(size=16))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.axis('off')\n",
    "plt.margins(x=0, y=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## Assign the topics to the contributions"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                              Name  \\\n",
       "ID                                   \n",
       "38                        Greensam   \n",
       "39               Hindenburgstrasse   \n",
       "40              Schnackenburgallee   \n",
       "41             Magistrale Wandsbek   \n",
       "42                Wilhelmsburg-Ost   \n",
       "43  Spielplatz bei der Osterkirche   \n",
       "44                   Clever Cities   \n",
       "45    Fußverkehrskonzept St. Georg   \n",
       "46                   Jungfernstieg   \n",
       "47       Fußwegekonzept Eidelstedt   \n",
       "48        Spielplatz Op´n Hainholt   \n",
       "49      Wellingbütteler Landstraße   \n",
       "50           Radverkehr Eimsbüttel   \n",
       "51        RISE Billstedt Horn 2025   \n",
       "52    Oberbillwerder - Active City   \n",
       "53    Spielplatz Walddörfer Straße   \n",
       "54                    Elbgaustraße   \n",
       "55                     Lindenallee   \n",
       "56   Grünanlage König-Heinrich-Weg   \n",
       "57                           LAP 2   \n",
       "58          Unser Klima Eimsbüttel   \n",
       "59          Spielplatz Weidenallee   \n",
       "60     Spielplatz Glückstädter Weg   \n",
       "\n",
       "                                                 Link Completed  \\\n",
       "ID                                                                \n",
       "38              https://beteiligung.hamburg/greensam/      True   \n",
       "39     https://beteiligung.hamburg/hindenburgstrasse/      True   \n",
       "40    https://beteiligung.hamburg/schnackenburgallee/      True   \n",
       "41  https://beteiligung.hamburg/magistralen-wandsbek/      True   \n",
       "42      https://beteiligung.hamburg/wilhelmsburg-ost/      True   \n",
       "43            https://beteiligung.hamburg/bdo-altona/      True   \n",
       "44  https://beteiligung.hamburg/naturerlebnisplatz...      True   \n",
       "45  https://beteiligung.hamburg/fussverkehr-st-georg/      True   \n",
       "46         https://beteiligung.hamburg/jungfernstieg/     False   \n",
       "47   https://beteiligung.hamburg/fusswege-eidelstedt/      True   \n",
       "48            https://beteiligung.hamburg/oph-altona/      True   \n",
       "49  https://beteiligung.hamburg/wellingsbuetteler-...      True   \n",
       "50  https://beteiligung.hamburg/radverkehr-eimsbue...      True   \n",
       "51   https://beteiligung.hamburg/billstedt-horn-2025/      True   \n",
       "52  https://beteiligung.hamburg/oberbillwerderacti...     True    \n",
       "53  https://beteiligung.hamburg/spielplatz-walddoe...      True   \n",
       "54         https://beteiligung.hamburg/elbgaustrasse/     False   \n",
       "55           https://beteiligung.hamburg/lindenallee/     False   \n",
       "56       https://beteiligung.hamburg/gruenanlage-khw/     False   \n",
       "57  https://beteiligung.hamburg/zweite-oeffentlich...    False    \n",
       "58  https://beteiligung.hamburg/unser-klima-eimsbu...     False   \n",
       "59  https://beteiligung.hamburg/spielplatz-weidena...     False   \n",
       "60  https://beteiligung.hamburg/glueckstaedter-weg...     False   \n",
       "\n",
       "                                             geometry  \n",
       "ID                                                     \n",
       "38  POLYGON ((9.99443 53.55814, 10.00060 53.56575,...  \n",
       "39  POLYGON ((10.00715 53.61239, 10.01235 53.61279...  \n",
       "40  POLYGON ((9.91034 53.57642, 9.91053 53.57460, ...  \n",
       "41  POLYGON ((10.03265 53.56564, 10.03062 53.56111...  \n",
       "42  POLYGON ((10.00873 53.50714, 10.00946 53.50738...  \n",
       "43  POLYGON ((9.93338 53.55441, 9.93328 53.55446, ...  \n",
       "44  POLYGON ((9.82661 53.48124, 9.80582 53.47379, ...  \n",
       "45  POLYGON ((9.99757 53.55700, 9.99911 53.55675, ...  \n",
       "46  POLYGON ((9.99479 53.55221, 9.99278 53.55148, ...  \n",
       "47  POLYGON ((9.90770 53.58648, 9.90777 53.59005, ...  \n",
       "48  POLYGON ((9.79243 53.58529, 9.79272 53.57768, ...  \n",
       "49  POLYGON ((10.03223 53.62736, 10.03064 53.62609...  \n",
       "50  POLYGON ((9.93973 53.57974, 9.93903 53.58038, ...  \n",
       "51  POLYGON ((10.06963 53.55940, 10.07239 53.56097...  \n",
       "52  POLYGON ((10.14564 53.49340, 10.14793 53.49787...  \n",
       "53  POLYGON ((10.06735 53.57985, 10.07115 53.58022...  \n",
       "54  POLYGON ((9.88512 53.60948, 9.92009 53.59180, ...  \n",
       "55  POLYGON ((9.95795 53.56901, 9.95884 53.56921, ...  \n",
       "56  POLYGON ((9.96166 53.64247, 9.97150 53.64966, ...  \n",
       "57  POLYGON ((9.98603 53.55752, 9.99957 53.57042, ...  \n",
       "58  POLYGON ((9.94187 53.58064, 9.96344 53.58149, ...  \n",
       "59  POLYGON ((9.96127 53.56865, 9.96404 53.56957, ...  \n",
       "60  POLYGON ((9.85790 53.58920, 9.85837 53.58963, ...  "
      ],
      "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>Name</th>\n      <th>Link</th>\n      <th>Completed</th>\n      <th>geometry</th>\n    </tr>\n    <tr>\n      <th>ID</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>38</th>\n      <td>Greensam</td>\n      <td>https://beteiligung.hamburg/greensam/</td>\n      <td>True</td>\n      <td>POLYGON ((9.99443 53.55814, 10.00060 53.56575,...</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Hindenburgstrasse</td>\n      <td>https://beteiligung.hamburg/hindenburgstrasse/</td>\n      <td>True</td>\n      <td>POLYGON ((10.00715 53.61239, 10.01235 53.61279...</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Schnackenburgallee</td>\n      <td>https://beteiligung.hamburg/schnackenburgallee/</td>\n      <td>True</td>\n      <td>POLYGON ((9.91034 53.57642, 9.91053 53.57460, ...</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Magistrale Wandsbek</td>\n      <td>https://beteiligung.hamburg/magistralen-wandsbek/</td>\n      <td>True</td>\n      <td>POLYGON ((10.03265 53.56564, 10.03062 53.56111...</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>Wilhelmsburg-Ost</td>\n      <td>https://beteiligung.hamburg/wilhelmsburg-ost/</td>\n      <td>True</td>\n      <td>POLYGON ((10.00873 53.50714, 10.00946 53.50738...</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>Spielplatz bei der Osterkirche</td>\n      <td>https://beteiligung.hamburg/bdo-altona/</td>\n      <td>True</td>\n      <td>POLYGON ((9.93338 53.55441, 9.93328 53.55446, ...</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>Clever Cities</td>\n      <td>https://beteiligung.hamburg/naturerlebnisplatz...</td>\n      <td>True</td>\n      <td>POLYGON ((9.82661 53.48124, 9.80582 53.47379, ...</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Fußverkehrskonzept St. Georg</td>\n      <td>https://beteiligung.hamburg/fussverkehr-st-georg/</td>\n      <td>True</td>\n      <td>POLYGON ((9.99757 53.55700, 9.99911 53.55675, ...</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>Jungfernstieg</td>\n      <td>https://beteiligung.hamburg/jungfernstieg/</td>\n      <td>False</td>\n      <td>POLYGON ((9.99479 53.55221, 9.99278 53.55148, ...</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>Fußwegekonzept Eidelstedt</td>\n      <td>https://beteiligung.hamburg/fusswege-eidelstedt/</td>\n      <td>True</td>\n      <td>POLYGON ((9.90770 53.58648, 9.90777 53.59005, ...</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>Spielplatz Op´n Hainholt</td>\n      <td>https://beteiligung.hamburg/oph-altona/</td>\n      <td>True</td>\n      <td>POLYGON ((9.79243 53.58529, 9.79272 53.57768, ...</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>Wellingbütteler Landstraße</td>\n      <td>https://beteiligung.hamburg/wellingsbuetteler-...</td>\n      <td>True</td>\n      <td>POLYGON ((10.03223 53.62736, 10.03064 53.62609...</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>Radverkehr Eimsbüttel</td>\n      <td>https://beteiligung.hamburg/radverkehr-eimsbue...</td>\n      <td>True</td>\n      <td>POLYGON ((9.93973 53.57974, 9.93903 53.58038, ...</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>RISE Billstedt Horn 2025</td>\n      <td>https://beteiligung.hamburg/billstedt-horn-2025/</td>\n      <td>True</td>\n      <td>POLYGON ((10.06963 53.55940, 10.07239 53.56097...</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Oberbillwerder - Active City</td>\n      <td>https://beteiligung.hamburg/oberbillwerderacti...</td>\n      <td>True</td>\n      <td>POLYGON ((10.14564 53.49340, 10.14793 53.49787...</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>Spielplatz Walddörfer Straße</td>\n      <td>https://beteiligung.hamburg/spielplatz-walddoe...</td>\n      <td>True</td>\n      <td>POLYGON ((10.06735 53.57985, 10.07115 53.58022...</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>Elbgaustraße</td>\n      <td>https://beteiligung.hamburg/elbgaustrasse/</td>\n      <td>False</td>\n      <td>POLYGON ((9.88512 53.60948, 9.92009 53.59180, ...</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Lindenallee</td>\n      <td>https://beteiligung.hamburg/lindenallee/</td>\n      <td>False</td>\n      <td>POLYGON ((9.95795 53.56901, 9.95884 53.56921, ...</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Grünanlage König-Heinrich-Weg</td>\n      <td>https://beteiligung.hamburg/gruenanlage-khw/</td>\n      <td>False</td>\n      <td>POLYGON ((9.96166 53.64247, 9.97150 53.64966, ...</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>LAP 2</td>\n      <td>https://beteiligung.hamburg/zweite-oeffentlich...</td>\n      <td>False</td>\n      <td>POLYGON ((9.98603 53.55752, 9.99957 53.57042, ...</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>Unser Klima Eimsbüttel</td>\n      <td>https://beteiligung.hamburg/unser-klima-eimsbu...</td>\n      <td>False</td>\n      <td>POLYGON ((9.94187 53.58064, 9.96344 53.58149, ...</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>Spielplatz Weidenallee</td>\n      <td>https://beteiligung.hamburg/spielplatz-weidena...</td>\n      <td>False</td>\n      <td>POLYGON ((9.96127 53.56865, 9.96404 53.56957, ...</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Spielplatz Glückstädter Weg</td>\n      <td>https://beteiligung.hamburg/glueckstaedter-weg...</td>\n      <td>False</td>\n      <td>POLYGON ((9.85790 53.58920, 9.85837 53.58963, ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "participation_processes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "process_id = None # None for all processes\n",
    "num_topics = 10\n",
    "iterations = 20000\n",
    "\n",
    "if process_id is not None:\n",
    "    topic_probablities, topic_list, topic_model, dictionary, corpus = vc.TopicModeller.get_topics(contributions_gdf[\"Contributiontext_de_preprocessed\"].loc[process_id],num_topics=num_topics,iterations=iterations)\n",
    "else:\n",
    "    topic_probablities, topic_list, topic_model, dictionary, corpus = vc.TopicModeller.get_topics(contributions_gdf[\"Contributiontext_de_preprocessed\"],num_topics=num_topics,iterations=iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "7      0.038785 -0.114308       1        1  26.669198\n",
       "5      0.235743 -0.036924       2        1  17.010301\n",
       "1     -0.060325 -0.018922       3        1  11.800876\n",
       "4     -0.010972 -0.035240       4        1  10.266690\n",
       "8      0.104937 -0.004201       5        1   8.902180\n",
       "2     -0.153030 -0.160092       6        1   8.387160\n",
       "6     -0.064857 -0.053081       7        1   5.720273\n",
       "3      0.009794  0.146648       8        1   4.508741\n",
       "0      0.050328  0.120570       9        1   3.622095\n",
       "9     -0.150403  0.155550      10        1   3.112486, topic_info=           Term         Freq        Total Category  logprob  loglift\n",
       "388        Auto  1575.000000  1575.000000  Default  30.0000  30.0000\n",
       "96    Radfahrer   665.000000   665.000000  Default  29.0000  29.0000\n",
       "91    Fußgänger   635.000000   635.000000  Default  28.0000  28.0000\n",
       "843          ja   562.000000   562.000000  Default  27.0000  27.0000\n",
       "85          gut   939.000000   939.000000  Default  26.0000  26.0000\n",
       "...         ...          ...          ...      ...      ...      ...\n",
       "265      Mensch    24.199609   380.406868  Topic10  -5.1030   0.7148\n",
       "1449     nehmen    19.508428   149.264233  Topic10  -5.3185   1.4349\n",
       "1007       Idee    21.171191   290.722032  Topic10  -5.2367   0.8500\n",
       "730      finden    21.752694   367.227985  Topic10  -5.2096   0.6435\n",
       "193      Lösung    19.359058   198.768119  Topic10  -5.3262   1.1408\n",
       "\n",
       "[778 rows x 6 columns], token_table=      Topic      Freq        Term\n",
       "term                             \n",
       "2876      5  0.988192            \n",
       "1357      2  0.954251    Abbiegen\n",
       "4747      1  0.964462       Abend\n",
       "7429      9  0.866910   Abschluss\n",
