diff --git a/README.md b/README.md
index 27d1b449d70440d68f2547a3e5004eaaadea26d6..f1bee04dfaad7e405257b2b484130d7f8ebf1044 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,21 @@
 # Newseye
 
-Newseye Jupyter Notebook: János Békési, Martin Gasteiner
\ No newline at end of file
+Newseye Jupyter Notebook: János Békési, Martin Gasteiner
+
+
+## Data
+
+22 MB transkribus json data (down to article level) resp. 12 MB csv data of the same
+
+
+## Workflow
+
+Sometimes the generating of topic models is rather tedious, since difficulties can arise regarding
+input data (datetime series with different starting or ending points, unforeseen gaps, etc.), or 
+the output formatting has to consider presentation quirks or sequence fittings. Though time aufgewendet 
+will mostly be more than estimated, any of those obstructions will be mastered (?) with a bit of
+patience and thoughtfulness.
+
+When preparing data for topic modelling, especially when using scanned data, it is crucious to allow for
+some document structure (pages, sections, documents).
+
diff --git a/workflow.ipynb b/workflow.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a2dbdf0f36a316e1975943afe5a9ec012b68514d
--- /dev/null
+++ b/workflow.ipynb
@@ -0,0 +1,1597 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Workflow of Processing Transcribus Data: From Scans to Topic Model Visualizations\n",
+    "\n",
+    "First, the necessary libraries and modules are imported and some variables are initiated. Note the use of \n",
+    "the convenience module `tm_utils.py` containing helper functions for operations needed frequently. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import csv\n",
+    "import json\n",
+    "from pprint import pprint\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import gensim\n",
+    "\n",
+    "sys.path = [\"/opt/scratch\"] + sys.path\n",
+    "\n",
+    "import tm_utils\n",
+    "DATA = Path(\"./data-telegraf\")\n",
+    "corpusname = \"telegraph\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Some data exploration\n",
+    "\n",
+    "First, we have a look at the `json` data coming out of `transkribus` by printing its length and some of\n",
+    "its content (this could also be done in `jupyterlab` by opening the `json` file)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4261\n",
+      "                                           id                  date  \\\n",
+      "0   neue_freie_presse_nfp18700723_article_363  1870-07-23T00:00:00Z   \n",
+      "1   neue_freie_presse_nfp18700723_article_531  1870-07-23T00:00:00Z   \n",
+      "2   neue_freie_presse_nfp18690706_article_398  1869-07-06T00:00:00Z   \n",
+      "3  neue_freie_presse_nfp18680324_article_1523  1868-03-24T00:00:00Z   \n",
+      "4   neue_freie_presse_nfp18700123_article_643  1870-01-23T00:00:00Z   \n",
+      "\n",
+      "        newspaper_id     type  \\\n",
+      "0  neue_freie_presse  article   \n",
+      "1  neue_freie_presse  article   \n",
+      "2  neue_freie_presse  article   \n",
+      "3  neue_freie_presse  article   \n",
+      "4  neue_freie_presse  article   \n",
+      "\n",
+      "                                                text  \n",
+      "0  [Local=Telegraph.] Am Franz=Josephs=Kai, Gonzaga¬  \n",
+      "1        (Per transatlantischen Telegraph.). („„“ g.  \n",
+      "2  Oest.-ung Zuckerf.-Act.-Gesellschaft\\nWolfsegg...  \n",
+      "3                                      Telegraph. A.  \n",
+      "4               Wiener Privat-Telegraph m. 30% Einz.  \n"
+     ]
+    }
+   ],
+   "source": [
+    "explor_file = DATA.joinpath(\"export_tg1_22_03_2021_11_42.json\")\n",
+    "with open(explor_file) as jsf:\n",
+    "    data = json.load(jsf)\n",
+    "print(len(data))\n",
+    "#for key in data[:10]:\n",
+    "#    print(key)\n",
+    "    \n",
+    "# look if there is much article scatter\n",
+    "\n",
+    "ids = [x['id'] for x in data]\n",
+    "# print(list(sorted(ids))[:20] )\n",
+    "\n",
+    "# yes, only short text pieces\n",
+    "# we need id, text, type, newspaper_id, date (last 3 are redundant, since encoded in id)\n",
+    "dfl = []\n",
+    "for r in data:\n",
+    "    dfl.append([r['id'],r['date'],r['newspaper_id'],r['type'],r['text']])\n",
+    "df = pd.DataFrame(dfl, columns=['id', 'date', 'newspaper_id', 'type', 'text'])\n",
+    "print(df.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Concatenating Files, Some Cleaning\n",
+    "\n",
+    "After proper inspection, the files are concatenated to one large `DataFrame`. Some cleaning is done: \"Daily Telegraph\" is replaced with \"DailyTelegraph\", as to not clutter topics. Then we save the DataFrame to a `csv` file. Saving is a smart precaution, because processing can be interrupted that way and continued at a later time. In our case, we have a `JupyterHub` instance running which shuts the Python kernel down after one hour of inactivity. Also, a boxplot graphics gives an impression of the character length distribution regarding median and quartiles (later on, we will repeat this for word numbers per document)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "438 characters median length of text column of 14949 rows\n",
+      "CPU times: user 854 ms, sys: 38.8 ms, total: 893 ms\n",
+      "Wall time: 1.35 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# one big file now\n",
+    "dfl = []\n",
+    "dailyt = [] #  look if `Daily Telegraph` occurences are meaningful nonetheless\n",
+    "corpusname = \"telegraph\"\n",
+    "outputfile = DATA.joinpath(f\"{corpusname}_01.csv\")\n",
+    "for fn in sorted(DATA.glob(\"export_tg*.json\")\n",
+    "    fns = fn.name.split(\"_\")[1]\n",
+    "    with open(fn) as jsf:\n",
+    "        aggreg = json.load(jsf)\n",
+    "        for r in aggreg:\n",
+    "            if r['text'].find('Daily Telegraph') > -1 and len(r['text']) < 500:\n",
+    "                # save short `Daily Telegraph` entries (false positives in topics)\n",
+    "                dailyt.append([r['id'], fns, r['date'],r['newspaper_id'],r['type'],r['text']])\n",
+    "                # remove them from corpus, when not commented:\n",
+    "                # continue\n",
+    "            dfl.append([r['id'], fns, r['date'],r['newspaper_id'],r['type'],r['text']])\n",
+    "df = pd.DataFrame(dfl, columns=['id', 'file', 'date', 'newspaper_id', 'type', 'text'])\n",
+    "dfdt = pd.DataFrame(dailyt, columns=['id', 'file', 'date', 'newspaper_id', 'type', 'text'])\n",
+    "df['text_len'] = df['text'].fillna(\"\").apply(lambda x: len(x))\n",
+    "df['text'] = df[\"text\"].fillna(\"\").apply(lambda x: x.replace(\"Daily Telegraph\", \"DailyTelegraph\"))\n",
+    "print(f\"{int(df.median())} characters median length of text column of {df.shape[0]} rows\")\n",
+    "# might not be too expressive, so switch to word count later on\n",
+    "bp = df.boxplot(column=[\"text_len\"])\n",
+    "# sorting for possible time series later\n",
+    "df.sort_values(by=[\"date\"], inplace=True)\n",
+    "df.to_csv(outputfile, sep=\";\")\n",
+    "# save to csv for perusal and checking if articles are worth including -- they are\n",
+    "dfdt.to_csv(DATA.joinpath(\"daily_t.csv\"), sep=';')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing and Stopwords\n",
+    "\n",
+    "We remove short word components (`simple_preprocess`) like punctuation, articles and so on, and remove stopwords, too (\"Telegraph\" is added to the stopword list, because it was the search criterion for generating the document corpus in the first place). Bigrams and trigrams are searched and added, and finally we lemmatize each document, i.e. remove flections and word variants by using `spacy` (URL), a tool for natural language processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# next stage: same as above, but now use (preprocessed) words\n",
+    "data_words = []\n",
+    "documents = df['text'].tolist()\n",