       "7274      9  0.955012     Absolut\n",
       "...     ...       ...         ...\n",
       "2338      7  0.112614   überholen\n",
       "1668      6  0.918117  überlassen\n",
       "319       1  0.011171  überqueren\n",
       "319       2  0.983051  überqueren\n",
       "2086      5  0.943302  überwinden\n",
       "\n",
       "[1837 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[8, 6, 2, 5, 9, 3, 7, 4, 1, 10])"
      ],
      "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_el2177063805519689776687077\"></div>\n<script type=\"text/javascript\">\n\nvar ldavis_el2177063805519689776687077_data = {\"mdsDat\": {\"x\": [0.038784763834001555, 0.23574294168672688, -0.06032524516919639, -0.010971682181644102, 0.10493743287255486, -0.15303043047911719, -0.06485664241272227, 0.009794334213196402, 0.050327583462090514, -0.1504030558258904], \"y\": [-0.1143080468219514, -0.0369239311822173, -0.018922224227825447, -0.035240075968813594, -0.004200944694442633, -0.1600915600720785, -0.053081055226952056, 0.14664768594968094, 0.12057018957126875, 0.1555499626733312], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [26.669197804953427, 17.01030094298592, 11.800875660874663, 10.266689952655588, 8.902180408048737, 8.387160481896377, 5.720273030441822, 4.508741309482025, 3.6220945633731185, 3.112485845288321]}, \"tinfo\": {\"Term\": [\"Auto\", \"Radfahrer\", \"Fu\\u00dfg\\u00e4nger\", \"ja\", \"gut\", \"Ampel\", \"Ich\", \"Parkplatz\", \"Radweg\", \"Idee\", \"Kind\", \"mehr\", \"ganz\", \"geben\", \"mal\", \"Stellplatz\", \"jed\", \"Raum\", \"\\u00f6ffentlich\", \"Platz\", \"finden\", \"Beitrag\", \"Richtung\", \"Ja\", \"gef\\u00e4hrlich\", \"Stra\\u00dfe\", \"Mensch\", \"Jungfernstieg\", \"Baum\", \"Lindenallee\", \"Fahrer\", \"alleine\", \"Georg\", \"Kindergarten\", \"R\\u00fccksicht\", \"Kennzeichen\", \"Million\", \"spielend\", \"allermeisten\", \"besetzen\", \"Lastenrad\", \"jeglich\", \"eins\", \"lebenswert\", \"Gegen\", \"vorschreiben\", \"Beleuchtung\", \"schrecklich\", \"absurd\", \"beanspruchen\", \"Gastro\", \"Luxus\", \"ungef\\u00e4hr\", \"Parkplatzsuche\", \"wobei\", \"Abend\", \"aggressiv\", \"au\\u00dferhalb\", \"Sillemstrasse\", \"fangen\", \"pl\\u00f6tzlich\", \"St\", \"unterwegs\", \"anfahren\", \"morgens\", \"Kind\", \"teilen\", \"Fu\\u00dfg\\u00e4ngerin\", \"meist\", \"einhalten\", \"Spielstra\\u00dfe\", \"spielen\", \"vorbei\", \"Auto\", \"t\\u00e4glich\", \"Gehweg\", \"wiederum\", \"Dame\", \"Tag\", \"Spielpl\\u00e4tzen\", \"T\\u00fcr\", \"bewegen\", \"Platz\", \"Fahrrad\", \"m\\u00fcssen\", \"Mansteinstra\\u00dfe\", \"Parkplatz\", \"fahren\", \"wohnen\", \"sitzen\", \"Stra\\u00dfe\", \"benutzen\", \"parken\", \"oft\", \"geben\", \"mehr\", \"all\", \"L\\u00e4rm\", \"Wenn\", \"einfach\", \"warum\", \"Radfahrer\", \"nutzen\", \"Anwohner\", \"wenig\", \"mal\", \"Ich\", \"sollen\", \"Fu\\u00dfg\\u00e4nger\", \"schon\", \"ganz\", \"immer\", \"gehen\", \"Weg\", \"gro\\u00df\", \"kommen\", \"Stadt\", \"Radfahrende\", \"\\u00fcberqueren\", \"un\\u00fcbersichtlich\", \"Konflikt\", \"Tunnel\", \"Radfahrstreifen\", \"Ampel\", \"\\u00dcberquerung\", \"Bramfelder\", \"kreuzen\", \"Radstreifen\", \"Asphalt\", \"Bushaltestelle\", \"definieren\", \"Saseler\", \"Rot\", \"trennen\", \"Gr\\u00fcnphase\", \"z\\u00fcgig\", \"Radweg\", \"Abbiegen\", \"DooringZone\", \"Gef\\u00e4hrdung\", \"realit\\u00e4tsfern\", \"Abstimmung\", \"Schlo\\u00dfstra\\u00dfe\", \"stadteinw\\u00e4rts\", \"Fu\\u00dfg\\u00e4nger\\u00fcberweg\", \"Verbreiterung\", \"Zumutung\", \"Querung\", \"Br\\u00fccke\", \"abbiegen\", \"Kieler\", \"Zebrastreifen\", \"Kreuzung\", \"gef\\u00e4hrlich\", \"Chaussee\", \"Richtung\", \"Radwege\", \"Radfahrer\", \"Fu\\u00dfg\\u00e4nger\", \"Veloroute\", \"\\u00fcberholen\", \"warten\", \"Wandsbeker\", \"Radverkehr\", \"Fu\\u00dfweg\", \"Autoverkehr\", \"Stelle\", \"Hier\", \"schmal\", \"Weg\", \"schlecht\", \"breit\", \"Fahrbahn\", \"Stra\\u00dfe\", \"Fahrradfahrer\", \"fahren\", \"kommen\", \"Autofahrer\", \"sollen\", \"gut\", \"Bereich\", \"m\\u00fcssen\", \"geben\", \"nutzen\", \"Volle\", \"Finde\", \"Entschuldigung\", \"zun\\u00e4chst\", \"Vorstellung\", \"Stra\\u00dfenraums\", \"beide\", \"erinnern\", \"informieren\", \"beschlie\\u00dfen\", \"DANKE\", \"zugute\", \"wozu\", \"Typus\", \"steigend\", \"Debatte\", \"SPD\", \"konstruktiv\", \"verweilen\", \"Speedbumps\", \"Effekt\", \"anfangen\", \"Variante\", \"Verfasser\", \"Stra\\u00dfenbelag\", \"Aydin\", \"Tierschutz\", \"Unterst\\u00fctzung\", \"Katze\", \"Hundewiese\", \"Umsetzung\", \"Zustimmung\", \"Altonaer\", \"erscheinen\", \"genutzt\", \"Fortschritt\", \"Dank\", \"Drucksache\", \"Jungfernstieg\", \"Schritt\", \"Sehe\", \"Ma\\u00df\", \"finden\", \"autofrei\", \"jung\", \"Innenstadt\", \"gut\", \"Aufenthaltsqualit\\u00e4t\", \"bedeuten\", \"Endlich\", \"Ich\", \"mehr\", \"Lindenallee\", \"glauben\", \"Umgestaltung\", \"Kommentar\", \"denken\", \"Sache\", \"sehen\", \"richtig\", \"eher\", \"Ma\\u00dfnahme\", \"schon\", \"Beitrag\", \"ja\", \"gro\\u00df\", \"Hamburg\", \"Stra\\u00dfe\", \"Vorschlag\", \"Idee\", \"wenig\", \"weit\", \"sollen\", \"bleiben\", \"geben\", \"mal\", \"Anwohner\", \"UBahn\", \"ernsthaft\", \"Mineral\\u00f6lsteuer\", \"Redaktion\", \"Anmerkung\", \"negativ\", \"Beteiligungsplattform\", \"Wind\", \"gehbehindert\", \"ablehnen\", \"\\u00fcberdachen\", \"hinzu\", \"R\\u00fcckhalt\", \"extern\", \"Stra\\u00dfeninfrastruktur\", \"Job\", \"Oh\", \"n\", \"Elektroautos\", \"Fahrgast\", \"jen\", \"Beachten\", \"\\u00dcberdachung\", \"FAQ\", \"EBike\", \"verwehren\", \"Ebenfalls\", \"bl\\u00f6d\", \"Paris\", \"Stadtautobahnen\", \"bequemen\", \"Nachteil\", \"Zaun\", \"betreffen\", \"Siehe\", \"Eingang\", \"Taxi\", \"Hindenburgstra\\u00dfe\", \"Parkverbot\", \"oben\", \"Monat\", \"sollen\", \"Nutzung\", \"paar\", \"Gebiet\", \"entfernen\", \"schon\", \"Beitrag\", \"Park\", \"darauf\", \"geben\", \"seit\", \"immer\", \"all\", \"Lindenallee\", \"Jahr\", \"kommen\", \"Dieser\", \"siehe\", \"Ich\", \"Viertel\", \"leider\", \"gehen\", \"L\\u00f6sung\", \"gut\", \"bringen\", \"Bus\", \"Autofahrer\", \"Stra\\u00dfe\", \"fahren\", \"mal\", \"Parkplatz\", \"ja\", \"Stadt\", \"Auto\", \"Jungfernstieg\", \"Anwohner\", \"weit\", \"\", \"unterst\\u00fctzen\", \"Wahrheit\", \"Wochenmarkt\", \"einbinden\", \"Bargteheider\", \"Wissen\", \"WM\", \"Marktplatz\", \"Tats\\u00e4chlich\", \"utopisch\", \"max\", \"Utopie\", \"Verkehrsraum\", \"Beidseitig\", \"tempor\\u00e4r\", \"SBahn\", \"Ansatz\", \"Anwohnerparkpl\\u00e4tze\", \"ehemals\", \"CarSharing\", \"Nee\", \"Ideologie\", \"Fahrradverkehrs\", \"Falle\", \"selben\", \"\\u00fcberwinden\", \"Gr\\u00f6\\u00dfe\", \"Initiative\", \"fl\\u00fcssig\", \"Wandsbek\", \"qm\", \"Kennzeichenpflicht\", \"Winter\", \"erforderlich\", \"Treffpunkt\", \"Gut\", \"Markt\", \"tollen\", \"Begr\\u00fcnung\", \"Bahnhof\", \"gestalten\", \"gt\", \"Super\", \"Anbindung\", \"einladen\", \"Idee\", \"Sehr\", \"Baum\", \"Platz\", \"bieten\", \"Fl\\u00e4che\", \"gut\", \"Ausbau\", \"aufwerten\", \"Vorschlag\", \"mehr\", \"Stra\\u00dfe\", \"attraktiv\", \"Ma\\u00dfnahme\", \"schaffen\", \"Wandsbeker\", \"wenig\", \"Eine\", \"neu\", \"Durch\", \"gro\\u00df\", \"Stadt\", \"Richtung\", \"beid\", \"Ein\", \"Raum\", \"sollen\", \"all\", \"nutzen\", \"Haushalt\", \"mieten\", \"kostenlos\", \"bereiten\", \"Lastenr\\u00e4der\", \"heutzutage\", \"Bed\\u00fcrfnis\", \"subventioniert\", \"Statistik\", \"Privileg\", \"Anspruchsdenken\", \"argumentieren\", \"Egoismus\", \"\\u00fcberlassen\", \"Student\", \"ums\", \"Niemand\", \"mitbekommen\", \"Zitat\", \"Posts\", \"httpswwwhamburgmagazindeartikelhamburgsautosinzahlen\", \"legitim\", \"Stadtraums\", \"Top\", \"inhaltlich\", \"Macht\", \"befriedigen\", \"siehts\", \"gehts\", \"Sozialwohnung\", \"beleben\", \"Stellplatz\", \"kostenfrei\", \"halt\", \"privat\", \"\\u00f6ffentlich\", \"Nat\\u00fcrlich\", \"eigen\", \"Raum\", \"bezahlen\", \"zahlen\", \"Bewohner\", \"Wer\", \"Zahl\", \"besitzen\", \"Auto\", \"Allgemeinheit\", \"Parkplatz\", \"brauchen\", \"ja\", \"warum\", \"geben\", \"Kosten\", \"parken\", \"jed\", \"Mensch\", \"wenig\", \"Argument\", \"all\", \"daf\\u00fcr\", \"mehr\", \"gehen\", \"sollen\", \"Ich\", \"Stadt\", 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\"absolut\", \"Nur\", \"geben\", \"nehmen\", \"Rad\", \"kommen\", \"Fl\\u00e4che\", \"sehen\", \"In\", \"Eine\", \"Ger\\u00e4t\", \"Schnitt\", \"Automobilindustrie\", \"Link\", \"Genauso\", \"Sportanlage\", \"demnach\", \"Wahrscheinlich\", \"Sch\\u00f6nes\", \"Sitzgruppen\", \"Basketballkorb\", \"ordentliche\", \"Stange\", \"Medium\", \"wert\", \"japanische\", \"Aha\", \"Eigent\\u00fcmer\", \"Stra\\u00dfenbahn\", \"witzig\", \"Zeitung\", \"mach\", \"Halbstarke\", \"Paketdienste\", \"Hochbeet\", \"Klimmzugstange\", \"anzeigen\", \"Vergr\\u00e4mungen\", \"ergo\", \"draus\", \"Gute\", \"Anlage\", \"Laut\", \"denkbar\", \"Gerne\", \"vergleichen\", \"ausgehen\", \"Sport\", \"Geb\\u00e4ude\", \"Niendorf\", \"klasse\", \"Baum\", \"Idee\", \"unterschiedlich\", \"froh\", \"super\", \"welch\", \"Bild\", \"klingen\", \"\\u00dcbrigens\", \"ca\", \"pflanzen\", \"definitiv\", \"Ein\", \"zB\", \"\\u00e4hnlich\", \"vier\", \"Bitte\", \"In\", \"sollen\", \"sch\\u00f6n\", \"bitte\", \"Ich\", \"Stadt\", 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\"Wie\", \"Wie\", \"Wie\", \"Wie\", \"Wie\", \"Wie\", \"Wind\", \"Winter\", \"Winter\", \"Winter\", \"Wissen\", \"Wochenmarkt\", \"Zahl\", \"Zahl\", \"Zaun\", \"Zaun\", \"Zebrastreifen\", \"Zebrastreifen\", \"Zebrastreifen\", \"Zeitung\", \"Zitat\", \"Zugest\\u00e4ndnis\", \"Zumutung\", \"Zustimmung\", \"Zustimmung\", \"abbiegen\", \"abbiegen\", \"abgeben\", \"ablehnen\", \"abschleppen\", \"abschleppen\", \"absolut\", \"absolut\", \"absolut\", \"absolut\", \"absolut\", \"absolut\", \"absolut\", \"absurd\", \"aggressiv\", \"all\", \"all\", \"all\", \"all\", \"all\", \"all\", \"all\", \"all\", \"all\", \"all\", \"alleine\", \"allermeisten\", \"anfahren\", \"anfahren\", \"anfangen\", \"anschlie\\u00dfen\", \"anschlie\\u00dfen\", \"anschlie\\u00dfen\", \"anschlie\\u00dfen\", \"anst\\u00e4ndig\", \"anzeigen\", \"argumentieren\", \"attraktiv\", \"attraktiv\", \"attraktiv\", \"attraktiv\", \"attraktiv\", \"attraktiv\", \"aufs\", \"aufs\", \"aufwerten\", \"aufwerten\", \"aufwerten\", \"ausgehen\", \"ausgehen\", \"autofrei\", \"autofrei\", \"autofrei\", \"autofrei\", \"autogerecht\", \"au\\u00dferhalb\", \"au\\u00dferhalb\", \"beanspruchen\", \"bedeuten\", \"bedeuten\", \"bedeuten\", \"bedeuten\", \"bedeuten\", \"befreien\", \"befreien\", \"befriedigen\", \"behaupten\", \"beid\", \"beid\", \"beid\", \"beid\", \"beid\", \"beide\", \"beleben\", \"beleben\", \"benutzen\", \"benutzen\", \"benutzen\", \"bequemen\", \"bequemen\", \"bereiten\", \"bereits\", \"bereits\", \"bereits\", \"bereits\", \"bereits\", \"bereits\", \"bereits\", \"bereits\", \"bereits\", \"beschlie\\u00dfen\", \"besetzen\", \"besitzen\", \"besitzen\", \"besitzen\", \"beteiligen\", \"beteiligen\", \"betreffen\", \"betreffen\", \"bewegen\", \"bewegen\", \"bewegen\", \"bewegen\", \"bew\\u00e4hren\", \"bezahlen\", \"bezahlen\", \"bezahlen\", \"bezahlen\", \"bezahlen\", \"bieten\", \"bieten\", \"bieten\", \"bieten\", \"bieten\", \"bieten\", \"bieten\", \"bitte\", \"bitte\", \"bitte\", \"bitte\", \"bitte\", \"bitte\", \"bitte\", \"bitte\", \"bleiben\", \"bleiben\", \"bleiben\", \"bleiben\", \"bleiben\", \"bleiben\", \"bleiben\", \"bleiben\", \"bleiben\", \"bleiben\", \"bl\\u00f6d\", \"brauchen\", \"brauchen\", \"brauchen\", \"brauchen\", \"brauchen\", \"brauchen\", \"brauchen\", \"brauchen\", \"breit\", \"breit\", \"breit\", \"breit\", \"breit\", \"breit\", \"breit\", \"breit\", \"bringen\", \"bringen\", \"bringen\", \"bringen\", \"bringen\", \"bringen\", \"bringen\", \"ca\", \"ca\", \"ca\", \"ca\", \"ca\", \"daf\\u00fcr\", \"daf\\u00fcr\", \"daf\\u00fcr\", \"daf\\u00fcr\", \"daf\\u00fcr\", \"daf\\u00fcr\", \"daf\\u00fcr\", \"darauf\", \"darauf\", \"darauf\", \"darauf\", \"darauf\", \"dauern\", \"dauern\", \"dauern\", \"definieren\", \"definitiv\", \"definitiv\", \"definitiv\", \"definitiv\", \"demnach\", \"demokratisch\", \"denkbar\", \"denkbar\", \"denken\", \"denken\", \"denken\", \"denken\", \"denken\", \"denken\", \"denken\", \"derjenige\", \"derjenige\", \"deutlich\", \"deutlich\", \"deutlich\", \"deutlich\", \"deutlich\", \"deutlich\", \"deutlich\", \"deutlich\", \"diesbez\\u00fcglich\", \"doppeln\", \"draus\", \"d\\u00fcrfen\", \"d\\u00fcrfen\", \"d\\u00fcrfen\", \"d\\u00fcrfen\", \"d\\u00fcrfen\", \"d\\u00fcrfen\", \"d\\u00fcrfen\", \"d\\u00fcrfen\", \"d\\u00fcrfen\", \"effektiv\", \"ehemals\", \"eher\", \"eher\", \"eher\", \"eher\", \"eher\", \"eher\", \"eher\", \"eher\", \"eigen\", \"eigen\", \"eigen\", \"eigen\", \"eigen\", \"eigen\", \"eigen\", \"einbinden\", \"einfach\", \"einfach\", \"einfach\", \"einfach\", \"einfach\", \"einfach\", \"einfach\", \"einfach\", \"einhalten\", \"einhalten\", \"einladen\", \"einladen\", \"eins\", \"einzig\", \"einzig\", \"einzig\", \"einzig\", \"einzig\", \"einzig\", \"einzig\", \"einz\\u00e4unen\", \"einz\\u00e4unen\", \"empfindlich\", \"empfindlich\", \"entfernen\", \"entfernen\", \"entfernen\", \"entfernen\", \"entfernen\", \"entfernen\", \"erforderlich\", \"erforderlich\", \"ergo\", \"erhalten\", \"erhalten\", \"erhalten\", \"erhalten\", \"erhalten\", \"erhalten\