+    "for text in documents:\n",
+    "    data_words.append(list(\n",
+    "            gensim.utils.simple_preprocess(\n",
+    "                text, deacc=True, min_len=5)\n",
+    "        ))\n",
+    "add_stopword = [\"telegraph\"]\n",
+    "data_words = tm_utils.remove_stopwords(data_words, include=add_stopword)\n",
+    "data_words_bigrams, data_words_trigrams = tm_utils.make_bi_trigrams(data_words)\n",
+    "print(data_words_bigrams[0:2])\n",
+    "\n",
+    "data_lemmatized = tm_utils.lemmatization(data_words_bigrams)\n",
+    "save_lemmatized = tm_utils.save_lemmatized(data_lemmatized, corpusname=corpusname,\n",
+    "                                           datadir=DATA)\n",
+    "print(\"done\") # this is for cell `%time` timing -- takes about 90 secs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "corpusname: telegraph_\n"
+     ]
+    }
+   ],
+   "source": [
+    "# append lemmata to data file as a column for cross-checking\n",
+    "outputfile = DATA.joinpath(f\"{corpusname}_01.csv\")\n",
+    "data_lemmatized = tm_utils.get_lemmatized(corpusname=corpusname, datadir=DATA)\n",
+    "lemmata = [\" \".join(x) for x in data_lemmatized]\n",
+    "df = pd.read_csv(outputfile, sep=\";\")\n",
+    "\n",
+    "# done: \n",
+    "df['lemmata'] = lemmata\n",
+    "# df.to_csv(outputfile, sep=\";\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interlude: boxplot of lemmata numbers\n",
+    "inputfile = DATA.joinpath(f\"{corpusname}_01.csv\")\n",
+    "df = pd.read_csv(inputfile, sep=\";\")\n",
+    "df['lemmata_len'] = df['lemmata'].fillna(\"\").apply(lambda x: len(x.split(\" \")))\n",
+    "print(f\"{int(df.median()[-1])} words median length of lemmata column of {df.shape[0]} rows\")\n",
+    "print(df.median()[-1])\n",
+    "bp = df.boxplot(column=[\"lemmata_len\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bigram display\n",
+    "\n",
+    "We can display some of the bigrams already present in lemmatized documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "found 1452 bigrams\n",
+      "['service_bestehend', 'pension_carte', 'gesgesellschaft_oesterr', 'santen_bremen', 'wirkung_muskel', 'autoritativer_seite', 'mitleidenschaft_gezogen', 'bozen_dolomiten', 'binnen_kurzem', 'holzverkleidung_flugelthuren', 'privat_forstbank', 'speciell_empfohlen', 'million_pfund', 'grand_hotel', 'kuche_gedeckter', 'pensionats_genugt', 'romische_korrespondent', 'seehandlung_osterr', 'arbeiter_soldatenrates', 'rangsklasse_technischer']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# display bigrams\n",
+    "bigrams = set()\n",
+    "for row in data_lemmatized:\n",
+    "    [bigrams.add(x) for x in row if x.find('_') > -1]\n",
+    "print(\"found {} bigrams\".format(len(bigrams)))\n",
+    "print(list(bigrams)[:20])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "id2word, corpus = tm_utils.get_corpus_dictionary(data_lemmatized, \n",
+    "                        corpusname=corpusname, save=False, \n",
+    "                        datadir=DATA, from_file=True)\n",
+    "\n",
+    "# comparing gensim lda and mallet lda (by coherence value)\n",
+    "\n",
+    "mallet_path = '/usr/local/bin/mallet-2.0.8/bin/mallet'\n",
+    "num_topics = 20\n",
+    "models = []\n",
+    "start = 8\n",
+    "step = 4\n",
+    "stop = 29\n",
+    "for num_topics in range(start, stop, step):\n",
+    "    lda_model_g = gensim.models.LdaModel(corpus=corpus,\n",
+    "                                               id2word=id2word,\n",
+    "                                               num_topics=num_topics)\n",
+    "\n",
+    "    cm_g = gensim.models.coherencemodel.CoherenceModel(model=lda_model_g, corpus=corpus, \n",
+    "                                                       texts=data_lemmatized, coherence=\"c_v\")\n",
+    "    coh = cm_g.get_coherence()\n",
+    "    models.append((num_topics, coh, lda_model_g.show_topics(), cm_g, lda_model_g))    \n",
+    "    # now the mallet variant...\n",
+    "    # print(models[0][3].get_coherence())\n",
+    "\n",
+    "    if Path(mallet_path).is_file():\n",
+    "        lda_model_m = gensim.models.wrappers.LdaMallet(mallet_path, \n",
+    "                                corpus=corpus, num_topics=num_topics, id2word=id2word)\n",
+    "        # Show Topics\n",
+    "        # pprint(lda_model_m.show_topics(formatted=False))\n",
+    "\n",
+    "        cm_m = gensim.models.coherencemodel.CoherenceModel(model=lda_model_m, corpus=corpus, \n",
+    "                                                           texts=data_lemmatized, coherence=\"c_v\")\n",
+    "        coh = cm_m.get_coherence()\n",
+    "        models.append((num_topics, coh, lda_model_m.show_topics(), cm_m, lda_model_m))    \n",
+    "\n",
+    "for r in models:\n",
+    "    pprint(r[:2])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### comparison gensim / mallet\n",
+    "\n",
+    "perhaps we should do some more runs, but as for now gensim wins:\n",
+    "\n",
+    "    telegraph_20210323-162000 telegraph\n",
+    "    loading dict and corpus from data-telegraf/dict_telegraph_20210323-162000.dict, data-telegraf/corpus_telegraph_20210323-162000.mm\n",
+    "    (12, 0.4638558128553885)\n",
+    "    (12, 0.4714251587721215)\n",
+    "    (16, 0.4900238768530328)\n",
+    "    (16, 0.44632999316631283)\n",
+    "    (20, 0.5020486654206496)\n",
+    "    (20, 0.4540267313569305)\n",
+    "    (24, 0.510863799135756)\n",
+    "    (24, 0.4593669283001203)\n",
+    "    CPU times: user 52.7 s, sys: 2.87 s, total: 55.6 s\n",
+    "    Wall time: 3min 50s\n",
+    "    \n",
+    "(first topic_no gensim, second same one mallet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "telegraph_20210324-124039 telegraph\n",
+      "loading dict and corpus from data-telegraf/dict_telegraph_20210323-162000.dict, data-telegraf/corpus_telegraph_20210323-162000.mm\n",
+      "corpusname: telegraph_\n",
+      "computing coherence for the model starting with 16, interval 4 and stopping at 37\n",
+      "computing lda num topics of 16\n",
+      "computing lda num topics of 20\n",
+      "computing lda num topics of 24\n",
+      "computing lda num topics of 28\n",
+      "computing lda num topics of 32\n",
+      "computing lda num topics of 36\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "topics: 16 coherence:  0.47821523935980215\n",
+      "topics: 20 coherence:  0.47457657394812325\n",
+      "topics: 24 coherence:  0.462129238803163\n",
+      "topics: 28 coherence:  0.5309224697251984\n",
+      "topics: 32 coherence:  0.4822613246905857\n",
+      "topics: 36 coherence:  0.5001276113368437\n",
+      "=== 16\n",
+      "=== 20\n",
+      "=== 24\n",
+      "=== 28\n",
+      "=== 32\n",
+      "=== 36\n",
+      "CPU times: user 1min 2s, sys: 2.25 s, total: 1min 5s\n",
+      "Wall time: 1min 7s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# compute coherence...\n",
+    "\n",
+    "id2word, corpus = tm_utils.get_corpus_dictionary(data_lemmatized, \n",
+    "                        corpusname=corpusname, save=False, \n",
+    "                        datadir=DATA, from_file=True)\n",
+    "data_lemmatized = tm_utils.get_lemmatized(corpusname=corpusname, datadir=DATA)\n",
+    "\n",
+    "tm_utils.compute_coherence(corpus, id2word, data_lemmatized, start=16, stop=37,\n",
+    "                           corpusname=corpusname, datadir=DATA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training took 0:15:13.985832\n",
+      "CPU times: user 15min 5s, sys: 380 ms, total: 15min 5s\n",
+      "Wall time: 15min 13s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# switching per_word_topics to False\n",
+    "# decreasing minimum probability for time series compatibility (like mallet)\n",
+    "# but the min prob increases computing time 10 fold!\n",
+    "import datetime\n",
+    "now = datetime.datetime.now()\n",