", \"erhalten\", \"erhalten\", \"erinnern\", \"ernsthaft\", \"erscheinen\", \"erscheinen\", \"erz\\u00e4hlen\", \"extern\", \"fahren\", \"fahren\", \"fahren\", \"fahren\", \"fahren\", \"fahren\", \"fahren\", \"fahren\", \"fahren\", \"fallen\", \"fallen\", \"fallen\", \"fangen\", \"finden\", \"finden\", \"finden\", \"finden\", \"finden\", \"finden\", \"finden\", \"finden\", \"fl\\u00fcssig\", \"froh\", \"froh\", \"ganz\", \"ganz\", \"ganz\", \"ganz\", \"ganz\", \"ganz\", \"ganz\", \"ganz\", \"geben\", \"geben\", \"geben\", \"geben\", \"geben\", \"geben\", \"geben\", \"geben\", \"geben\", \"gef\\u00e4hrlich\", \"gef\\u00e4hrlich\", \"gehbehindert\", \"gehen\", \"gehen\", \"gehen\", \"gehen\", \"gehen\", \"gehen\", \"gehen\", \"gehen\", \"gehts\", \"generell\", \"generell\", \"generell\", \"generell\", \"generell\", \"generell\", \"genutzt\", \"genutzt\", \"gestalten\", \"gestalten\", \"gestalten\", \"gestalten\", \"gestalten\", \"gestalten\", \"gestalten\", \"gestern\", \"glauben\", \"glauben\", \"glauben\", \"glauben\", \"gro\\u00df\", \"gro\\u00df\", \"gro\\u00df\", \"gro\\u00df\", \"gro\\u00df\", \"gro\\u00df\", \"gro\\u00df\", \"gro\\u00df\", \"gt\", \"gt\", \"gut\", \"gut\", \"gut\", \"gut\", \"gut\", \"gut\", \"gut\", \"gut\", \"gut\", \"gut\", \"halt\", \"halt\", \"halt\", \"heutzutage\", \"hierher\", \"hinterher\", \"hinzu\", \"hoch\", \"hoch\", \"hoch\", \"hoch\", \"hoch\", \"hoch\", \"hoch\", \"hoch\", \"hoch\", \"httpswwwhamburgmagazindeartikelhamburgsautosinzahlen\", \"immer\", \"immer\", \"immer\", \"immer\", \"immer\", \"immer\", \"immer\", \"immer\", \"immer\", \"immer\", \"individuelle\", \"individuelle\", \"informieren\", \"inhaltlich\", \"intelligent\", \"intelligent\", \"ja\", \"ja\", \"ja\", \"ja\", \"ja\", \"ja\", \"ja\", \"ja\", \"jahrzehntelang\", \"japanische\", \"jed\", \"jed\", \"jed\", \"jed\", \"jed\", \"jed\", \"jed\", \"jed\", \"jeglich\", \"jen\", \"jung\", \"jung\", \"jung\", \"klasse\", \"klasse\", \"klein\", \"klein\", \"klein\", \"klein\", \"klein\", \"klein\", \"klein\", \"klein\", \"klein\", \"klingen\", \"klingen\", \"klingen\", \"klingen\", \"klingen\", \"klingen\", \"kommen\", \"kommen\", \"kommen\", \"kommen\", \"kommen\", \"kommen\", \"kommen\", \"kommen\", \"kommen\", \"kommentieren\", \"kommentieren\", \"konstruktiv\", \"kontrollieren\", \"kontrollieren\", \"kontrollieren\", \"kontrollieren\", \"kost\", \"kostenfrei\", \"kostenfrei\", \"kostenlos\", \"kostenlos\", \"kreuzen\", \"k\\u00fchlen\", \"lebenswert\", \"legitim\", \"leider\", \"leider\", \"leider\", \"leider\", \"leider\", \"leider\", \"leider\", \"leider\", \"leise\", \"leise\", \"l\\u00e4cherliche\", \"mach\", \"mal\", \"mal\", \"mal\", \"mal\", \"mal\", \"mal\", \"mal\", \"mal\", \"mangels\", \"max\", \"ma\\u00dfen\", \"mehr\", \"mehr\", \"mehr\", \"mehr\", \"mehr\", \"mehr\", \"mehr\", \"mehr\", \"mehr\", \"mehr\", \"meist\", \"meist\", \"meist\", \"meist\", \"meist\", \"meist\", \"menschenfreundlich\", \"menschenfreundlich\", \"mieten\", \"mitbekommen\", \"morgens\", \"morgens\", \"m\\u00fcssen\", \"m\\u00fcssen\", \"m\\u00fcssen\", \"m\\u00fcssen\", \"m\\u00fcssen\", \"m\\u00fcssen\", \"m\\u00fcssen\", \"m\\u00fcssen\", \"n\", \"negativ\", \"nehmen\", \"nehmen\", \"nehmen\", \"nehmen\", \"nehmen\", \"nehmen\", \"nehmen\", \"nehmen\", \"neu\", \"neu\", \"neu\", \"neu\", \"neu\", \"neu\", \"neu\", \"nutzen\", \"nutzen\", \"nutzen\", \"nutzen\", \"nutzen\", \"nutzen\", \"nutzen\", \"nutzen\", \"nutzen\", \"oben\", \"oben\", \"oben\", \"oben\", \"oft\", \"oft\", \"oft\", \"oft\", \"oft\", \"oft\", \"onlineBeteiligung\", \"optimieren\", \"ordentliche\", \"paar\", \"paar\", \"paar\", \"paar\", \"paar\", \"paar\", \"parken\", \"parken\", \"parken\", \"parken\", \"parken\", \"parken\", \"pflanzen\", \"pflanzen\", \"pflanzen\", \"pl\\u00f6tzlich\", \"pl\\u00f6tzlich\", \"privat\", \"privat\", \"privat\", \"privat\", \"pr\\u00e4sentieren\", \"qm\", \"qm\", \"qm\", \"radikale\", \"realit\\u00e4tsfern\", \"richtig\", \"richtig\", \"richtig\", \"richtig\", \"richtig\", \"richtig\", \"richtig\", \"rum\", \"rum\", \"r\\u00e4umlich\", \"r\\u00e4umlich\", \"sag\", \"sage\", \"sagen\", \"sagen\", \"sagen\", \"sagen\", \"sagen\", \"sagen\", \"sagen\", \"sammeln\", \"schaffen\", \"schaffen\", \"schaffen\", \"schaffen\", \"schaffen\", \"schaffen\", \"scheinbar\", \"scheinbar\", \"schlecht\", \"schlecht\", \"schlecht\", \"schlecht\", \"schlecht\", \"schlecht\", \"schmal\", \"schmal\", \"schmal\", \"schmal\", \"schmal\", \"schon\", \"schon\", \"schon\", \"schon\", \"schon\", \"schon\", \"schon\", \"schon\", \"schon\", \"schon\", \"schrecklich\", \"sch\\u00f6n\", \"sch\\u00f6n\", \"sch\\u00f6n\", \"sch\\u00f6n\", \"sch\\u00f6n\", \"sch\\u00f6n\", \"sch\\u00f6n\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"sehen\", \"seit\", \"seit\", \"seit\", \"seit\", \"seit\", \"seit\", \"selben\", \"sicherlich\", \"sicherlich\", \"sicherlich\", \"sicherlich\", \"sicherlich\", \"siehe\", \"siehe\", \"siehe\", \"siehts\", \"sitzen\", \"sitzen\", \"sitzen\", \"sitzen\", \"sofort\", \"sofort\", \"sofort\", \"sogar\", \"sogar\", \"sogar\", \"sogar\", \"sogar\", \"sogar\", \"sogar\", \"sogar\", \"sogar\", \"sollen\", \"sollen\", \"sollen\", \"sollen\", \"sollen\", \"sollen\", \"sollen\", \"sollen\", \"sollen\", \"sollen\", \"sorry\", \"sorry\", \"sozialvertr\\u00e4glich\", \"spielen\", \"spielen\", \"spielen\", \"spielend\", \"stadteinw\\u00e4rts\", \"stammen\", \"stehen\", \"stehen\", \"stehen\", \"stehen\", \"stehen\", \"stehen\", \"stehen\", \"stehen\", \"steigend\", \"sterben\", \"stimme\", \"stimme\", \"stimme\", \"stimme\", \"stimme\", \"subjektive\", \"subventioniert\", \"suchen\", \"suchen\", \"suchen\", \"suchen\", \"super\", \"super\", \"super\", \"super\", \"super\", \"teilen\", \"teilen\", \"teilen\", \"tempor\\u00e4r\", \"tollen\", \"tollen\", \"tollen\", \"tollen\", \"tollen\", \"trennen\", \"trennen\", \"tun\", \"tun\", \"tun\", \"tun\", \"tun\", \"tun\", \"tun\", \"t\\u00e4glich\", \"t\\u00e4glich\", \"t\\u00e4glich\", \"t\\u00e4glich\", \"ums\", \"unbezahlbar\", \"ungef\\u00e4hr\", \"unterschiedlich\", \"unterschiedlich\", \"unterschiedlich\", \"unterschiedlich\", \"unterschiedlich\", \"unterst\\u00fctzen\", \"unterwegs\", \"unterwegs\", \"unterwegs\", \"un\\u00fcbersichtlich\", \"utopisch\", \"verdienen\", \"verdienen\", \"vergleichen\", \"vergleichen\", \"verwehren\", \"verweilen\", \"vier\", \"vier\", \"vier\", \"voll\", \"voll\", \"voll\", \"voll\", \"voll\", \"voll\", \"vorbei\", \"vorbei\", \"vorgeschrieben\", \"vorherig\", \"vorschreiben\", \"warten\", \"warten\", \"warten\", \"warum\", \"warum\", \"warum\", \"warum\", \"warum\", \"warum\", \"weit\", \"weit\", \"weit\", \"weit\", \"weit\", \"weit\", \"weit\", \"weit\", \"weit\", \"weit\", \"weiterleiten\", \"welch\", \"welch\", \"welch\", \"welch\", \"welch\", \"welch\", \"wenig\", \"wenig\", \"wenig\", \"wenig\", \"wenig\", \"wenig\", \"wenig\", \"wenig\", \"wenig\", \"wert\", \"wiederum\", \"wiederum\", \"wirklich\", \"wirklich\", \"wirklich\", \"wirklich\", \"wirklich\", \"wirklich\", \"wirklich\", \"wirklich\", \"wirklich\", \"witzig\", \"wobei\", \"wochenlang\", \"wohnen\", \"wohnen\", \"wohnen\", \"wohnen\", \"wohnen\", \"wozu\", \"w\\u00e4hlen\", \"w\\u00e4hlen\", \"w\\u00e4hlen\", \"zB\", \"zB\", \"zB\", \"zB\", \"zB\", \"zB\", \"zB\", \"zB\", \"zB\", \"zahlen\", \"zahlen\", \"zahlen\", \"zahlen\", \"zahlen\", \"ziehen\", \"ziehen\", \"ziehen\", \"zugute\", \"zun\\u00e4chst\", \"zustimmen\", \"zustimmen\", \"zustimmen\", \"zuviel\", \"z\\u00fcgig\", \"z\\u00fcgig\", \"\\u00d6PNV\", \"\\u00d6PNV\", \"\\u00d6PNV\", \"\\u00d6PNV\", \"\\u00d6PNV\", \"\\u00d6PNV\", \"\\u00d6PNV\", \"\\u00dcberdachung\", \"\\u00dcberquerung\", \"\\u00dcbrigens\", \"\\u00dcbrigens\", \"\\u00dcbrigens\", \"\\u00e4hnlich\", \"\\u00e4hnlich\", \"\\u00e4hnlich\", \"\\u00e4hnlich\", \"\\u00e4hnlich\", \"\\u00f6ffentlich\", \"\\u00f6ffentlich\", \"\\u00f6ffentlich\", \"\\u00f6ffentlich\", \"\\u00f6ffentlich\", \"\\u00fcberdachen\", \"\\u00fcberholen\", \"\\u00fcberholen\", \"\\u00fcberholen\", \"\\u00fcberlassen\", \"\\u00fcberqueren\", \"\\u00fcberqueren\", \"\\u00fcberwinden\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [8, 6, 2, 5, 9, 3, 7, 4, 1, 10]};\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_el2177063805519689776687077\", ldavis_el2177063805519689776687077_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_el2177063805519689776687077\", ldavis_el2177063805519689776687077_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_el2177063805519689776687077\", ldavis_el2177063805519689776687077_data);\n            })\n         });\n}\n</script>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim_models.prepare(topic_model, corpus, dictionary=dictionary)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                     Topic 0   Topic 1   Topic 2   Topic 3   Topic 4  \\\n",
       "Node ID Comment ID                                                     \n",
       "23      0           0.000659  0.001109  0.000765  0.000950  0.000729   \n",
       "28      0           0.000515  0.996314  0.000598  0.000742  0.000569   \n",
       "29      0           0.001207  0.002031  0.001402  0.001739  0.990659   \n",
       "30      0           0.003291  0.005538  0.003821  0.004742  0.003639   \n",
       "31      0           0.988197  0.002532  0.001747  0.002168  0.001664   \n",
       "...                      ...       ...       ...       ...       ...   \n",
       "417     844         0.001257  0.991000  0.001459  0.001811  0.001390   \n",
       "        908         0.001370  0.002305  0.001590  0.001974  0.001515   \n",
       "427     839         0.000917  0.001543  0.001065  0.001321  0.001014   \n",
       "        866         0.990892  0.001954  0.001348  0.001673  0.001284   \n",
       "442     919         0.002970  0.004998  0.977182  0.004280  0.003284   \n",
       "\n",
       "                     Topic 5   Topic 6 Assigned Topic  \\\n",
       "Node ID Comment ID                                      \n",
       "23      0           0.000802  0.994985              6   \n",
       "28      0           0.000626  0.000637              1   \n",
       "29      0           0.001468  0.001493              4   \n",
       "30      0           0.004003  0.974966              6   \n",
       "31      0           0.001830  0.001861              0   \n",
       "...                      ...       ...            ...   \n",
       "417     844         0.001529  0.001554              1   \n",
       "        908         0.989553  0.001694              5   \n",
       "427     839         0.001115  0.993025              6   \n",
       "        866         0.001412  0.001436              0   \n",
       "442     919         0.003613  0.003673              2   \n",
       "\n",
       "                    Probability Assigned Topic  \n",
       "Node ID Comment ID                              \n",
       "23      0                             0.994985  \n",
       "28      0                             0.996314  \n",
       "29      0                             0.990659  \n",
       "30      0                             0.974966  \n",
       "31      0                             0.988197  \n",
       "...                                        ...  \n",
       "417     844                           0.991000  \n",
       "        908                           0.989553  \n",
       "427     839                           0.993025  \n",
       "        866                           0.990892  \n",
       "442     919                           0.977182  \n",
       "\n",
       "[1078 rows x 9 columns]"
      ],
      "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></th>\n      <th>Topic 0</th>\n      <th>Topic 1</th>\n      <th>Topic 2</th>\n      <th>Topic 3</th>\n      <th>Topic 4</th>\n      <th>Topic 5</th>\n      <th>Topic 6</th>\n      <th>Assigned Topic</th>\n      <th>Probability Assigned Topic</th>\n    </tr>\n    <tr>\n      <th>Node ID</th>\n      <th>Comment ID</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>23</th>\n      <th>0</th>\n      <td>0.000659</td>\n      <td>0.001109</td>\n      <td>0.000765</td>\n      <td>0.000950</td>\n      <td>0.000729</td>\n      <td>0.000802</td>\n      <td>0.994985</td>\n      <td>6</td>\n      <td>0.994985</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <th>0</th>\n      <td>0.000515</td>\n      <td>0.996314</td>\n      <td>0.000598</td>\n      <td>0.000742</td>\n      <td>0.000569</td>\n      <td>0.000626</td>\n      <td>0.000637</td>\n      <td>1</td>\n      <td>0.996314</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <th>0</th>\n      <td>0.001207</td>\n      <td>0.002031</td>\n      <td>0.001402</td>\n      <td>0.001739</td>\n      <td>0.990659</td>\n      <td>0.001468</td>\n      <td>0.001493</td>\n      <td>4</td>\n      <td>0.990659</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <th>0</th>\n      <td>0.003291</td>\n      <td>0.005538</td>\n      <td>0.003821</td>\n      <td>0.004742</td>\n      <td>0.003639</td>\n      <td>0.004003</td>\n      <td>0.974966</td>\n      <td>6</td>\n      <td>0.974966</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <th>0</th>\n      <td>0.988197</td>\n      <td>0.002532</td>\n      <td>0.001747</td>\n      <td>0.002168</td>\n      <td>0.001664</td>\n      <td>0.001830</td>\n      <td>0.001861</td>\n      <td>0</td>\n      <td>0.988197</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">417</th>\n      <th>844</th>\n      <td>0.001257</td>\n      <td>0.991000</td>\n      <td>0.001459</td>\n      <td>0.001811</td>\n      <td>0.001390</td>\n      <td>0.001529</td>\n      <td>0.001554</td>\n      <td>1</td>\n      <td>0.991000</td>\n    </tr>\n    <tr>\n      <th>908</th>\n      <td>0.001370</td>\n      <td>0.002305</td>\n      <td>0.001590</td>\n      <td>0.001974</td>\n      <td>0.001515</td>\n      <td>0.989553</td>\n      <td>0.001694</td>\n      <td>5</td>\n      <td>0.989553</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">427</th>\n      <th>839</th>\n      <td>0.000917</td>\n      <td>0.001543</td>\n      <td>0.001065</td>\n      <td>0.001321</td>\n      <td>0.001014</td>\n      <td>0.001115</td>\n      <td>0.993025</td>\n      <td>6</td>\n      <td>0.993025</td>\n    </tr>\n    <tr>\n      <th>866</th>\n      <td>0.990892</td>\n      <td>0.001954</td>\n      <td>0.001348</td>\n      <td>0.001673</td>\n      <td>0.001284</td>\n      <td>0.001412</td>\n      <td>0.001436</td>\n      <td>0</td>\n      <td>0.990892</td>\n    </tr>\n    <tr>\n      <th>442</th>\n      <th>919</th>\n      <td>0.002970</td>\n      <td>0.004998</td>\n      <td>0.977182</td>\n      <td>0.004280</td>\n      <td>0.003284</td>\n      <td>0.003613</td>\n      <td>0.003673</td>\n      <td>2</td>\n      <td>0.977182</td>\n    </tr>\n  </tbody>\n</table>\n<p>1078 rows × 9 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "topic_probablities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          Topic 0        Topic 1     Topic 2  Topic 3      Topic 4  \\\n",
       "0           Reihe            gut        Auto     Idee       sollen   \n",
       "1             gut           mehr       geben     Baum        geben   \n",
       "2              zB            Ich   Parkplatz   sollen        schon   \n",
       "3         Beitrag         finden          ja      Ich          all   \n",
       "4          sollen         Straße  Stellplatz    welch          Ich   \n",
       "5          kommen  Jungfernstieg  öffentlich      gut       kommen   \n",
       "6         Absolut           Dank        mehr      Ein        immer   \n",
       "7         bereits    Lindenallee      sollen     Gute          gut   \n",
       "8       Parkplatz          schon       wenig    Stadt  Lindenallee   \n",
       "9   Jungfernstieg             ja        Raum       In      Beitrag   \n",
       "10     KfzVerkehr          sehen         Ich       zB       Straße   \n",
       "11         Parken           groß         jed      Weg         Jahr   \n",
       "12         Stimmt     Zustimmung      Mensch      jed        gehen   \n",
       "13       deutlich          wenig         all    schön       fahren   \n",
       "14      Fußgänger         sollen    brauchen    Bitte         seit   \n",
       "15         stehen     Innenstadt       gehen    bitte    Parkplatz   \n",
       "16          gehen        Hamburg       warum    super          mal   \n",
       "17          schon           weit       eigen  Hamburg       leider   \n",
       "18          Lange        Beitrag      parken    Gerät         Auto   \n",
       "19         Stelle           Idee         Wer   finden           ja   \n",
       "\n",
       "       Topic 5  Topic 6    Topic 7     Topic 8         Topic 9  \n",
       "0    Radfahrer      mal       Auto      Straße              ja  \n",
       "1    Fußgänger     Auto     Straße         gut              Ja  \n",
       "2       Straße   stehen       Kind        mehr            ganz  \n",
       "3        Ampel       ja      geben       Platz             Ich  \n",
       "4       Radweg      Wie       mehr        Idee             gut  \n",
       "5     Richtung    Stadt  Parkplatz       wenig           Bitte  \n",
       "6       fahren   sollen      Platz      Fläche  Anwohnerparken  \n",
       "7       sollen   Mensch     fahren        Baum              Du  \n",
       "8   gefährlich     seit     sollen   gestalten             jed  \n",
       "9     Kreuzung     Jahr        Ich        Eine            Tier  \n",
       "10         Weg      MIV     müssen   Vorschlag         Prozent  \n",
       "11      kommen     ÖPNV        all    schaffen         Beitrag  \n",
       "12         gut    geben  Radfahrer    Wandsbek            weit  \n",
       "13        Hier  Hamburg    Fahrrad        Sehr        wirklich  \n",
       "14    Chaussee  Problem  Fußgänger        groß           gehen  \n",
       "15     Radwege      jed      wenig       Stadt          Mensch  \n",
       "16  Autofahrer   kommen     Gehweg  Wandsbeker            Fall  \n",
       "17  Wandsbeker      tun      schon      tollen            voll  \n",
       "18  Radverkehr    sagen        mal    Richtung          finden  \n",
       "19       geben   ziehen     nutzen         neu           Taube  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Topic 0</th>\n      <th>Topic 1</th>\n      <th>Topic 2</th>\n      <th>Topic 3</th>\n      <th>Topic 4</th>\n      <th>Topic 5</th>\n      <th>Topic 6</th>\n      <th>Topic 7</th>\n      <th>Topic 8</th>\n      <th>Topic 9</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Reihe</td>\n      <td>gut</td>\n      <td>Auto</td>\n      <td>Idee</td>\n      <td>sollen</td>\n      <td>Radfahrer</td>\n      <td>mal</td>\n      <td>Auto</td>\n      <td>Straße</td>\n      <td>ja</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>gut</td>\n      <td>mehr</td>\n      <td>geben</td>\n      <td>Baum</td>\n      <td>geben</td>\n      <td>Fußgänger</td>\n      <td>Auto</td>\n      <td>Straße</td>\n      <td>gut</td>\n      <td>Ja</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>zB</td>\n      <td>Ich</td>\n      <td>Parkplatz</td>\n      <td>sollen</td>\n      <td>schon</td>\n      <td>Straße</td>\n      <td>stehen</td>\n      <td>Kind</td>\n      <td>mehr</td>\n      <td>ganz</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Beitrag</td>\n      <td>finden</td>\n      <td>ja</td>\n      <td>Ich</td>\n      <td>all</td>\n      <td>Ampel</td>\n      <td>ja</td>\n      <td>geben</td>\n      <td>Platz</td>\n      <td>Ich</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>sollen</td>\n      <td>Straße</td>\n      <td>Stellplatz</td>\n      <td>welch</td>\n      <td>Ich</td>\n      <td>Radweg</td>\n      <td>Wie</td>\n      <td>mehr</td>\n      <td>Idee</td>\n      <td>gut</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>kommen</td>\n      <td>Jungfernstieg</td>\n      <td>öffentlich</td>\n      <td>gut</td>\n      <td>kommen</td>\n      <td>Richtung</td>\n      <td>Stadt</td>\n      <td>Parkplatz</td>\n      <td>wenig</td>\n      <td>Bitte</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Absolut</td>\n      <td>Dank</td>\n      <td>mehr</td>\n      <td>Ein</td>\n      <td>immer</td>\n      <td>fahren</td>\n      <td>sollen</td>\n      <td>Platz</td>\n      <td>Fläche</td>\n      <td>Anwohnerparken</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>bereits</td>\n      <td>Lindenallee</td>\n      <td>sollen</td>\n      <td>Gute</td>\n      <td>gut</td>\n      <td>sollen</td>\n      <td>Mensch</td>\n      <td>fahren</td>\n      <td>Baum</td>\n      <td>Du</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Parkplatz</td>\n      <td>schon</td>\n      <td>wenig</td>\n      <td>Stadt</td>\n      <td>Lindenallee</td>\n      <td>gefährlich</td>\n      <td>seit</td>\n      <td>sollen</td>\n      <td>gestalten</td>\n      <td>jed</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Jungfernstieg</td>\n      <td>ja</td>\n      <td>Raum</td>\n      <td>In</td>\n      <td>Beitrag</td>\n      <td>Kreuzung</td>\n      <td>Jahr</td>\n      <td>Ich</td>\n      <td>Eine</td>\n      <td>Tier</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>KfzVerkehr</td>\n      <td>sehen</td>\n      <td>Ich</td>\n      <td>zB</td>\n      <td>Straße</td>\n      <td>Weg</td>\n      <td>MIV</td>\n      <td>müssen</td>\n      <td>Vorschlag</td>\n      <td>Prozent</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Parken</td>\n      <td>groß</td>\n      <td>jed</td>\n      <td>Weg</td>\n      <td>Jahr</td>\n      <td>kommen</td>\n      <td>ÖPNV</td>\n      <td>all</td>\n      <td>schaffen</td>\n      <td>Beitrag</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Stimmt</td>\n      <td>Zustimmung</td>\n      <td>Mensch</td>\n      <td>jed</td>\n      <td>gehen</td>\n      <td>gut</td>\n      <td>geben</td>\n      <td>Radfahrer</td>\n      <td>Wandsbek</td>\n      <td>weit</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>deutlich</td>\n      <td>wenig</td>\n      <td>all</td>\n      <td>schön</td>\n      <td>fahren</td>\n      <td>Hier</td>\n      <td>Hamburg</td>\n      <td>Fahrrad</td>\n      <td>Sehr</td>\n      <td>wirklich</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Fußgänger</td>\n      <td>sollen</td>\n      <td>brauchen</td>\n      <td>Bitte</td>\n      <td>seit</td>\n      <td>Chaussee</td>\n      <td>Problem</td>\n      <td>Fußgänger</td>\n      <td>groß</td>\n      <td>gehen</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>stehen</td>\n      <td>Innenstadt</td>\n      <td>gehen</td>\n      <td>bitte</td>\n      <td>Parkplatz</td>\n      <td>Radwege</td>\n      <td>jed</td>\n      <td>wenig</td>\n      <td>Stadt</td>\n      <td>Mensch</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>gehen</td>\n      <td>Hamburg</td>\n      <td>warum</td>\n      <td>super</td>\n      <td>mal</td>\n      <td>Autofahrer</td>\n      <td>kommen</td>\n      <td>Gehweg</td>\n      <td>Wandsbeker</td>\n      <td>Fall</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>schon</td>\n      <td>weit</td>\n      <td>eigen</td>\n      <td>Hamburg</td>\n      <td>leider</td>\n      <td>Wandsbeker</td>\n      <td>tun</td>\n      <td>schon</td>\n      <td>tollen</td>\n      <td>voll</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Lange</td>\n      <td>Beitrag</td>\n      <td>parken</td>\n      <td>Gerät</td>\n      <td>Auto</td>\n      <td>Radverkehr</td>\n      <td>sagen</td>\n      <td>mal</td>\n      <td>Richtung</td>\n      <td>finden</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Stelle</td>\n      <td>Idee</td>\n      <td>Wer</td>\n      <td>finden</td>\n      <td>ja</td>\n      <td>geben</td>\n      <td>ziehen</td>\n      <td>nutzen</td>\n      <td>neu</td>\n      <td>Taube</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "pd.DataFrame(topic_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_list = pd.DataFrame(columns=[\"Word\", \"Weight\", \"Topic\"])\n",
    "\n",
    "for topic in range(topic_model.num_topics):\n",
    "    for word, probability in topic_model.show_topic(topic, topn=15):\n",
    "        topic_list = topic_list.append({\"Word\":word, \"Weight\":probability, \"Topic\": topic},ignore_index=True)\n",
    "        \n",
    "topic_list = topic_list.set_index(\"Topic\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                    Word\n",
       "Topic                                                   \n",
       "0      Wandsbeker Chaussee Straße sollen mehr Platz R...\n",
       "1      sollen Beitrag Bus zB Wandsbek Auto sammeln sc...\n",
       "2      gut Auto geben mehr Straße Fußgänger Mensch ko...\n",
       "3      gut Straße sollen wenig mehr Zustimmung fahren...\n",
       "4      Beitrag mehr sinnvoll bekommen Fußgänger Chaus...\n",
       "5      Wie all Kreuzung Magistrale mehr Tunnel Radfah...\n",
       "6      Rad gut Ich weit Autofahrer schon Zug Mensch W...\n",
       "7      Wandsbeker Straße gut Park Chaussee sicher att...\n",
       "8      mal Rad entlang gut kommen Platz sollen Auto f...\n",
       "9      Baum all gut fahren Parkplatz Eine Vorschlag m...\n",
       "10     Straße Auto wenig Platz Tempo sollen deutlich ...\n",
       "11     Vorschlag gut Radverkehr Straße sollen Wandsbe...\n",
       "12     sollen geben gut Auto Richtung Hier Wandsbeker...\n",
       "13     gut entlang finden Innenstadt Spur tollen Chau...\n",
       "14     sollen Straße Kreuzung gut Wandsbek nutzen gro...\n",
       "15     Kreuzung Fußgänger kommen mehr Ampel Hier Chau...\n",
       "16     Straße geben sollen Chaussee Wandsbeker nutzen...\n",
       "17     Wandsbeker Radfahrer mehr Chaussee Platz fahre...\n",
       "18     sollen Straße geben gut Fußgänger Auto mehr Wa...\n",
       "19     Fußgänger Wandsbeker geben sollen Fahrrad Ein ...\n",
       "20     Straße Auto geben sollen groß Eine Kind kommen...\n",
       "21     Radfahrer mehr Fußgänger breit sollen schaffen...\n",
       "22     sollen Fußgänger Radfahrer gut Platz schon Mag...\n",
       "23     Ampel fahren Wandsbeker mehr geben Straße Kreu...\n",
       "24     Straße Wandsbeker sollen wenig Chaussee Fußgän...\n",
       "25     Auto geben fahren Straße Ampel Weg Radfahrer B...\n",
       "26     Hier gut Straße geben Fahrrad Fußgänger Baum K...\n",
       "27     Straße Stadt Gehweg Ich sollen Bramfelder Radf...\n",
       "28     sollen mehr Richtung gut Baum ja Wandsbek alt ...\n",
       "29     gut sollen Wandsbek Wandsbeker Ich Nacht Anwoh..."