+    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
+    "                                           id2word=id2word,\n",
+    "                                           num_topics=28, \n",
+    "                                           random_state=35009,\n",
+    "                                           update_every=1,\n",
+    "                                           chunksize=100,\n",
+    "                                           passes=30,\n",
+    "                                           alpha='auto',\n",
+    "                                           # per_word_topics=True,\n",
+    "                                           per_word_topics=False,\n",
+    "                                           minimum_probability=0.001,\n",
+    "                                           )\n",
+    "lda_model.save(str(DATA.joinpath(\"{}_lda_top28.model\".format(corpusname))))\n",
+    "print(\"training took {}\".format(datetime.datetime.now() - now))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lda_model.save(str(DATA.joinpath(\"{}_lda_top28.model\".format(corpusname))))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
+       "\n",
+       "\n",
+       "<div id=\"ldavis_el111431403343771649926869268628\"></div>\n",
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\"regelma\\u00dfig\", \"regeln\", \"regelung\", \"regierung\", \"regierungen\", \"reich\", \"reichen\", \"reihe\", \"reise\", \"religion\", \"revolution\", \"richtung\", \"romische\", \"romischen\", \"rucken\", \"ruckkehr\", \"russisch\", \"russische\", \"schaffen\", \"scharf\", \"schicksal\", \"schiff\", \"schildern\", \"schlagen\", \"schlie\\u00dflich\", \"schnell\", \"schreiben\", \"schutz\", \"schwarz\", \"schwierig\", \"sehen\", \"seinerseits\", \"seiten\", \"seltsam\", \"senden\", \"sendung\", \"setzen\", \"sicherheit\", \"sicherlich\", \"sinken\", \"sitzen\", \"sitzung\", \"sofortig\", \"sogleich\", \"soldaten\", \"spanisch\", \"spanische\", \"sprache\", \"sprechen\", \"staat\", \"staate\", \"staaten\", \"stadte\", \"starke\", \"statten\", \"stattgefunden\", \"stehen\", \"steigen\", \"stellen\", \"sterben\", \"stimme\", \"stimmung\", \"stra\\u00dfe\", \"stra\\u00dfen\", \"strecken\", \"sturz\", \"sturzen\", \"suden\", \"tadeln\", \"taglich\", \"tapfer\", \"telegramm\", \"telegraphen\", \"tendenz\", \"tiefe\", \"treffen\", \"truppen\", \"uberall\", \"ubergeben\", \"uberhaupt\", \"uberlassen\", \"ubermittelt\", \"uberschritten\", \"ubertragen\", \"ublichen\", \"ueberlegenheit\", \"umfassend\", \"umstand\", \"unabhangigkeit\", \"unbedingt\", \"unfall\", \"ungefahr\", \"ungewohnlich\", \"union\", \"unklar\", \"unmittelbar\", \"unmoglichkeit\", \"unrichtig\", \"unruhen\", \"unten\", \"unterdessen\", \"unterdruckung\", \"unternehmen\", \"unternehmungen\", \"unterredung\", \"unterricht\", \"unterrichten\", \"unterstutzung\", \"unterwerfung\", \"ununterbrochen\", \"verabreden\", \"verbessern\", \"verbinden\", \"verbindung\", \"verbreitung\", \"verbundeten\", \"verfasser\", \"verfassung\", \"verfehlen\", \"verfolgung\", \"verfugen\", \"verfugung\", \"verhaften\", \"verhalten\", \"verhaltnisse\", \"verhandlung\", \"verhandlungen\", \"verhindern\", \"verkauf\", \"verkehr\", \"verkehrs\", \"verlauten\", \"verletzen\", \"verlieren\", \"vermehren\", \"vermeiden\", \"vermindern\", \"vermitteln\", \"vermittlung\", \"veroffentlichen\", \"veroffentlichte\", \"verpflichten\", \"versammeln\", \"verschworung\", \"versicherung\", \"versprechen\", \"verstandigen\", \"verstandigt\", \"versuchen\", \"vertrag\", \"vertreten\", \"vertreter\", \"vertretung\", \"verweilen\", \"verwirklichen\", \"verwirklichung\", \"verwirrung\", \"verzweifeln\", \"vielmehr\", \"volkes\", \"voll\", \"vollenden\", \"vollendung\", \"vollig\", \"vollmachen\", \"voraus\", \"vorerst\", \"vorgange\", \"vorgeschlagen\", \"vorgestern\", \"vorher\", \"vorig\", \"vorlage\", \"vorlaufig\", \"vorlegen\", \"vornehmen\", \"vorschlag\", \"vorschlagen\", \"waffen\", \"wagen\", \"wahlen\", \"wahren\", \"wahrscheinlich\", \"warten\", \"wasser\", \"weilen\", \"weise\", \"weisen\", \"weit\", \"widerspruch\", \"widerspruche\", \"widerstehen\", \"widmen\", \"wiedergeben\", \"wiederholen\", \"wiener\", \"willigen\", \"wirken\", \"wirklich\", \"wirksam\", \"wirkung\", \"witterung\", \"wonach\", \"wortlich\", \"wundern\", \"zahllos\", \"zahlreich\", \"zahlreiche\", \"zeichen\", \"zeigen\", \"zeitung\", \"zerstort\", \"zerstreuen\", \"ziehen\", \"zugeben\", \"zugehen\", \"zugestandnisse\", \"zumal\", \"zunachst\", \"zuruck\", \"zuruckkehren\", \"zuruckzufuhren\", \"zuruckzuziehen\", \"zusammenhang\", \"zusammenkunft\", \"zusammensetzung\", \"zusammentreffen\", \"zustande\", \"zustehen\", \"zustimmung\", \"zuversicht\", \"zuweilen\", \"zweifeln\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [15, 26, 21, 9, 11, 10, 29, 17, 27, 16, 1, 2, 13, 22, 19, 7, 12, 23, 14, 8, 6, 24, 5, 3, 4, 18, 20, 28, 25]};\n",
+       "\n",
+       "function LDAvis_load_lib(url, callback){\n",
+       "  var s = document.createElement('script');\n",
+       "  s.src = url;\n",
+       "  s.async = true;\n",
+       "  s.onreadystatechange = s.onload = callback;\n",
+       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "}\n",
+       "\n",
+       "if(typeof(LDAvis) !== \"undefined\"){\n",
+       "   // already loaded: just create the visualization\n",
+       "   !function(LDAvis){\n",
+       "       new LDAvis(\"#\" + \"ldavis_el111431403343771649926869268628\", ldavis_el111431403343771649926869268628_data);\n",
+       "   }(LDAvis);\n",
+       "}else if(typeof define === \"function\" && define.amd){\n",
+       "   // require.js is available: use it to load d3/LDAvis\n",
+       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
+       "   require([\"d3\"], function(d3){\n",
+       "      window.d3 = d3;\n",
+       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "        new LDAvis(\"#\" + \"ldavis_el111431403343771649926869268628\", ldavis_el111431403343771649926869268628_data);\n",
+       "      });\n",
+       "    });\n",
+       "}else{\n",
+       "    // require.js not available: dynamically load d3 & LDAvis\n",
+       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
+       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
+       "                 new LDAvis(\"#\" + \"ldavis_el111431403343771649926869268628\", ldavis_el111431403343771649926869268628_data);\n",
+       "            })\n",
+       "         });\n",
+       "}\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plotting tools\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
+    "\n",
+    "import pyLDAvis\n",
+    "import pyLDAvis.gensim  # don't skip this\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "mds = ('mmds', 'pcoa', 'tsne')\n",
+    "visu = pyLDAvis.gensim.prepare(lda_model, corpus, id2word, mds='mmds')\n",
+    "savejson = str(DATA.joinpath(\"saved_lda_{}_{}.json\".format(corpusname, NOWSTR)))\n",
+    "pyLDAvis.save_json(visu, savejson)\n",
+    "pyLDAvis.save_html(visu, str(DATA.joinpath(\"saved_lda_{}_{}.html\".format(corpusname, NOWSTR))),\n",
+    "                  visid=f\"id_{corpusname}\")\n",
+    "pyLDAvis.display(visu)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+      "['topic_0', 'topic_1', 'topic_2', 'topic_3', 'topic_4', 'topic_5', 'topic_6', 'topic_7', 'topic_8', 'topic_9', 'topic_10', 'topic_11', 'topic_12', 'topic_13', 'topic_14', 'topic_15', 'topic_16', 'topic_17', 'topic_18', 'topic_19', 'topic_20', 'topic_21', 'topic_22', 'topic_23', 'topic_24', 'topic_25', 'topic_26', 'topic_27']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now for the timeseries...\n",
+    "topics = {}\n",
+    "topic_no = 28\n",
+    "topic_headers = []\n",
+    "trow_templ = [0 for x in range(topic_no)]\n",
+    "print(trow_templ)\n",
+    "# collect topic words\n",