      ],
      "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>Word</th>\n    </tr>\n    <tr>\n      <th>Topic</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Wandsbeker Chaussee Straße sollen mehr Platz R...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>sollen Beitrag Bus zB Wandsbek Auto sammeln sc...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>gut Auto geben mehr Straße Fußgänger Mensch ko...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>gut Straße sollen wenig mehr Zustimmung fahren...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Beitrag mehr sinnvoll bekommen Fußgänger Chaus...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Wie all Kreuzung Magistrale mehr Tunnel Radfah...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Rad gut Ich weit Autofahrer schon Zug Mensch W...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Wandsbeker Straße gut Park Chaussee sicher att...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>mal Rad entlang gut kommen Platz sollen Auto f...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Baum all gut fahren Parkplatz Eine Vorschlag m...</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Straße Auto wenig Platz Tempo sollen deutlich ...</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Vorschlag gut Radverkehr Straße sollen Wandsbe...</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>sollen geben gut Auto Richtung Hier Wandsbeker...</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>gut entlang finden Innenstadt Spur tollen Chau...</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>sollen Straße Kreuzung gut Wandsbek nutzen gro...</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Kreuzung Fußgänger kommen mehr Ampel Hier Chau...</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Straße geben sollen Chaussee Wandsbeker nutzen...</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Wandsbeker Radfahrer mehr Chaussee Platz fahre...</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>sollen Straße geben gut Fußgänger Auto mehr Wa...</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Fußgänger Wandsbeker geben sollen Fahrrad Ein ...</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Straße Auto geben sollen groß Eine Kind kommen...</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Radfahrer mehr Fußgänger breit sollen schaffen...</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>sollen Fußgänger Radfahrer gut Platz schon Mag...</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Ampel fahren Wandsbeker mehr geben Straße Kreu...</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Straße Wandsbeker sollen wenig Chaussee Fußgän...</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Auto geben fahren Straße Ampel Weg Radfahrer B...</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Hier gut Straße geben Fahrrad Fußgänger Baum K...</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Straße Stadt Gehweg Ich sollen Bramfelder Radf...</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>sollen mehr Richtung gut Baum ja Wandsbek alt ...</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>gut sollen Wandsbek Wandsbeker Ich Nacht Anwoh...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "topic_list.groupby(\"Topic\").agg({'Word': ' '.join})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the Contribution DataFrame with the topics\n",
    "if process_id is not None:\n",
    "    contributions_topics_gdf = contributions_gdf.loc[process_id].merge(topic_probablities[[\"Assigned Topic\", \"Probability Assigned Topic\"]], on=[\"Node ID\", \"Comment ID\"])\n",
    "    contributions_topics_gdf[\"Assigned Topic\"] = contributions_topics_gdf[\"Assigned Topic\"].astype(int)\n",
    "else:\n",
    "    contributions_topics_gdf = contributions_gdf.merge(topic_probablities[[\"Assigned Topic\", \"Probability Assigned Topic\"]], on=[\"Process ID\",\"Node ID\", \"Comment ID\"])\n",
    "    contributions_topics_gdf[\"Assigned Topic\"] = contributions_topics_gdf[\"Assigned Topic\"].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([['Bitte schnell das Chaos um den U-Bahnhof Trabrennbahn beenden, bevor es hier noch ein größeres Unglück gibt. Am besten die komplette Eckerkoppel der Veloroute 6 zur Fahrradstraße machen, und das ganz schnell.'],\n",
       "       ['Ich wohne in der Wandsbeker Chaussee, an der Kreuzung  Wartenau/Landwehr. Kurz nach der Ampel stadtauswärts beschleunigen viele Autos auf deutlich über 50 km/h, bremsen kurz vor dem Blitzer an der Richardstraße ab, um dann weiter Gas zu geben. In der Richtung stadteinwärts ist es ähnlich. Der Blitzer ist nur ein kurzes Hindernis.\\nDie Geschäfte, die da sind, werden von Kunden bevorzugt, die gern in zweiter Reihe mitten auf der Straße halten. Wichtig scheint zu sein, dass die anderen Kunden im Geschäft das Auto ständig im Blick haben...\\nEs wäre schön, wenn die Wandsbeker Chaussee nach dem Vorbild der Fuhlsbütteler Straße (?) beruhigt würde. Der Zeitverlust für Durchfahrten ist geringt, der Gewinn an Ästhetik und Lebensqualität für alle enorm. Dann lohnte sich auch das Ansiedeln von vielfältigen Geschäften, da die Mehrheit der Menschen dann langsam unterwegs wäre (Fußgänger, Radfahrer) und zum Einkauf/Verzehr animiert werden könnte.'],\n",
       "       ['das wird hier schon alles ernst genommen denke ich...also gern was konstruktives...'],\n",
       "       ['Interessanter Punkt den Sie da ansprechen. Sicherlich lassen sich so auch einige höhere Gebäude an dieser Magistrale erklären. Ich denke aber, dass (unabhängig des Verdichtungsargumentes) die Markierung bestimmter (wichtiger) Orte durch städtebaulich auffallende Gebäude auch für alle anderen Verkehrsteilnehmer*innen praktisch/interessant sein können.'],\n",
       "       ['-&gt; Bindestrich und Rechtspfeil'],\n",
       "       ['Gut beschrieben! Alles seit sehr langer Zeit bekannt. Leider bisher kein Wille vorhanden gewesen um auch nur über minimale Eingriffe die Situation für Fußgänger, Radfahrer und Anwohner erträglicher zu machen.\\n\\nEin gutes Beispiel für Ihre Idee liegt Luftlinie keine 200m weg. Die Bürgerweide kommt dem recht nah. Wichtig sind qualitativ hochwertige Nebenflächen die auch zum Aufenthalt einladen. Daneben eine konsequente Parkraumbewirtschaftung und Kontrolle. Zeit das sich was ändert!'],\n",
       "       ['Fahrräder, zunehmend auch pedelecs entlasten den Verkehr. \\nNur auf die Magistralen geschaut, braucht es daher gut asphaltierte, durchgängige und geschützte Fahrradspuren in beiden Richtungen. Der Ausbau der S-Bahn wird den Autoverkehr weiter entlasten.\\nEin Gesamtkonzept für Magistralen und Fahrradstraßen ist allerdings dringend geboten. \\nSo ist wegen des Feinstaubs ist eine echte Fahrradstraße mit Bus- und Anliefererlaubnis ohne Poller vorzuziehen. Die Walddörferstraße bietet sich trotz bisheriger Bedenken nach wie vor dafür an zwischen dem Friedrich-Ebert-Damm und der Ahrensburger Straße. Die anliegenden Geschäfte der Walddörferstraße gewinnen dadurch sogar an Publikum, wie die Errichtung von Fußgängerzonen andernorts in der Vergangenheit gezeigt hat. \\nEine vorrangig Fahrradstraße schützt die Radfahrer vor Unfällen und gesundheitsschädlichen Emissionen während der gesamten Fahrt.'],\n",
       "       ['Die Planer scheinen in eine ähnliche Richtung zu denken. In den \"Ersten Ideen\" zum Bereich Wandsbek Zentrum wird die Frage aufgeworfen \"Soll/kann die Schloßstraße zwischen der Wandsbeker Marktstraße und dem Ring 2 zurückgebaut werden?\"'],\n",
       "       ['entschuldigung -&gt; ich glaube das auch!'],\n",
       "       ['Tempo 30 zumindest in der Nacht halte ich für eine sehr sinnvollen Idee die ohne große Kosten (außer ein paar Verkehrsschildern) einen großen Effekt für die Anwohner hat die dann ruhiger schlafen könnten.']],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "contributions_topics_gdf[[\"Contributiontext_de\"]][contributions_topics_gdf[\"Assigned Topic\"] == 4].sample(10).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                count      mean       std       min       25%       50%  \\\n",
       "Assigned Topic                                                            \n",
       "0                65.0  0.604978  0.209476  0.239139  0.431881  0.555644   \n",
       "1                72.0  0.550512  0.164985  0.274614  0.426833  0.540121   \n",
       "2               200.0  0.548537  0.187751  0.240725  0.407117  0.522978   \n",
       "3               236.0  0.552467  0.178812  0.218923  0.432927  0.520424   \n",
       "4               280.0  0.543869  0.160385  0.201006  0.424993  0.517981   \n",
       "5               143.0  0.566784  0.186497  0.241571  0.417211  0.533270   \n",
       "6               967.0  0.597995  0.181048  0.128608  0.464429  0.571744   \n",
       "7                61.0  0.535507  0.157173  0.204169  0.400725  0.538193   \n",
       "8                48.0  0.564745  0.176261  0.282940  0.445690  0.495156   \n",
       "9                78.0  0.543306  0.169432  0.217438  0.433077  0.523826   \n",
       "10               48.0  0.619101  0.188256  0.236671  0.488136  0.564896   \n",
       "11               61.0  0.607034  0.177699  0.299429  0.484572  0.609418   \n",
       "12              113.0  0.553751  0.177502  0.243272  0.416860  0.519626   \n",
       "13               97.0  0.551206  0.176545  0.201419  0.419160  0.529025   \n",
       "14              117.0  0.555861  0.173728  0.254879  0.417033  0.543760   \n",
       "\n",
       "                     75%       max  \n",
       "Assigned Topic                      \n",
       "0               0.788171  0.965643  \n",
       "1               0.647555  0.977521  \n",
       "2               0.693621  0.987014  \n",
       "3               0.659105  0.986522  \n",
       "4               0.642680  0.973005  \n",
       "5               0.691698  0.964635  \n",
       "6               0.722012  0.990098  \n",
       "7               0.632995  0.930826  \n",
       "8               0.709258  0.943836  \n",
       "9               0.587680  0.976186  \n",
       "10              0.754919  0.979587  \n",
       "11              0.708672  0.980914  \n",
       "12              0.672351  0.972907  \n",
       "13              0.661755  0.958274  \n",
       "14              0.657878  0.971590  "
      ],
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>Assigned Topic</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65.0</td>\n      <td>0.604978</td>\n      <td>0.209476</td>\n      <td>0.239139</td>\n      <td>0.431881</td>\n      <td>0.555644</td>\n      <td>0.788171</td>\n      <td>0.965643</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>72.0</td>\n      <td>0.550512</td>\n      <td>0.164985</td>\n      <td>0.274614</td>\n      <td>0.426833</td>\n      <td>0.540121</td>\n      <td>0.647555</td>\n      <td>0.977521</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>200.0</td>\n      <td>0.548537</td>\n      <td>0.187751</td>\n      <td>0.240725</td>\n      <td>0.407117</td>\n      <td>0.522978</td>\n      <td>0.693621</td>\n      <td>0.987014</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>236.0</td>\n      <td>0.552467</td>\n      <td>0.178812</td>\n      <td>0.218923</td>\n      <td>0.432927</td>\n      <td>0.520424</td>\n      <td>0.659105</td>\n      <td>0.986522</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>280.0</td>\n      <td>0.543869</td>\n      <td>0.160385</td>\n      <td>0.201006</td>\n      <td>0.424993</td>\n      <td>0.517981</td>\n      <td>0.642680</td>\n      <td>0.973005</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>143.0</td>\n      <td>0.566784</td>\n      <td>0.186497</td>\n      <td>0.241571</td>\n      <td>0.417211</td>\n      <td>0.533270</td>\n      <td>0.691698</td>\n      <td>0.964635</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>967.0</td>\n      <td>0.597995</td>\n      <td>0.181048</td>\n      <td>0.128608</td>\n      <td>0.464429</td>\n      <td>0.571744</td>\n      <td>0.722012</td>\n      <td>0.990098</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>61.0</td>\n      <td>0.535507</td>\n      <td>0.157173</td>\n      <td>0.204169</td>\n      <td>0.400725</td>\n      <td>0.538193</td>\n      <td>0.632995</td>\n      <td>0.930826</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>48.0</td>\n      <td>0.564745</td>\n      <td>0.176261</td>\n      <td>0.282940</td>\n      <td>0.445690</td>\n      <td>0.495156</td>\n      <td>0.709258</td>\n      <td>0.943836</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>78.0</td>\n      <td>0.543306</td>\n      <td>0.169432</td>\n      <td>0.217438</td>\n      <td>0.433077</td>\n      <td>0.523826</td>\n      <td>0.587680</td>\n      <td>0.976186</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>48.0</td>\n      <td>0.619101</td>\n      <td>0.188256</td>\n      <td>0.236671</td>\n      <td>0.488136</td>\n      <td>0.564896</td>\n      <td>0.754919</td>\n      <td>0.979587</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>61.0</td>\n      <td>0.607034</td>\n      <td>0.177699</td>\n      <td>0.299429</td>\n      <td>0.484572</td>\n      <td>0.609418</td>\n      <td>0.708672</td>\n      <td>0.980914</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>113.0</td>\n      <td>0.553751</td>\n      <td>0.177502</td>\n      <td>0.243272</td>\n      <td>0.416860</td>\n      <td>0.519626</td>\n      <td>0.672351</td>\n      <td>0.972907</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>97.0</td>\n      <td>0.551206</td>\n      <td>0.176545</td>\n      <td>0.201419</td>\n      <td>0.419160</td>\n      <td>0.529025</td>\n      <td>0.661755</td>\n      <td>0.958274</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>117.0</td>\n      <td>0.555861</td>\n      <td>0.173728</td>\n      <td>0.254879</td>\n      <td>0.417033</td>\n      <td>0.543760</td>\n      <td>0.657878</td>\n      <td>0.971590</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "contributions_topics_gdf.groupby(\"Assigned Topic\")[\"Probability Assigned Topic\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/ricoherzog/opt/anaconda3/envs/MasterThesis/lib/python3.9/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n  and should_run_async(code)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "count    1497.000000\n",
       "mean        0.869167\n",
       "std         0.163910\n",
       "min         0.153264\n",
       "25%         0.799239\n",
       "50%         0.955524\n",
       "75%         0.982062\n",
       "max         0.996233\n",
       "Name: Probability Assigned Topic, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "contributions_topics_gdf[\"Probability Assigned Topic\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                     Category                     Rubric  \\\n",
       "Node ID Comment ID                                         \n",
       "23      0           Städtebau  Idee, Vorschlag, Anregung   \n",
       "28      0            Freiraum  Idee, Vorschlag, Anregung   \n",
       "29      0             Verkehr                     Kritik   \n",
       "30      0             Verkehr  Idee, Vorschlag, Anregung   \n",
       "31      0             Verkehr                     Kritik   \n",
       "...                       ...                        ...   \n",
       "417     844              None                       None   \n",
       "        908              None                       None   \n",
       "427     839              None                       None   \n",
       "        866              None                       None   \n",
       "442     919              None                       None   \n",
       "\n",
       "                                Created (UTC)    Rating  Total votes  Upvotes  \\\n",
       "Node ID Comment ID                                                              \n",
       "23      0           2020-06-23T16:04:45+00:00  0.200000          5.0      3.0   \n",
       "28      0           2020-06-25T09:39:03+00:00  0.866667         15.0     14.0   \n",
       "29      0           2020-06-25T11:20:57+00:00  0.939394         33.0     32.0   \n",
       "30      0           2020-06-25T12:10:39+00:00  0.333333          3.0      2.0   \n",
       "31      0           2020-06-25T12:45:41+00:00  1.000000          8.0      8.0   \n",
       "...                                       ...       ...          ...      ...   \n",
       "417     844         2020-08-12T10:32:39+00:00       NaN          NaN      NaN   \n",
       "        908         2020-08-16T11:04:00+00:00       NaN          NaN      NaN   \n",
       "427     839         2020-08-12T10:00:01+00:00       NaN          NaN      NaN   \n",
       "        866         2020-08-12T14:56:16+00:00       NaN          NaN      NaN   \n",
       "442     919         2020-08-17T03:30:42+00:00       NaN          NaN      NaN   \n",
       "\n",
       "                    Downvotes                  Last Vote  \\\n",
       "Node ID Comment ID                                         \n",
       "23      0                 2.0  2020-07-28T17:12:56+00:00   \n",
       "28      0                 1.0  2020-07-24T21:18:07+00:00   \n",
       "29      0                 1.0  2020-08-06T08:47:42+00:00   \n",
       "30      0                 1.0  2020-07-04T10:09:06+00:00   \n",
       "31      0                 0.0  2020-08-09T11:27:17+00:00   \n",
       "...                       ...                        ...   \n",
       "417     844               NaN                       None   \n",
       "        908               NaN                       None   \n",
       "427     839               NaN                       None   \n",
       "        866               NaN                       None   \n",
       "442     919               NaN                       None   \n",
       "\n",
       "                                                             Title_de  \\\n",
       "Node ID Comment ID                                                      \n",
       "23      0                                 Die Magistralen in Wandsbek   \n",
       "28      0           Mehr Lebensqualität an der Wandsbeker Chaussee...   \n",
       "29      0                       Fahrradstraße Eckerkoppel Veloroute 6   \n",
       "30      0                   Grüne Mittelstreifen zur Temporeduzierung   \n",
       "31      0           Linksabbiegemöglichkeit für Radfahrende zum Fi...   \n",
       "...                                                               ...   \n",
       "417     844                                        Antwort an Nr. 818   \n",
       "        908                                        Antwort an Nr. 844   \n",
       "427     839                                        Antwort an Nr. 831   \n",
       "        866                                        Antwort an Nr. 839   \n",
       "442     919                                        Antwort an Nr. 913   \n",
       "\n",
       "                                                  Contributiontext_de  ...  \\\n",
       "Node ID Comment ID                                                     ...   \n",
       "23      0           Ohne Magistralen können Verkehrs-\"flüsse\" bish...  ...   \n",
       "28      0           Die heutige 6-Spurigkeit der Wandsbeker Chauss...  ...   \n",
       "29      0           Hallo,\\n\\nda die Eckerkoppel, zwischen Tegelwe...  ...   \n",
       "30      0           Nichts lädt mehr zum Gasgeben ein als breite A...  ...   \n",
       "31      0           Es gibt heute keine direkte Linksabbiegemöglic...  ...   \n",
       "...                                                               ...  ...   \n",
       "417     844         Es ist eine Magistrale mit mehr als genug Fläc...  ...   \n",
       "        908         Die genannten Maßnahmen sind gut und richtig, ...  ...   \n",
       "427     839         Während Fußgänger und Radfahrer sich bisher sp...  ...   \n",
       "        866         Genau, meistens wird der Gehweg ausreichend Br...  ...   \n",
       "442     919         Am Doraustieg ist keine Ampel vorhanden. Entwe...  ...   \n",
       "\n",
       "                    Contributiontext_translation_error  \\\n",
       "Node ID Comment ID                                       \n",
       "23      0                                        False   \n",
       "28      0                                        False   \n",
       "29      0                                        False   \n",
       "30      0                                        False   \n",
       "31      0                                        False   \n",
       "...                                                ...   \n",
       "417     844                                      False   \n",
       "        908                                      False   \n",
       "427     839                                      False   \n",
       "        866                                      False   \n",
       "442     919                                      False   \n",
       "\n",
       "                                     Contributiontext_de_preprocessed  \\\n",
       "Node ID Comment ID                                                      \n",
       "23      0           Magistralen Verkehrsflüsse bisher meistern Urb...   \n",
       "28      0           heutige Wandsbeker Chaussee mehr zeitgemäß Hin...   \n",
       "29      0           Hallo Eckerkoppel Tegelweg Am Stadtrand aufgru...   \n",
       "30      0           Nichts laden mehr Gasgeben breit Asphaltfläche...   \n",
       "31      0           geben heute direkt Linksabbiegemöglichkeit Rad...   \n",
       "...                                                               ...   \n",
       "417     844         Magistrale mehr genug Fläche fahren fertigen M...   \n",
       "        908         genannt Maßnahme gut richtig schließen Begrenz...   \n",
       "427     839         Fußgänger Radfahrer bisher spärlich Platz teil...   \n",
       "        866         Genau meistens Gehweg ausreichen Breit Hochboa...   \n",
       "442     919         Am Doraustieg Ampel vorhanden entweder Beitrag...   \n",
       "\n",
       "                                     Contributiontext_en_preprocessed    neg  \\\n",
       "Node ID Comment ID                                                             \n",
       "23      0           traffic flow master without main road accord R...  0.054   \n",
       "28      0           Todays Wandsbeker Chaussee long date addition ...  0.028   \n",
       "29      0           hello since Eckerkoppel Tegelweg Am Stadtrand ...  0.067   \n",
       "30      0           nothing invite accelerate wide asphalt surface...  0.000   \n",
       "31      0           today direct left turn option cyclist Meridian...  0.106   \n",
       "...                                                               ...    ...   \n",
       "417     844         main road enough space drive h h madness kill ...  0.184   \n",
       "        908         measure mention good correct rule limit km h c...  0.040   \n",
       "427     839         pedestrian cyclist share sparse space create c...  0.062   \n",
       "        866         exactly time sidewalk become sufficiently wide...  0.023   \n",
       "442     919         traffic light Doraustieg either post mean traf...  0.064   \n",
       "\n",
       "                      neu    pos compound                      geometry  \\\n",
       "Node ID Comment ID                                                        \n",
       "23      0           0.784  0.162   0.9642     POINT (10.06238 53.57109)   \n",
       "28      0           0.972  0.000  -0.3595     POINT (10.04983 53.56831)   \n",
       "29      0           0.873  0.060  -0.1361     POINT (10.10440 53.59802)   \n",
       "30      0           1.000  0.000   0.0000  POINT (-117.27194 -40.06000)   \n",
       "31      0           0.868  0.026  -0.6800     POINT (10.08060 53.57627)   \n",
       "...                   ...    ...      ...                           ...   \n",
       "417     844         0.779  0.037  -0.8880                          None   \n",
       "        908         0.677  0.283   0.9547                          None   \n",
       "427     839         0.790  0.148   0.7632                          None   \n",
       "        866         0.868  0.109   0.6249                          None   \n",
       "442     919         0.936  0.000  -0.2960                          None   \n",
       "\n",
       "                    Assigned Topic  Probability Assigned Topic  \n",
       "Node ID Comment ID                                              \n",
       "23      0                        6                    0.994985  \n",
       "28      0                        1                    0.996314  \n",
       "29      0                        4                    0.990659  \n",
       "30      0                        6                    0.974966  \n",
       "31      0                        0                    0.988197  \n",
       "...                            ...                         ...  \n",
       "417     844                      1                    0.991000  \n",
       "        908                      5                    0.989553  \n",
       "427     839                      6                    0.993025  \n",
       "        866                      0                    0.990892  \n",
       "442     919                      2                    0.977182  \n",
       "\n",
       "[1078 rows x 29 columns]"
      ],
      "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></th>\n      <th>Category</th>\n      <th>Rubric</th>\n      <th>Created (UTC)</th>\n      <th>Rating</th>\n      <th>Total votes</th>\n      <th>Upvotes</th>\n      <th>Downvotes</th>\n      <th>Last Vote</th>\n      <th>Title_de</th>\n      <th>Contributiontext_de</th>\n      <th>...</th>\n      <th>Contributiontext_translation_error</th>\n      <th>Contributiontext_de_preprocessed</th>\n      <th>Contributiontext_en_preprocessed</th>\n      <th>neg</th>\n      <th>neu</th>\n      <th>pos</th>\n      <th>compound</th>\n      <th>geometry</th>\n      <th>Assigned Topic</th>\n      <th>Probability Assigned Topic</th>\n    </tr>\n    <tr>\n      <th>Node ID</th>\n      <th>Comment ID</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>23</th>\n      <th>0</th>\n      <td>Städtebau</td>\n      <td>Idee, Vorschlag, Anregung</td>\n      <td>2020-06-23T16:04:45+00:00</td>\n      <td>0.200000</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>2020-07-28T17:12:56+00:00</td>\n      <td>Die Magistralen in Wandsbek</td>\n      <td>Ohne Magistralen können Verkehrs-\"flüsse\" bish...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>Magistralen Verkehrsflüsse bisher meistern Urb...</td>\n      <td>traffic flow master without main road accord R...</td>\n      <td>0.054</td>\n      <td>0.784</td>\n      <td>0.162</td>\n      <td>0.9642</td>\n      <td>POINT (10.06238 53.57109)</td>\n      <td>6</td>\n      <td>0.994985</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <th>0</th>\n      <td>Freiraum</td>\n      <td>Idee, Vorschlag, Anregung</td>\n      <td>2020-06-25T09:39:03+00:00</td>\n      <td>0.866667</td>\n      <td>15.0</td>\n      <td>14.0</td>\n      <td>1.0</td>\n      <td>2020-07-24T21:18:07+00:00</td>\n      <td>Mehr Lebensqualität an der Wandsbeker Chaussee...</td>\n      <td>Die heutige 6-Spurigkeit der Wandsbeker Chauss...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>heutige Wandsbeker Chaussee mehr zeitgemäß Hin...</td>\n      <td>Todays Wandsbeker Chaussee long date addition ...</td>\n      <td>0.028</td>\n      <td>0.972</td>\n      <td>0.000</td>\n      <td>-0.3595</td>\n      <td>POINT (10.04983 53.56831)</td>\n      <td>1</td>\n      <td>0.996314</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <th>0</th>\n      <td>Verkehr</td>\n      <td>Kritik</td>\n      <td>2020-06-25T11:20:57+00:00</td>\n      <td>0.939394</td>\n      <td>33.0</td>\n      <td>32.0</td>\n      <td>1.0</td>\n      <td>2020-08-06T08:47:42+00:00</td>\n      <td>Fahrradstraße Eckerkoppel Veloroute 6</td>\n      <td>Hallo,\\n\\nda die Eckerkoppel, zwischen Tegelwe...