+    "for t in lda_model.show_topics(num_topics=topic_no, num_words=10, log=False, formatted=False):\n",
+    "    tnum = t[0]\n",
+    "    tl = t[1]\n",
+    "    topics[tnum] = \", \".join([word for word, prop in tl])\n",
+    "    topic_headers.append(f\"topic_{tnum}\")\n",
+    "print(topic_headers)\n",
+    "outputfile = DATA.joinpath(f\"{corpusname}_01.csv\")\n",
+    "raw_df = pd.read_csv(outputfile, sep=\";\")\n",
+    "#create an array for use in time series\n",
+    "data = []\n",
+    "for i, row in enumerate(lda_model[corpus]):\n",
+    "    # the format of each row representing a document is:\n",
+    "    # 1) list of topic numbers with percentage of contributing to document\n",
+    "    # output: index year-month topic0 topic2 topic3... topic11\n",
+    "    values = trow_templ[:]\n",
+    "    #if i > 4:\n",
+    "    #    break\n",
+    "    date = raw_df.loc[i]['date']\n",
+    "    topic_values = {}\n",
+    "    for r in row:\n",
+    "        topic_idx, val = r\n",
+    "        values[topic_idx] = val\n",
+    "\n",
+    "    data.append([date] + values)\n",
+    "    #print(\"\\n\")\n",
+    "df = pd.DataFrame(data, columns=[\"date\"] + topic_headers)\n",
+    "timeseries = f\"{corpusname}_time.csv\"\n",
+    "df.to_csv(DATA.joinpath(timeseries), sep=\";\", index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timeseries = f\"{corpusname}_time.csv\"\n",
+    "\n",
+    "data = pd.read_csv(DATA.joinpath(timeseries), sep=\";\")\n",
+    "data = data.sort_values(by=[\"date\"], ignore_index=True) #  sorting \n",
+    "data = data.reset_index(drop=True)\n",
+    "print(data.head())\n",
+    "imgdir = Path(\"images\")\n",
+    "x = range(0, data.shape[0])\n",
+    "#data['year'].fillna(0).apply(lambda x: \n",
+    "#                            pd.to_datetime(df['birth_date'](x))\n",
+    "# data['publ'] = data['publ'].str.cat(data[['month']], sep='-', na_rep=\"\")\n",
+    "x = list(data['date'])\n",
+    "# y = data\n",
+    "chunksize = 4\n",
+    "print(len(topics), \"Topics\")\n",
+    "variant = \"\"\n",
+    "offset = 1\n",
+    "plt.figure(figsize=[20,20], dpi=200)\n",
+    "for j in range(0, int(len(topics)/chunksize)):\n",
+    "    plt.subplot(511+j)\n",
+    "    for i in range(0, chunksize):\n",
+    "        col = i + chunksize * j\n",
+    "        col_topic_idx = col + offset\n",
+    "        maxval = max(data.iloc[:, col_topic_idx])\n",
+    "        # print(\"col:\", col_topic_idx, \"topic: \", col, \"maxval:\", maxval)\n",
+    "        if maxval:\n",
+    "            xval = data.loc[data[f'topic_{col}'] == maxval].index.item()\n",
+    "        else:\n",
+    "            xval = None\n",
+    "        print(\"xval: {} for topic {} (max: {})\".format(xval, col, maxval))\n",
+    "        descr = \", \".join(topics[col].split(\", \")[:4])\n",
+    "        src = \"\"\n",
+    "        label=\"Topic {}: {}... {}\".format(col+1, descr, src)\n",
+    "        if xval:\n",
+    "            plt.annotate(\"x_{}\".format(col + 1), (int(xval), maxval)) #  annotate max value for topic    \n",
+    "        plt.plot(x, data.iloc[:, col_topic_idx], label=label)\n",
+    "        plt.legend()\n",
+    "    plt.title('Newseye Topic Modelling (Newspaper: \"Telegraph\")', fontsize=22)\n",
+    "    plt.xlabel(\"Telegraph Newspaper ({})\".format(data.shape[0]))\n",
+    "    plt.ylabel(\"Topic percentage / 100\")\n",
+    "    # plt.xlim(-18, 700)\n",
+    "plt.autoscale(enable=True, axis=\"x\")\n",
+    "plt.savefig(str(imgdir.joinpath(\"telegraph_topics.png\")) )\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "corpusname: telegraph_\n",
+      "telegraph_20210330-150450 telegraph\n",
+      "loading dict and corpus from data-telegraf/dict_telegraph_20210323-162000.dict, data-telegraf/corpus_telegraph_20210323-162000.mm\n",
+      "LdaModel(num_terms=61923, num_topics=28, decay=0.5, chunksize=100)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# retrieve ldamodel\n",
+    "data_lemmatized = tm_utils.get_lemmatized(corpusname=corpusname, datadir=DATA)\n",
+    "id2word, corpus = tm_utils.get_corpus_dictionary(data_lemmatized, \n",
+    "                        corpusname=corpusname, save=False, \n",
+    "                        datadir=DATA, from_file=True)\n",
+    "modelfile = str(DATA.joinpath(\"{}_lda_top28.model\".format(corpusname)))\n",
+    "lda_model = gensim.models.ldamodel.LdaModel.load(modelfile)\n",
+    "print(lda_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+      "['topic_0', 'topic_1', 'topic_2', 'topic_3', 'topic_4', 'topic_5', 'topic_6', 'topic_7', 'topic_8', 'topic_9', 'topic_10', 'topic_11', 'topic_12', 'topic_13', 'topic_14', 'topic_15', 'topic_16', 'topic_17', 'topic_18', 'topic_19', 'topic_20', 'topic_21', 'topic_22', 'topic_23', 'topic_24', 'topic_25', 'topic_26', 'topic_27']\n"
+     ]
+    }
+   ],
+   "source": [
+    "## now trying to single out newspapers for meaningful timeline\n",
+    "# now for the timeseries...\n",
+    "topics = {}\n",
+    "topic_no = 28\n",
+    "topic_headers = []\n",
+    "trow_templ = [0 for x in range(topic_no)]\n",
+    "print(trow_templ)\n",
+    "# collect topic words\n",
+    "for t in lda_model.show_topics(num_topics=topic_no, num_words=10, log=False, formatted=False):\n",
+    "    tnum = t[0]\n",
+    "    tl = t[1]\n",
+    "    topics[tnum] = \", \".join([word for word, prop in tl])\n",
+    "    topic_headers.append(f\"topic_{tnum}\")\n",
+    "print(topic_headers)\n",
+    "outputfile = DATA.joinpath(f\"{corpusname}_01.csv\")\n",
+    "raw_df = pd.read_csv(outputfile, sep=\";\")\n",
+    "\n",
+    "#create an array for use in time series\n",
+    "data = []\n",
+    "for i, row in enumerate(lda_model[corpus]):\n",
+    "    # the format of each row representing a document is:\n",
+    "    # 1) list of topic numbers with percentage of contributing to document\n",
+    "    # output: index year-month topic0 topic2 topic3... topic11\n",
+    "    values = trow_templ[:]\n",
+    "    #if i > 4:\n",
+    "    #    break\n",
+    "    date = raw_df.loc[i]['date']\n",
+    "    topic_values = {}\n",
+    "    for r in row:\n",
+    "        topic_idx, val = r\n",
+    "        values[topic_idx] = val\n",
+    "\n",
+    "    data.append([date] + values)\n",
+    "    #print(\"\\n\")\n",
+    "df = pd.DataFrame(data, columns=[\"date\"] + topic_headers)\n",
+    "timeseries = f\"{corpusname}_time.csv\"\n",
+    "#df.to_csv(DATA.joinpath(timeseries), sep=\";\", index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['innsbrucker_nachrichten' 'neue_freie_presse' 'arbeiter_zeitung'\n",
+      " 'illustrierte_kronen_zeitung']\n",
+      "innsbrucker_nachrichten\n",
+      "data for in : 2212\n",
+      "neue_freie_presse\n",
+      "data for nfp : 10981\n",
+      "arbeiter_zeitung\n",
+      "data for az : 1259\n",
+      "illustrierte_kronen_zeitung\n",
+      "data for ikz : 497\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now timeseries for one newspaper only \n",
+    "print(raw_df['newspaper_id'].unique())\n",
+    "\n",
+    "# refine raw_df (old columns from earlier oversight)\n",
+    "try:\n",
+    "    raw_df.rename(columns = {'Unnamed: 0.1':'index_old'}, inplace = True)\n",
+    "    raw_df.drop(columns=['Unnamed: 0'], inplace=True)\n",
+    "except:\n",
+    "    pass\n",
+    "\n",
+    "for np in raw_df['newspaper_id'].unique():\n",
+    "    print(np)\n",
+    "    npdf = raw_df[raw_df['newspaper_id'] == np]\n",
+    "    short_id = \"\".join([x[0] for x in np.split(\"_\")])\n",
+    "    # npdf.to_csv(DATA.joinpath(f\"time_{corpusname}_{short_id}.csv\"), sep=\";\", index=False)\n",
+    "    data = []\n",
+    "    #if short_id == \"nfp\":\n",
+    "    #    continue\n",
+    "    for row in npdf.itertuples():\n",
+    "        old_id, date = row[1], row[4]\n",
+    "        modeldata = lda_model[corpus][old_id]\n",
+    "        for r in modeldata:\n",
+    "            topic_idx, val = r\n",
+    "            values[topic_idx] = val\n",
+    "        data.append([date] + values)\n",
+    "    print(\"data for\", short_id, \":\", len(data))\n",
+    "    df = pd.DataFrame(data, columns=[\"date\"] + topic_headers)\n",
+    "    timeseries = f\"ts_topic_{short_id}_{corpusname}.csv\"\n",
+    "    df.to_csv(DATA.joinpath(timeseries), sep=\";\", index=False)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "presentation = True #  for presentation only show ikz as png in notebook\n",
+    "imgdir = Path(\"images\")\n",
+    "chunksize = 7\n",
+    "print(len(topics), \"Topics\")\n",
+    "npnames = ['innsbrucker_nachrichten', 'neue_freie_presse', 'arbeiter_zeitung',\n",
+    " 'illustrierte_kronen_zeitung']\n",
+    "intervals = [\n",
+    "    ('1810-01-01', '1880-01-01'),\n",
+    "    ('1880-01-01', '1910-01-01'),\n",
+    "    ('1910-01-01', '1925-01-01'),\n",
+    "    ('1925-01-01','2010-01-01')\n",
+    "]\n",
+    "exception_map = {0:  {'ikz': ('1880-01-01', '1925-01-01'),\n",
+    "                     'az': ('1880-01-01', '1925-01-01')},\n",
+    "                1: {'ikz': ('1880-01-01', '1925-01-01'),\n",
+    "                   'az': ('1880-01-01', '1925-01-01')}, \n",
+    "                2: {'ikz': ('1880-01-01', '1925-01-01') } }\n",
+    "for idx, n in enumerate(npnames):\n",
+    "    short_id = \"\".join([x[0] for x in n.split(\"_\")])\n",
+    "    print(\"processing\", n)\n",
+    "    data_raw = pd.read_csv(DATA.joinpath(f\"ts_topic_{short_id}_{corpusname}.csv\"), sep=\";\")\n",
+    "    #data = data['']\n",
+    "    for iidx, intv in enumerate(intervals):\n",
+    "        ifrom = intv[0]\n",
+    "        ito = intv[1]\n",
+    "        if iidx in exception_map.keys():\n",
+    "            if short_id in exception_map[iidx]:\n",
+    "                ifrom = exception_map[iidx][short_id][0]\n",
+    "                ito = exception_map[iidx][short_id][1]\n",
+    "        data = data_raw[data_raw['date'] > ifrom]\n",
+    "        data = data[data['date'] < ito]\n",
+    "        # x = range(0, data.shape[0])\n",
+    "        #data['year'].fillna(0).apply(lambda x: \n",
+    "        #                            pd.to_datetime(df['birth_date'](x))\n",
+    "        # data['publ'] = data['publ'].str.cat(data[['month']], sep='-', na_rep=\"\")\n",
+    "        data['mydate'] = pd.to_datetime(data['date'])\n",
+    "        x = data['mydate']\n",
+    "        # y = data\n",
+    "        variant = \"\"\n",
+    "        offset = 1\n",
+    "        # print(x)\n",
+    "        plt.figure(figsize=[20,40], dpi=200)\n",
+    "        for j in range(0, int(len(topics)/chunksize)):\n",
+    "            plt.subplot(511+j)\n",
+    "            for i in range(0, chunksize):\n",
+    "                col = i + chunksize * j\n",
+    "                col_topic_idx = col + offset\n",
+    "                values = data.iloc[:, col_topic_idx]\n",
+    "                if values.empty:\n",
+    "                    continue\n",
+    "                maxval = max(values)\n",
+    "                # print(\"col:\", col_topic_idx, \"topic: \", col, \"maxval:\", maxval)\n",
+    "                if maxval:\n",
+    "                    try:\n",
+    "                        xval = data.loc[data[f'topic_{col}'] == maxval].index.item()\n",
+    "                    except:\n",
+    "                        xval = None\n",
+    "                else:\n",
+    "                    xval = None\n",
+    "                # print(\"xval: {} for topic {} (max: {})\".format(xval, col, maxval))\n",
+    "                descr = \", \".join(topics[col].split(\", \")[:4])\n",
+    "                src = \"\"\n",
+    "                label=\"Topic {}: {}... {}\".format(col+1, descr, src)\n",
+    "                if xval:\n",
+    "                    plt.annotate(\"x_{}\".format(col + 1), (int(xval), maxval)) #  annotate max value for topic    \n",
+    "                plt.plot(x, data.iloc[:, col_topic_idx], label=label)\n",
+    "                plt.legend()\n",
+    "            plt.title(f'Newseye Topic Modelling {n}: \"Telegraph\")', fontsize=12)\n",
+    "            plt.xlabel(\"Telegraph Newspaper {} ({})\".format(n, data.shape[0]))\n",
+    "            plt.ylabel(\"Topic percentage / 100\")\n",
+    "            # plt.xlim(-18, 700)\n",
+    "        plt.autoscale(enable=True, axis=\"x\")\n",
+    "        plt.savefig(str(imgdir.joinpath(f\"telegraph_topics_{short_id}_{iidx}.png\")) )\n",
+    "        if presentation:\n",
+    "            print(\"Displaying ikz time series only; charts are saved to ./images\")\n",
+    "            if short_id == 'ikz':\n",
+    "                plt.show()\n",
+    "        else:\n",
+    "            plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n",
+      "CPU times: user 2.12 s, sys: 236 ms, total: 2.36 s\n",
+      "Wall time: 46.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# since only 2 topics raise above the 0.1 threshold (12, 18), we try with mallet (methodologically unsound, but for display's sake)\n",
+    "\n",
+    "mallet_path = '/usr/local/bin/mallet-2.0.8/bin/mallet'\n",
+    "\n",
+    "ldam_model = gensim.models.wrappers.LdaMallet(mallet_path, \n",
+    "                                corpus=corpus, num_topics=28, id2word=id2word)\n",
+    "\n",
+    "\n",
+    "ldam_model.save(str(DATA.joinpath(\"{}_ldamallet_top28.model\".format(corpusname))))\n",
+    "\n",
+    "# convert to gensim lda (from tm_utils)\n",
+    "model_gensim = gensim.models.LdaModel(\n",
+    "    id2word=ldam_model.id2word, num_topics=ldam_model.num_topics,\n",
+    "    alpha=ldam_model.alpha, eta=0,\n",
+    ")\n",
+    "model_gensim.state.sstats[...] = ldam_model.wordtopics\n",
+    "model_gensim.sync_state()\n",
+    "print(\"done\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Topic Distribution in Documents\n",
+    "\n",
+    "* Most dominant topics\n",
+    "* Most representative topics\n",
+    "* Distribution of topics\n",
+    "\n",
+    "We only show an abridged version of the dominant topic list; our `datasette` instance would render all data, so that the direct link to actual source data would be functional.\n",
+    "\n",
+    "### Most Dominant Topic per Document\n",
+    "\n",
+    "At first we display the most dominant topic for each document:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "corpusname: telegraph_\n",
+      "telegraph_20210331-094906 telegraph\n",
+      "loading dict and corpus from data-telegraf/dict_telegraph_20210323-162000.dict, data-telegraf/corpus_telegraph_20210323-162000.mm\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Document_No</th>\n",
+       "      <th>Dominant_Topic</th>\n",
+       "      <th>Topic_Perc_Contrib</th>\n",
+       "      <th>Keywords</th>\n",
+       "      <th>Text</th>\n",
+       "      <th>Link</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.1938</td>\n",
+       "      <td>27: telegraphen, verbindung, linie, kabel, ver...</td>\n",
+       "      <td>Ueber die mangelhafte telegraphische Verbindun...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.1213</td>\n",
+       "      <td>18: oesterreich, preußisch, seite, politisch, ...</td>\n",
+       "      <td>Neuestes.\\nFrankfurt a. M., 25. Febr. Bundesta...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0536</td>\n",
+       "      <td>17: konig, bringen, bleiben, landes, halten, r...</td>\n",
+       "      <td>Wie „Dayly Telegraph“ wissen will, hat der Kro...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.0916</td>\n",
+       "      <td>8: prozent, borse, stark, zeigen, hoch, haltun...</td>\n",
+       "      <td>Dem Grazer „Telegraph“ wird aus Leibnitz gesch...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.1299</td>\n",