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>Hallo Eckerkoppel Tegelweg Am Stadtrand aufgru...</td>\n      <td>hello since Eckerkoppel Tegelweg Am Stadtrand ...</td>\n      <td>0.067</td>\n      <td>0.873</td>\n      <td>0.060</td>\n      <td>-0.1361</td>\n      <td>POINT (10.10440 53.59802)</td>\n      <td>4</td>\n      <td>0.990659</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <th>0</th>\n      <td>Verkehr</td>\n      <td>Idee, Vorschlag, Anregung</td>\n      <td>2020-06-25T12:10:39+00:00</td>\n      <td>0.333333</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2020-07-04T10:09:06+00:00</td>\n      <td>Grüne Mittelstreifen zur Temporeduzierung</td>\n      <td>Nichts lädt mehr zum Gasgeben ein als breite A...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>Nichts laden mehr Gasgeben breit Asphaltfläche...</td>\n      <td>nothing invite accelerate wide asphalt surface...</td>\n      <td>0.000</td>\n      <td>1.000</td>\n      <td>0.000</td>\n      <td>0.0000</td>\n      <td>POINT (-117.27194 -40.06000)</td>\n      <td>6</td>\n      <td>0.974966</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <th>0</th>\n      <td>Verkehr</td>\n      <td>Kritik</td>\n      <td>2020-06-25T12:45:41+00:00</td>\n      <td>1.000000</td>\n      <td>8.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n      <td>2020-08-09T11:27:17+00:00</td>\n      <td>Linksabbiegemöglichkeit für Radfahrende zum Fi...</td>\n      <td>Es gibt heute keine direkte Linksabbiegemöglic...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>geben heute direkt Linksabbiegemöglichkeit Rad...</td>\n      <td>today direct left turn option cyclist Meridian...</td>\n      <td>0.106</td>\n      <td>0.868</td>\n      <td>0.026</td>\n      <td>-0.6800</td>\n      <td>POINT (10.08060 53.57627)</td>\n      <td>0</td>\n      <td>0.988197</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">417</th>\n      <th>844</th>\n      <td>None</td>\n      <td>None</td>\n      <td>2020-08-12T10:32:39+00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>None</td>\n      <td>Antwort an Nr. 818</td>\n      <td>Es ist eine Magistrale mit mehr als genug Fläc...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>Magistrale mehr genug Fläche fahren fertigen M...</td>\n      <td>main road enough space drive h h madness kill ...</td>\n      <td>0.184</td>\n      <td>0.779</td>\n      <td>0.037</td>\n      <td>-0.8880</td>\n      <td>None</td>\n      <td>1</td>\n      <td>0.991000</td>\n    </tr>\n    <tr>\n      <th>908</th>\n      <td>None</td>\n      <td>None</td>\n      <td>2020-08-16T11:04:00+00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>None</td>\n      <td>Antwort an Nr. 844</td>\n      <td>Die genannten Maßnahmen sind gut und richtig, ...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>genannt Maßnahme gut richtig schließen Begrenz...</td>\n      <td>measure mention good correct rule limit km h c...</td>\n      <td>0.040</td>\n      <td>0.677</td>\n      <td>0.283</td>\n      <td>0.9547</td>\n      <td>None</td>\n      <td>5</td>\n      <td>0.989553</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">427</th>\n      <th>839</th>\n      <td>None</td>\n      <td>None</td>\n      <td>2020-08-12T10:00:01+00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>None</td>\n      <td>Antwort an Nr. 831</td>\n      <td>Während Fußgänger und Radfahrer sich bisher sp...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>Fußgänger Radfahrer bisher spärlich Platz teil...</td>\n      <td>pedestrian cyclist share sparse space create c...</td>\n      <td>0.062</td>\n      <td>0.790</td>\n      <td>0.148</td>\n      <td>0.7632</td>\n      <td>None</td>\n      <td>6</td>\n      <td>0.993025</td>\n    </tr>\n    <tr>\n      <th>866</th>\n      <td>None</td>\n      <td>None</td>\n      <td>2020-08-12T14:56:16+00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>None</td>\n      <td>Antwort an Nr. 839</td>\n      <td>Genau, meistens wird der Gehweg ausreichend Br...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>Genau meistens Gehweg ausreichen Breit Hochboa...</td>\n      <td>exactly time sidewalk become sufficiently wide...</td>\n      <td>0.023</td>\n      <td>0.868</td>\n      <td>0.109</td>\n      <td>0.6249</td>\n      <td>None</td>\n      <td>0</td>\n      <td>0.990892</td>\n    </tr>\n    <tr>\n      <th>442</th>\n      <th>919</th>\n      <td>None</td>\n      <td>None</td>\n      <td>2020-08-17T03:30:42+00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>None</td>\n      <td>Antwort an Nr. 913</td>\n      <td>Am Doraustieg ist keine Ampel vorhanden. Entwe...</td>\n      <td>...</td>\n      <td>False</td>\n      <td>Am Doraustieg Ampel vorhanden entweder Beitrag...</td>\n      <td>traffic light Doraustieg either post mean traf...</td>\n      <td>0.064</td>\n      <td>0.936</td>\n      <td>0.000</td>\n      <td>-0.2960</td>\n      <td>None</td>\n      <td>2</td>\n      <td>0.977182</td>\n    </tr>\n  </tbody>\n</table>\n<p>1078 rows × 29 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "contributions_topics_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the results\n",
    "contributions_topics_gdf.to_file(\"../data/02_experiments/topic_clusters.geojson\", driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Name         23\n",
       "Link         23\n",
       "Completed    23\n",
       "geometry     23\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "participation_processes.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Comment         3107\n",
       "Contribution    2074\n",
       "Reply           1387\n",
       "Name: Type, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "source": [
    "contributions_gdf[\"Type\"].astype(\"category\").value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "4702.0"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "contributions_gdf[\"Downvotes\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "contributions_gdf[\"Title_de_preprocessed\"] = contributions_gdf[\"Title_de\"].apply(lambda x: vc.Preprocesser.complete_preprocessing(\"de\", x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_probablities, topic_list, topic_model, dictionary, corpus = vc.TopicModeller.get_topics(contributions_gdf[\"Title_de_preprocessed\"],num_topics=25,iterations=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          Topic 0               Topic 1          Topic 2            Topic 3  \\\n",
       "0              Nr                Parken            Platz               Auto   \n",
       "1         Antwort                     S          Beitrag              wenig   \n",
       "2      Autorennen              Fahrzeug             Park            sperren   \n",
       "3         Bauhaus             notwendig             Mehr              Super   \n",
       "4   Linksabbiegen                  Ring        Fußgänger  Durchgangsverkehr   \n",
       "5      Wandsbeker                  Auto             Hier             Straße   \n",
       "6        Chaussee            Parkverbot           stehen      Infrastruktur   \n",
       "7          Straße             Elbgaustr        Radfahrer      Lohkampstraße   \n",
       "8         illegal                Kieler            Phase     Anwohnerparken   \n",
       "9         Verkehr                Straße          Fahrrad           Übergang   \n",
       "10      Fußgänger                   amp  Bushaltestellen           komplett   \n",
       "11        täglich                     E         Anregung              Sache   \n",
       "12         Alster  Güterverbindungsbahn             Spur        Fahrradspur   \n",
       "13    Fahrradspur                  grün             Kita             ordnen   \n",
       "14     Öjendorfer             Grünphase      Spadenteich         Gänsemarkt   \n",
       "15           mehr             Verhalten      Beleuchtung          Rücksicht   \n",
       "16         langen           Haltestelle      ermöglichen              gehen   \n",
       "17      Radfahrer            Velorouten    Linksabbieger       Falschparken   \n",
       "18            Wie               glauben             ÖPNV         Bergstraße   \n",
       "19          Ampel                  fast   Fußgängerampel                PKW   \n",
       "\n",
       "          Topic 4            Topic 5            Topic 6          Topic 7  \\\n",
       "0            mehr             Grüner           Kreuzung    Zebrastreifen   \n",
       "1            Raum               Kind         Innenstadt      Fahrradwege   \n",
       "2    Fahrradbügel      Fahrradfahrer    Fahrradstreifen             Baum   \n",
       "3      Autofahrer                Rad        Redingskamp         brauchen   \n",
       "4      öffentlich        Spielplätze                Tor             Mehr   \n",
       "5            Haus       Kreisverkehr           Berliner          Hamburg   \n",
       "6           wenig                Zur         Handwerker     Sillemstraße   \n",
       "7        brauchen               Grün                 St           kleine   \n",
       "8        Eilbeker         Stellplatz              Georg        vorhanden   \n",
       "9           Altes             Gehweg         Masterplan     Langenfelder   \n",
       "10           Grün          Bundesstr                Aus             Damm   \n",
       "11        illegal       Sillemstraße       Verbesserung        Fehlender   \n",
       "12            Weg          Mobilität         konsequent              Mit   \n",
       "13  Fahrradfahrer       Kirchenallee               groß          Bessere   \n",
       "14         Gehweg              klein         Mülltonnen          schwach   \n",
       "15          engen          verbinden  Serviceparkplätze  Geschwindigkeit   \n",
       "16           Bild         Verbindung      Pflegedienste    Lieferverkehr   \n",
       "17            bzw           Sperrung       Paketdienste         Einfahrt   \n",
       "18     Hundewiese  Anforderungsampel            Zufahrt        Veloroute   \n",
       "19      Eilbektal          umstellen            Zentrum          dringen   \n",
       "\n",
       "             Topic 8             Topic 9  ...          Topic 15  \\\n",
       "0           Chaussee               breit  ...         Fußgänger   \n",
       "1         Wandsbeker  Parkplatzsituation  ...      Umgestaltung   \n",
       "2            Radwege                Idee  ...         Radfahrer   \n",
       "3        Lindenallee           Situation  ...           sichern   \n",
       "4            Querung             überall  ...            fehlen   \n",
       "5               Ecke  Fahrradstellplätze  ...          Fehlende   \n",
       "6         Gestaltung              Garten  ...          Schulweg   \n",
       "7         Bramfelder                toll  ...           Querung   \n",
       "8       Grünstreifen         Düngelskamp  ...                vs   \n",
       "9            Saseler               Welle  ...         attraktiv   \n",
       "10       Pinneberger           Querungen  ...         errichten   \n",
       "11          Marktstr             Bahnhof  ...  Hoheluftchaussee   \n",
       "12  verkehrsberuhigt     berücksichtigen  ...            sicher   \n",
       "13            Alster               schon  ...         gestalten   \n",
       "14           Planung         Fahrradwege  ...          Fahrbahn   \n",
       "15           Bereich          Spielplatz  ...            Gefahr   \n",
       "16           Vorbild                Kind  ...  Mönckebergstraße   \n",
       "17        Holsteiner             Hamburg  ...         Anmerkung   \n",
       "18          wünschen               bitte  ...            modern   \n",
       "19            Straße                 Ich  ...              März   \n",
       "\n",
       "               Topic 16        Topic 17                Topic 18  \\\n",
       "0             Parkplatz          Radweg                  Erhalt   \n",
       "1                  Mehr         Endlich                     Süd   \n",
       "2                Fußweg          kommen           Einbahnstraße   \n",
       "3          Sillemstraße       Steindamm              Innenstadt   \n",
       "4           Magistralen         Fußwege                     Tod   \n",
       "5              Wandsbek      Hörgensweg                StadtRAD   \n",
       "6                 Markt      gefährlich      Anwohnerparkplätze   \n",
       "7                 wenig           Neuer            verschwinden   \n",
       "8   Aufenthaltsqualität  Lebensqualität  Einbahnstraßenregelung   \n",
       "9               Viertel      Hansaplatz            katastrophal   \n",
       "10                  all        Fusswege                 täglich   \n",
       "11              Verkehr          Schule                  Stelle   \n",
       "12              Bereich     Öffentliche                 beleben   \n",
       "13        Bundesstrasse          Eilbek          Radfahrerinnen   \n",
       "14          Lastenräder         baulich           Brennerstraße   \n",
       "15          Miteinander            Wall              Aufwertung   \n",
       "16        Treppenhäuser         trennen         Fußgängerampeln   \n",
       "17              Fussweg       Anbindung         Korallusviertel   \n",
       "18      Fahrradhäuschen   Fahrradstraße                     KFZ   \n",
       "19     Methfesselstraße       Fehlender                  Gehweg   \n",
       "\n",
       "             Topic 19        Topic 20     Topic 21            Topic 22  \\\n",
       "0              Gehweg           Ampel       Straße          Spielplatz   \n",
       "1                 gut            Zone        Tempo                 amp   \n",
       "2              schmal     Spielstraße    Veloroute  Verkehrsberuhigung   \n",
       "3            Autofrei   Einbahnstraße          Weg                 Fuß   \n",
       "4           Radfahrer      Grünanlage      entlang             Bessere   \n",
       "5               bitte        EScooter       Kieler           Kontrolle   \n",
       "6      Bushaltestelle       Vorschlag   Wördemanns           Sackgasse   \n",
       "7            ausbauen  Planungsgebiet      Olloweg        Falschparker   \n",