+       "      <td>24: stadt, gestern, folgen, abends, berichten,...</td>\n",
+       "      <td>Die „Ztg. f. Nordd.“ läßt sich von einem Augen...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.1381</td>\n",
+       "      <td>22: fragen, politik, krieg, konne, frieden, be...</td>\n",
+       "      <td>London, 19. Juni. Das Gerücht ist verbreitet, ...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0534</td>\n",
+       "      <td>24: stadt, gestern, folgen, abends, berichten,...</td>\n",
+       "      <td>Palermo, 27. Aug. Wie der Telegraph meldet, ha...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.1376</td>\n",
+       "      <td>3: millionen, telephon, betragen, einnahmen, f...</td>\n",
+       "      <td>Allerdings löst auch die Naturwissenschaft jen...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.1071</td>\n",
+       "      <td>23: kaiser, franzosischen, pariser, franzosisc...</td>\n",
+       "      <td>Noch spricht man von dem Project des Prinzen v...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.1677</td>\n",
+       "      <td>19: minister, stimmen, ministerium, partei, si...</td>\n",
+       "      <td>Der Geralrath von Ille=et=Vilaine hatte sich, ...</td>\n",
+       "      <td>https://d.org/ne_datasette/ne_dominant_topics/9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Document_No Dominant_Topic  Topic_Perc_Contrib  \\\n",
+       "0            0             27              0.1938   \n",
+       "1            1             18              0.1213   \n",
+       "2            2             17              0.0536   \n",
+       "3            3              8              0.0916   \n",
+       "4            4             24              0.1299   \n",
+       "5            5             22              0.1381   \n",
+       "6            6             24              0.0534   \n",
+       "7            7              3              0.1376   \n",
+       "8            8             23              0.1071   \n",
+       "9            9             19              0.1677   \n",
+       "\n",
+       "                                            Keywords  \\\n",
+       "0  27: telegraphen, verbindung, linie, kabel, ver...   \n",
+       "1  18: oesterreich, preußisch, seite, politisch, ...   \n",
+       "2  17: konig, bringen, bleiben, landes, halten, r...   \n",
+       "3  8: prozent, borse, stark, zeigen, hoch, haltun...   \n",
+       "4  24: stadt, gestern, folgen, abends, berichten,...   \n",
+       "5  22: fragen, politik, krieg, konne, frieden, be...   \n",
+       "6  24: stadt, gestern, folgen, abends, berichten,...   \n",
+       "7  3: millionen, telephon, betragen, einnahmen, f...   \n",
+       "8  23: kaiser, franzosischen, pariser, franzosisc...   \n",
+       "9  19: minister, stimmen, ministerium, partei, si...   \n",
+       "\n",
+       "                                                Text  \\\n",
+       "0  Ueber die mangelhafte telegraphische Verbindun...   \n",
+       "1  Neuestes.\\nFrankfurt a. M., 25. Febr. Bundesta...   \n",
+       "2  Wie „Dayly Telegraph“ wissen will, hat der Kro...   \n",
+       "3  Dem Grazer „Telegraph“ wird aus Leibnitz gesch...   \n",
+       "4  Die „Ztg. f. Nordd.“ läßt sich von einem Augen...   \n",
+       "5  London, 19. Juni. Das Gerücht ist verbreitet, ...   \n",
+       "6  Palermo, 27. Aug. Wie der Telegraph meldet, ha...   \n",
+       "7  Allerdings löst auch die Naturwissenschaft jen...   \n",
+       "8  Noch spricht man von dem Project des Prinzen v...   \n",
+       "9  Der Geralrath von Ille=et=Vilaine hatte sich, ...   \n",
+       "\n",
+       "                                              Link  \n",
+       "0  https://d.org/ne_datasette/ne_dominant_topics/0  \n",
+       "1  https://d.org/ne_datasette/ne_dominant_topics/1  \n",
+       "2  https://d.org/ne_datasette/ne_dominant_topics/2  \n",
+       "3  https://d.org/ne_datasette/ne_dominant_topics/3  \n",
+       "4  https://d.org/ne_datasette/ne_dominant_topics/4  \n",
+       "5  https://d.org/ne_datasette/ne_dominant_topics/5  \n",
+       "6  https://d.org/ne_datasette/ne_dominant_topics/6  \n",
+       "7  https://d.org/ne_datasette/ne_dominant_topics/7  \n",
+       "8  https://d.org/ne_datasette/ne_dominant_topics/8  \n",
+       "9  https://d.org/ne_datasette/ne_dominant_topics/9  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df and lda_model\n",
+    "#\n",
+    "# retrieve ldamodel (if not already loaded\n",
+    "data_lemmatized = tm_utils.get_lemmatized(corpusname=corpusname, datadir=DATA)\n",
+    "id2word, corpus = tm_utils.get_corpus_dictionary(data_lemmatized, \n",
+    "                        corpusname=corpusname, save=False, \n",
+    "                        datadir=DATA, from_file=True)\n",
+    "modelfile = str(DATA.joinpath(\"{}_lda_top28.model\".format(corpusname)))\n",
+    "lda_model = gensim.models.ldamodel.LdaModel.load(modelfile)\n",
+    "lda_model = model_gensim\n",
+    "# retrieve orig dataframe\n",
+    "outputfile = DATA.joinpath(f\"{corpusname}_01.csv\")\n",
+    "raw_df = pd.read_csv(outputfile, sep=\";\")\n",
+    "\n",
+    "# collect topic words\n",
+    "topics = {}\n",
+    "for t in lda_model.show_topics(num_topics=28, num_words=10, log=False, formatted=False):\n",
+    "    tnum = t[0]\n",
+    "    tl = t[1]\n",
+    "    topics[tnum] = \", \".join([word for word, prop in tl])\n",
+    "    \n",
+    "# pprint(topics)\n",
+    "\n",
+    "def format_topics_sentences(ldamodel=None, corpus=None, texts=None, topics=None):\n",
+    "    # cf. `testing_103.ipynb`\n",
+    "    from operator import itemgetter\n",
+    "    sent_topics_df = pd.DataFrame()\n",
+    "    # topics = {}\n",
+    "    # Get main topic in each document\n",
+    "    out = []\n",
+    "    for i, row in enumerate(ldamodel[corpus]):\n",
+    "        row_item = row\n",
+    "        row_item = sorted(row_item, key=itemgetter(1), reverse=True)[0]\n",
+    "        topic_num, prop_topic = row_item\n",
+    "        topic_keywords = topics[topic_num]\n",
+    "        out.append([int(topic_num), round(prop_topic,4), \n",
+    "                                    str(topic_num)+\": \" + topic_keywords])\n",
+    "    sent_topics_df = pd.DataFrame(out, columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']) \n",
+    "    contents = texts\n",
+    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
+    "    sent_topics_df['Dominant_Topic'] = sent_topics_df['Dominant_Topic'].fillna(0).apply(lambda x: str(int(x)))\n",
+    "    return sent_topics_df\n",
+    "\n",
+    "data = raw_df['text']\n",
+    "\n",
+    "# construct a URL to link to database instance:\n",
+    "urltempl = \"https://d.org/ne_datasette/ne_dominant_topics/{}\"\n",
+    "\n",
+    "\n",
+    "df_topic_sent_keywords = format_topics_sentences(ldamodel=lda_model, \n",
+    "                                                 corpus=corpus, texts=data, topics=topics)\n",
+    "\n",
+    "# Format\n",
+    "df_dominant_topic['link'] = urltempl.format(df_dominant_topic.index)\n",
+    "df_dominant_topic = df_topic_sent_keywords.reset_index()\n",
+    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
+    "df_dominant_topic['Link'] = df_dominant_topic['Document_No'].apply(lambda x: urltempl.format(x))\n",
+    "\n",
+    "df_dominant_topic.head()\n",
+    "\n",
+    "# Show\n",
+    "df_dominant_topic.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Most Representative Topic\n",
+    "Then we show the documents a given topic has contributed to the most, so the topic may be inferred by reading that documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Document_No</th>\n",
+       "      <th>Topic_Num</th>\n",
+       "      <th>Topic_Perc_Contrib</th>\n",
+       "      <th>Keywords</th>\n",
+       "      <th>Text</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</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>13557</th>\n",
+       "      <td>13557</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2919</td>\n",
+       "      <td>0: britisch, italienisch, korrespondenten, bri...</td>\n",
+       "      <td>London, 29. Mai. Eden legte heute dem Kabinett...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3616</th>\n",
+       "      <td>3616</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.2835</td>\n",
+       "      <td>1: melden, nachrichten, regierung, nachricht, ...</td>\n",
+       "      <td>Spanien. Ueber den Karlisten=Aufstand in Spani...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10746</th>\n",
+       "      <td>10746</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.4546</td>\n",
+       "      <td>10: stunden, wahren, schwer, befinden, sofort,...</td>\n",
+       "      <td>KB. Rotterdam, 23. April. Der „DailyTelegraph“...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8511</th>\n",
+       "      <td>8511</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.4036</td>\n",
+       "      <td>11: telephon, verschieden, wahren, fern, beste...</td>\n",
+       "      <td>Balatonfüred Begt am Balaton sein Klima ist mi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5309</th>\n",
+       "      <td>5309</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.3685</td>\n",
+       "      <td>12: schreiben, englisch, pressen, blatt, londo...</td>\n",
+       "      <td>Sbesessad Aisecn\\nin unmittelbarer Nähe von Se...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8270</th>\n",
+       "      <td>8270</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.3546</td>\n",
+       "      <td>13: letzt, wahren, stehen, arbeiter, arbeit, a...</td>\n",
+       "      <td>bicher sesenden in Grostelanten siud\\nArbeitge...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4100</th>\n",
+       "      <td>4100</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.4718</td>\n",
+       "      <td>14: bringen, beginnen, konnen, kriege, halten,...</td>\n",
+       "      <td>(Der moderne Bergmann.) Ein Korrespondent des ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8551</th>\n",
+       "      <td>8551</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.1902</td>\n",
+       "      <td>15: gestern, antwort, erwarten, erfahren, erkl...</td>\n",
+       "      <td>Proluiden\\nSommerfrische\\nherrliche geschützte...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4447</th>\n",
+       "      <td>4447</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.4195</td>\n",
+       "      <td>16: wiener, wiener_privat, privat, bericht, ge...</td>\n",
+       "      <td>Bauverein, niederösterr., 100fl. 5.—fl.\\nBierb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2416</th>\n",
+       "      <td>2416</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.3127</td>\n",
+       "      <td>17: konig, bringen, bleiben, landes, halten, r...</td>\n",
+       "      <td>Linz. 20. August. [Orig.=Corr.] (Ein Hirtenbri...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323</th>\n",
+       "      <td>323</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.5023</td>\n",
+       "      <td>18: oesterreich, preußisch, seite, politisch, ...</td>\n",
+       "      <td>Pest, 22. October. [Orig,=Corr.] (Zur Beurthei...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13390</th>\n",
+       "      <td>13390</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.4565</td>\n",
+       "      <td>19: minister, stimmen, ministerium, partei, si...</td>\n",
+       "      <td>Paris, 24. Januar. (Amtliche Nachrichtenstelle...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7934</th>\n",
+       "      <td>7934</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.3245</td>\n",
+       "      <td>2: melden, polizei, verhaften, sofort, begeben...</td>\n",
+       "      <td>Wien; mildes Klima,\\ntouren;\\nruhiger Aufentha...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11056</th>\n",
+       "      <td>11056</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.3045</td>\n",
+       "      <td>20: glauben, finden, sprechen, mussen, scheine...</td>\n",
+       "      <td>Berlin, 22. Januar.\\nDie „Vossische Zeitung“ m...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10843</th>\n",
+       "      <td>10843</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.2840</td>\n",
+       "      <td>21: regierung, fragen, verlangen, verhandlunge...</td>\n",
+       "      <td>Rotterdam, 2. August.\\nLaut dem „Nieuwe Rotter...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7467</th>\n",
+       "      <td>7467</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0.3192</td>\n",
+       "      <td>22: fragen, politik, krieg, konne, frieden, be...</td>\n",
+       "      <td>Der „DailyTelegraph ertiart, es könne keine\\nR...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1042</th>\n",
+       "      <td>1042</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.3246</td>\n",
+       "      <td>23: kaiser, franzosischen, pariser, franzosisc...</td>\n",
+       "      <td>Der Telegraph meldet heute, daß Herr Achilles ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11278</th>\n",
+       "      <td>11278</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.3993</td>\n",
+       "      <td>24: stadt, gestern, folgen, abends, berichten,...</td>\n",
+       "      <td>geriel die tschechische Bevölkerung sofort in ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8693</th>\n",
+       "      <td>8693</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.3730</td>\n",
+       "      <td>25: deutsch, englisch, englische, schiffe, flo...</td>\n",
+       "      <td>Die größte Ueberraschung bietet das Verhalten\\...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12219</th>\n",
+       "      <td>12219</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.2967</td>\n",
+       "      <td>26: konnen, schaffen, alt, mussen, krieg, lieg...</td>\n",
+       "      <td>Und ebenso ist in allen Kulturen die Technik d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5334</th>\n",
+       "      <td>5334</td>\n",
+       "      <td>27</td>\n",
+       "      <td>0.5415</td>\n",
+       "      <td>27: telegraphen, verbindung, linie, kabel, ver...</td>\n",
+       "      <td>(Die Telegraphie ohne Draht.) Im\\nrömischen Ma...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12180</th>\n",
+       "      <td>12180</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.6162</td>\n",
+       "      <td>3: millionen, telephon, betragen, einnahmen, f...</td>\n",
+       "      <td>Der Finanzminister wiederholt dann seine Ausfü...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7599</th>\n",
+       "      <td>7599</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.4624</td>\n",
+       "      <td>4: telephon, zimmer, hause, kuche, garen, verk...</td>\n",
+       "      <td>Vornehmes Landhaus,\\nHochparterre, in schönste...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13883</th>\n",
+       "      <td>13883</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.3554</td>\n",
+       "      <td>5: bringen, zeitung, artikel, erscheinen, reis...</td>\n",
+       "      <td>Schubert: a) Widerschein; b) An die Musik. Gri...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4693</th>\n",
+       "      <td>4693</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.3851</td>\n",
+       "      <td>6: truppen, armee, general, melden, grenze, so...</td>\n",
+       "      <td>bestiegen. Kaiser Wilhelm trug heute die schwa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6803</th>\n",
+       "      <td>6803</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.2748</td>\n",
+       "      <td>7: melden, meldung, russisch, amerikanisch, be...</td>\n",