       "8               Reihe      Lindenpark         Zone              Radweg   \n",
       "9              Fußweg        Umgebung   Magistrale               Curbs   \n",
       "10        Mansteinstr          Grüner     verlegen                Sehr   \n",
       "11        Gastronomie    Elbgaustraße  Marktstraße                 Kfz   \n",
       "12              Lange      Öjendorfer     Wolliner         Parkflächen   \n",
       "13              Seite            Park      Strasse          Ballindamm   \n",
       "14            trennen       Wartezeit         Eine                Gute   \n",
       "15     Mittelstreifen           neben   Wandsbeker          Sicherheit   \n",
       "16         Fahrradweg             alt    vergessen             Kleiner   \n",
       "17            östlich     Wochenmarkt    Begrünung      Fahrradstrasse   \n",
       "18           Fahrbahn      Superblock       Wandse                Lärm   \n",
       "19  Schmilinskystraße             all      Verkehr             möglich   \n",
       "\n",
       "            Topic 23          Topic 24  \n",
       "0             langen   Mansteinstrasse  \n",
       "1              Reihe               Str  \n",
       "2            Fahrrad         entfernen  \n",
       "3          Durchgang        Eidelstedt  \n",
       "4      Einbahnstraße              groß  \n",
       "5          aufwerten    Fußgängerampel  \n",
       "6                neu            ändern  \n",
       "7      Fußgängerzone           Furtweg  \n",
       "8           Richtung  Fußgängerüberweg  \n",
       "9        Fußgängerin             Spiel  \n",
       "10     Neugestaltung              Kind  \n",
       "11              beid               bzw  \n",
       "12  Ampelschaltungen           Fitness  \n",
       "13               eng        abschaffen  \n",
       "14        Fahrradweg           Familie  \n",
       "15  Außengastronomie             UBahn  \n",
       "16         Volkspark        Ladenzeile  \n",
       "17      Entscheidung        aufstocken  \n",
       "18           fördern           StGeorg  \n",
       "19         freigeben        aufstellen  \n",
       "\n",
       "[20 rows x 25 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Topic 0</th>\n      <th>Topic 1</th>\n      <th>Topic 2</th>\n      <th>Topic 3</th>\n      <th>Topic 4</th>\n      <th>Topic 5</th>\n      <th>Topic 6</th>\n      <th>Topic 7</th>\n      <th>Topic 8</th>\n      <th>Topic 9</th>\n      <th>...</th>\n      <th>Topic 15</th>\n      <th>Topic 16</th>\n      <th>Topic 17</th>\n      <th>Topic 18</th>\n      <th>Topic 19</th>\n      <th>Topic 20</th>\n      <th>Topic 21</th>\n      <th>Topic 22</th>\n      <th>Topic 23</th>\n      <th>Topic 24</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Nr</td>\n      <td>Parken</td>\n      <td>Platz</td>\n      <td>Auto</td>\n      <td>mehr</td>\n      <td>Grüner</td>\n      <td>Kreuzung</td>\n      <td>Zebrastreifen</td>\n      <td>Chaussee</td>\n      <td>breit</td>\n      <td>...</td>\n      <td>Fußgänger</td>\n      <td>Parkplatz</td>\n      <td>Radweg</td>\n      <td>Erhalt</td>\n      <td>Gehweg</td>\n      <td>Ampel</td>\n      <td>Straße</td>\n      <td>Spielplatz</td>\n      <td>langen</td>\n      <td>Mansteinstrasse</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Antwort</td>\n      <td>S</td>\n      <td>Beitrag</td>\n      <td>wenig</td>\n      <td>Raum</td>\n      <td>Kind</td>\n      <td>Innenstadt</td>\n      <td>Fahrradwege</td>\n      <td>Wandsbeker</td>\n      <td>Parkplatzsituation</td>\n      <td>...</td>\n      <td>Umgestaltung</td>\n      <td>Mehr</td>\n      <td>Endlich</td>\n      <td>Süd</td>\n      <td>gut</td>\n      <td>Zone</td>\n      <td>Tempo</td>\n      <td>amp</td>\n      <td>Reihe</td>\n      <td>Str</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Autorennen</td>\n      <td>Fahrzeug</td>\n      <td>Park</td>\n      <td>sperren</td>\n      <td>Fahrradbügel</td>\n      <td>Fahrradfahrer</td>\n      <td>Fahrradstreifen</td>\n      <td>Baum</td>\n      <td>Radwege</td>\n      <td>Idee</td>\n      <td>...</td>\n      <td>Radfahrer</td>\n      <td>Fußweg</td>\n      <td>kommen</td>\n      <td>Einbahnstraße</td>\n      <td>schmal</td>\n      <td>Spielstraße</td>\n      <td>Veloroute</td>\n      <td>Verkehrsberuhigung</td>\n      <td>Fahrrad</td>\n      <td>entfernen</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bauhaus</td>\n      <td>notwendig</td>\n      <td>Mehr</td>\n      <td>Super</td>\n      <td>Autofahrer</td>\n      <td>Rad</td>\n      <td>Redingskamp</td>\n      <td>brauchen</td>\n      <td>Lindenallee</td>\n      <td>Situation</td>\n      <td>...</td>\n      <td>sichern</td>\n      <td>Sillemstraße</td>\n      <td>Steindamm</td>\n      <td>Innenstadt</td>\n      <td>Autofrei</td>\n      <td>Einbahnstraße</td>\n      <td>Weg</td>\n      <td>Fuß</td>\n      <td>Durchgang</td>\n      <td>Eidelstedt</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Linksabbiegen</td>\n      <td>Ring</td>\n      <td>Fußgänger</td>\n      <td>Durchgangsverkehr</td>\n      <td>öffentlich</td>\n      <td>Spielplätze</td>\n      <td>Tor</td>\n      <td>Mehr</td>\n      <td>Querung</td>\n      <td>überall</td>\n      <td>...</td>\n      <td>fehlen</td>\n      <td>Magistralen</td>\n      <td>Fußwege</td>\n      <td>Tod</td>\n      <td>Radfahrer</td>\n      <td>Grünanlage</td>\n      <td>entlang</td>\n      <td>Bessere</td>\n      <td>Einbahnstraße</td>\n      <td>groß</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Wandsbeker</td>\n      <td>Auto</td>\n      <td>Hier</td>\n      <td>Straße</td>\n      <td>Haus</td>\n      <td>Kreisverkehr</td>\n      <td>Berliner</td>\n      <td>Hamburg</td>\n      <td>Ecke</td>\n      <td>Fahrradstellplätze</td>\n      <td>...</td>\n      <td>Fehlende</td>\n      <td>Wandsbek</td>\n      <td>Hörgensweg</td>\n      <td>StadtRAD</td>\n      <td>bitte</td>\n      <td>EScooter</td>\n      <td>Kieler</td>\n      <td>Kontrolle</td>\n      <td>aufwerten</td>\n      <td>Fußgängerampel</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Chaussee</td>\n      <td>Parkverbot</td>\n      <td>stehen</td>\n      <td>Infrastruktur</td>\n      <td>wenig</td>\n      <td>Zur</td>\n      <td>Handwerker</td>\n      <td>Sillemstraße</td>\n      <td>Gestaltung</td>\n      <td>Garten</td>\n      <td>...</td>\n      <td>Schulweg</td>\n      <td>Markt</td>\n      <td>gefährlich</td>\n      <td>Anwohnerparkplätze</td>\n      <td>Bushaltestelle</td>\n      <td>Vorschlag</td>\n      <td>Wördemanns</td>\n      <td>Sackgasse</td>\n      <td>neu</td>\n      <td>ändern</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Straße</td>\n      <td>Elbgaustr</td>\n      <td>Radfahrer</td>\n      <td>Lohkampstraße</td>\n      <td>brauchen</td>\n      <td>Grün</td>\n      <td>St</td>\n      <td>kleine</td>\n      <td>Bramfelder</td>\n      <td>toll</td>\n      <td>...</td>\n      <td>Querung</td>\n      <td>wenig</td>\n      <td>Neuer</td>\n      <td>verschwinden</td>\n      <td>ausbauen</td>\n      <td>Planungsgebiet</td>\n      <td>Olloweg</td>\n      <td>Falschparker</td>\n      <td>Fußgängerzone</td>\n      <td>Furtweg</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>illegal</td>\n      <td>Kieler</td>\n      <td>Phase</td>\n      <td>Anwohnerparken</td>\n      <td>Eilbeker</td>\n      <td>Stellplatz</td>\n      <td>Georg</td>\n      <td>vorhanden</td>\n      <td>Grünstreifen</td>\n      <td>Düngelskamp</td>\n      <td>...</td>\n      <td>vs</td>\n      <td>Aufenthaltsqualität</td>\n      <td>Lebensqualität</td>\n      <td>Einbahnstraßenregelung</td>\n      <td>Reihe</td>\n      <td>Lindenpark</td>\n      <td>Zone</td>\n      <td>Radweg</td>\n      <td>Richtung</td>\n      <td>Fußgängerüberweg</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Verkehr</td>\n      <td>Straße</td>\n      <td>Fahrrad</td>\n      <td>Übergang</td>\n      <td>Altes</td>\n      <td>Gehweg</td>\n      <td>Masterplan</td>\n      <td>Langenfelder</td>\n      <td>Saseler</td>\n      <td>Welle</td>\n      <td>...</td>\n      <td>attraktiv</td>\n      <td>Viertel</td>\n      <td>Hansaplatz</td>\n      <td>katastrophal</td>\n      <td>Fußweg</td>\n      <td>Umgebung</td>\n      <td>Magistrale</td>\n      <td>Curbs</td>\n      <td>Fußgängerin</td>\n      <td>Spiel</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Fußgänger</td>\n      <td>amp</td>\n      <td>Bushaltestellen</td>\n      <td>komplett</td>\n      <td>Grün</td>\n      <td>Bundesstr</td>\n      <td>Aus</td>\n      <td>Damm</td>\n      <td>Pinneberger</td>\n      <td>Querungen</td>\n      <td>...</td>\n      <td>errichten</td>\n      <td>all</td>\n      <td>Fusswege</td>\n      <td>täglich</td>\n      <td>Mansteinstr</td>\n      <td>Grüner</td>\n      <td>verlegen</td>\n      <td>Sehr</td>\n      <td>Neugestaltung</td>\n      <td>Kind</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>täglich</td>\n      <td>E</td>\n      <td>Anregung</td>\n      <td>Sache</td>\n      <td>illegal</td>\n      <td>Sillemstraße</td>\n      <td>Verbesserung</td>\n      <td>Fehlender</td>\n      <td>Marktstr</td>\n      <td>Bahnhof</td>\n      <td>...</td>\n      <td>Hoheluftchaussee</td>\n      <td>Verkehr</td>\n      <td>Schule</td>\n      <td>Stelle</td>\n      <td>Gastronomie</td>\n      <td>Elbgaustraße</td>\n      <td>Marktstraße</td>\n      <td>Kfz</td>\n      <td>beid</td>\n      <td>bzw</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Alster</td>\n      <td>Güterverbindungsbahn</td>\n      <td>Spur</td>\n      <td>Fahrradspur</td>\n      <td>Weg</td>\n      <td>Mobilität</td>\n      <td>konsequent</td>\n      <td>Mit</td>\n      <td>verkehrsberuhigt</td>\n      <td>berücksichtigen</td>\n      <td>...</td>\n      <td>sicher</td>\n      <td>Bereich</td>\n      <td>Öffentliche</td>\n      <td>beleben</td>\n      <td>Lange</td>\n      <td>Öjendorfer</td>\n      <td>Wolliner</td>\n      <td>Parkflächen</td>\n      <td>Ampelschaltungen</td>\n      <td>Fitness</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Fahrradspur</td>\n      <td>grün</td>\n      <td>Kita</td>\n      <td>ordnen</td>\n      <td>Fahrradfahrer</td>\n      <td>Kirchenallee</td>\n      <td>groß</td>\n      <td>Bessere</td>\n      <td>Alster</td>\n      <td>schon</td>\n      <td>...</td>\n      <td>gestalten</td>\n      <td>Bundesstrasse</td>\n      <td>Eilbek</td>\n      <td>Radfahrerinnen</td>\n      <td>Seite</td>\n      <td>Park</td>\n      <td>Strasse</td>\n      <td>Ballindamm</td>\n      <td>eng</td>\n      <td>abschaffen</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Öjendorfer</td>\n      <td>Grünphase</td>\n      <td>Spadenteich</td>\n      <td>Gänsemarkt</td>\n      <td>Gehweg</td>\n      <td>klein</td>\n      <td>Mülltonnen</td>\n      <td>schwach</td>\n      <td>Planung</td>\n      <td>Fahrradwege</td>\n      <td>...</td>\n      <td>Fahrbahn</td>\n      <td>Lastenräder</td>\n      <td>baulich</td>\n      <td>Brennerstraße</td>\n      <td>trennen</td>\n      <td>Wartezeit</td>\n      <td>Eine</td>\n      <td>Gute</td>\n      <td>Fahrradweg</td>\n      <td>Familie</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>mehr</td>\n      <td>Verhalten</td>\n      <td>Beleuchtung</td>\n      <td>Rücksicht</td>\n      <td>engen</td>\n      <td>verbinden</td>\n      <td>Serviceparkplätze</td>\n      <td>Geschwindigkeit</td>\n      <td>Bereich</td>\n      <td>Spielplatz</td>\n      <td>...</td>\n      <td>Gefahr</td>\n      <td>Miteinander</td>\n      <td>Wall</td>\n      <td>Aufwertung</td>\n      <td>Mittelstreifen</td>\n      <td>neben</td>\n      <td>Wandsbeker</td>\n      <td>Sicherheit</td>\n      <td>Außengastronomie</td>\n      <td>UBahn</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>langen</td>\n      <td>Haltestelle</td>\n      <td>ermöglichen</td>\n      <td>gehen</td>\n      <td>Bild</td>\n      <td>Verbindung</td>\n      <td>Pflegedienste</td>\n      <td>Lieferverkehr</td>\n      <td>Vorbild</td>\n      <td>Kind</td>\n      <td>...</td>\n      <td>Mönckebergstraße</td>\n      <td>Treppenhäuser</td>\n      <td>trennen</td>\n      <td>Fußgängerampeln</td>\n      <td>Fahrradweg</td>\n      <td>alt</td>\n      <td>vergessen</td>\n      <td>Kleiner</td>\n      <td>Volkspark</td>\n      <td>Ladenzeile</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Radfahrer</td>\n      <td>Velorouten</td>\n      <td>Linksabbieger</td>\n      <td>Falschparken</td>\n      <td>bzw</td>\n      <td>Sperrung</td>\n      <td>Paketdienste</td>\n      <td>Einfahrt</td>\n      <td>Holsteiner</td>\n      <td>Hamburg</td>\n      <td>...</td>\n      <td>Anmerkung</td>\n      <td>Fussweg</td>\n      <td>Anbindung</td>\n      <td>Korallusviertel</td>\n      <td>östlich</td>\n      <td>Wochenmarkt</td>\n      <td>Begrünung</td>\n      <td>Fahrradstrasse</td>\n      <td>Entscheidung</td>\n      <td>aufstocken</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Wie</td>\n      <td>glauben</td>\n      <td>ÖPNV</td>\n      <td>Bergstraße</td>\n      <td>Hundewiese</td>\n      <td>Anforderungsampel</td>\n      <td>Zufahrt</td>\n      <td>Veloroute</td>\n      <td>wünschen</td>\n      <td>bitte</td>\n      <td>...</td>\n      <td>modern</td>\n      <td>Fahrradhäuschen</td>\n      <td>Fahrradstraße</td>\n      <td>KFZ</td>\n      <td>Fahrbahn</td>\n      <td>Superblock</td>\n      <td>Wandse</td>\n      <td>Lärm</td>\n      <td>fördern</td>\n      <td>StGeorg</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Ampel</td>\n      <td>fast</td>\n      <td>Fußgängerampel</td>\n      <td>PKW</td>\n      <td>Eilbektal</td>\n      <td>umstellen</td>\n      <td>Zentrum</td>\n      <td>dringen</td>\n      <td>Straße</td>\n      <td>Ich</td>\n      <td>...</td>\n      <td>März</td>\n      <td>Methfesselstraße</td>\n      <td>Fehlender</td>\n      <td>Gehweg</td>\n      <td>Schmilinskystraße</td>\n      <td>all</td>\n      <td>Verkehr</td>\n      <td>möglich</td>\n      <td>freigeben</td>\n      <td>aufstellen</td>\n    </tr>\n  </tbody>\n</table>\n<p>20 rows × 25 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "pd.DataFrame(topic_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}