+       "      <td>Wien, 23. Juli.\\nDer angeblichen Antwortdepesc...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12530</th>\n",
+       "      <td>12530</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.5126</td>\n",
+       "      <td>8: prozent, borse, stark, zeigen, hoch, haltun...</td>\n",
+       "      <td>Vorgänge in U. S. A. zurück. Man hofft aber au...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1142</th>\n",
+       "      <td>1142</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.4138</td>\n",
+       "      <td>9: finden, erscheinen, bekannt, erhalten, nenn...</td>\n",
+       "      <td>[Adalbert Stifter †.] Der Telegraph hat uns he...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Document_No Topic_Num  Topic_Perc_Contrib  \\\n",
+       "index                                              \n",
+       "13557        13557         0              0.2919   \n",
+       "3616          3616         1              0.2835   \n",
+       "10746        10746        10              0.4546   \n",
+       "8511          8511        11              0.4036   \n",
+       "5309          5309        12              0.3685   \n",
+       "8270          8270        13              0.3546   \n",
+       "4100          4100        14              0.4718   \n",
+       "8551          8551        15              0.1902   \n",
+       "4447          4447        16              0.4195   \n",
+       "2416          2416        17              0.3127   \n",
+       "323            323        18              0.5023   \n",
+       "13390        13390        19              0.4565   \n",
+       "7934          7934         2              0.3245   \n",
+       "11056        11056        20              0.3045   \n",
+       "10843        10843        21              0.2840   \n",
+       "7467          7467        22              0.3192   \n",
+       "1042          1042        23              0.3246   \n",
+       "11278        11278        24              0.3993   \n",
+       "8693          8693        25              0.3730   \n",
+       "12219        12219        26              0.2967   \n",
+       "5334          5334        27              0.5415   \n",
+       "12180        12180         3              0.6162   \n",
+       "7599          7599         4              0.4624   \n",
+       "13883        13883         5              0.3554   \n",
+       "4693          4693         6              0.3851   \n",
+       "6803          6803         7              0.2748   \n",
+       "12530        12530         8              0.5126   \n",
+       "1142          1142         9              0.4138   \n",
+       "\n",
+       "                                                Keywords  \\\n",
+       "index                                                      \n",
+       "13557  0: britisch, italienisch, korrespondenten, bri...   \n",
+       "3616   1: melden, nachrichten, regierung, nachricht, ...   \n",
+       "10746  10: stunden, wahren, schwer, befinden, sofort,...   \n",
+       "8511   11: telephon, verschieden, wahren, fern, beste...   \n",
+       "5309   12: schreiben, englisch, pressen, blatt, londo...   \n",
+       "8270   13: letzt, wahren, stehen, arbeiter, arbeit, a...   \n",
+       "4100   14: bringen, beginnen, konnen, kriege, halten,...   \n",
+       "8551   15: gestern, antwort, erwarten, erfahren, erkl...   \n",
+       "4447   16: wiener, wiener_privat, privat, bericht, ge...   \n",
+       "2416   17: konig, bringen, bleiben, landes, halten, r...   \n",
+       "323    18: oesterreich, preußisch, seite, politisch, ...   \n",
+       "13390  19: minister, stimmen, ministerium, partei, si...   \n",
+       "7934   2: melden, polizei, verhaften, sofort, begeben...   \n",
+       "11056  20: glauben, finden, sprechen, mussen, scheine...   \n",
+       "10843  21: regierung, fragen, verlangen, verhandlunge...   \n",
+       "7467   22: fragen, politik, krieg, konne, frieden, be...   \n",
+       "1042   23: kaiser, franzosischen, pariser, franzosisc...   \n",
+       "11278  24: stadt, gestern, folgen, abends, berichten,...   \n",
+       "8693   25: deutsch, englisch, englische, schiffe, flo...   \n",
+       "12219  26: konnen, schaffen, alt, mussen, krieg, lieg...   \n",
+       "5334   27: telegraphen, verbindung, linie, kabel, ver...   \n",
+       "12180  3: millionen, telephon, betragen, einnahmen, f...   \n",
+       "7599   4: telephon, zimmer, hause, kuche, garen, verk...   \n",
+       "13883  5: bringen, zeitung, artikel, erscheinen, reis...   \n",
+       "4693   6: truppen, armee, general, melden, grenze, so...   \n",
+       "6803   7: melden, meldung, russisch, amerikanisch, be...   \n",
+       "12530  8: prozent, borse, stark, zeigen, hoch, haltun...   \n",
+       "1142   9: finden, erscheinen, bekannt, erhalten, nenn...   \n",
+       "\n",
+       "                                                    Text  \n",
+       "index                                                     \n",
+       "13557  London, 29. Mai. Eden legte heute dem Kabinett...  \n",
+       "3616   Spanien. Ueber den Karlisten=Aufstand in Spani...  \n",
+       "10746  KB. Rotterdam, 23. April. Der „DailyTelegraph“...  \n",
+       "8511   Balatonfüred Begt am Balaton sein Klima ist mi...  \n",
+       "5309   Sbesessad Aisecn\\nin unmittelbarer Nähe von Se...  \n",
+       "8270   bicher sesenden in Grostelanten siud\\nArbeitge...  \n",
+       "4100   (Der moderne Bergmann.) Ein Korrespondent des ...  \n",
+       "8551   Proluiden\\nSommerfrische\\nherrliche geschützte...  \n",
+       "4447   Bauverein, niederösterr., 100fl. 5.—fl.\\nBierb...  \n",
+       "2416   Linz. 20. August. [Orig.=Corr.] (Ein Hirtenbri...  \n",
+       "323    Pest, 22. October. [Orig,=Corr.] (Zur Beurthei...  \n",
+       "13390  Paris, 24. Januar. (Amtliche Nachrichtenstelle...  \n",
+       "7934   Wien; mildes Klima,\\ntouren;\\nruhiger Aufentha...  \n",
+       "11056  Berlin, 22. Januar.\\nDie „Vossische Zeitung“ m...  \n",
+       "10843  Rotterdam, 2. August.\\nLaut dem „Nieuwe Rotter...  \n",
+       "7467   Der „DailyTelegraph ertiart, es könne keine\\nR...  \n",
+       "1042   Der Telegraph meldet heute, daß Herr Achilles ...  \n",
+       "11278  geriel die tschechische Bevölkerung sofort in ...  \n",
+       "8693   Die größte Ueberraschung bietet das Verhalten\\...  \n",
+       "12219  Und ebenso ist in allen Kulturen die Technik d...  \n",
+       "5334   (Die Telegraphie ohne Draht.) Im\\nrömischen Ma...  \n",
+       "12180  Der Finanzminister wiederholt dann seine Ausfü...  \n",
+       "7599   Vornehmes Landhaus,\\nHochparterre, in schönste...  \n",
+       "13883  Schubert: a) Widerschein; b) An die Musik. Gri...  \n",
+       "4693   bestiegen. Kaiser Wilhelm trug heute die schwa...  \n",
+       "6803   Wien, 23. Juli.\\nDer angeblichen Antwortdepesc...  \n",
+       "12530  Vorgänge in U. S. A. zurück. Man hofft aber au...  \n",
+       "1142   [Adalbert Stifter †.] Der Telegraph hat uns he...  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Group top 5 documents under each topic\n",
+    "\n",
+    "mr_topics = pd.DataFrame()\n",
+    "\n",
+    "mr_topics_grpd = df_topic_sent_keywords.groupby('Dominant_Topic')\n",
+    "\n",
+    "for i, grp in mr_topics_grpd:\n",
+    "    mr_topics = pd.concat([mr_topics, grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n",
+    "                                axis=0)\n",
+    "    \n",
+    "# Reset Index    \n",
+    "mr_topics.reset_index(inplace=True)\n",
+    "mr_topics.set_index('index', drop=False, inplace=True)\n",
+    "# Format\n",
+    "mr_topics.columns = ['Document_No','Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n",
+    "\n",
+    "# Show\n",
+    "mr_topics.head(32)\n",
+    "# print(df_topic_sent_keywords['Dominant_Topic'].unique())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}