Clustering.ipynb

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   "source": [
    "# Visualizing Tweets Using LDA Based Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Code to Import Libraries\n",
    "import pandas as pd\n",
    "import nltk\n",
    "from nltk.corpus import stopwords \n",
    "import gensim\n",
    "from gensim import corpora\n",
    "from gensim.models import CoherenceModel\n",
    "stopwords = set(stopwords.words('english'))\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#nltk.download('stopwords')\n",
    "#nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Code Load_ Data into a List\n",
    "tweetsInfo = pd.read_csv('AllTweetInfo.csv')\n",
    "\n",
    "#Get Text for Topics\n",
    "def MorePreprocessing(text):\n",
    "    text_new = [t for t in text.split() if t not in stopwords]\n",
    "    text_new = [t for t in text_new if not str.isnumeric(t)]\n",
    "    \n",
    "    return text_new\n",
    "    \n",
    "tweetsInfo['topic_text'] =tweetsInfo['text_features_new'].apply(MorePreprocessing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "### Creat LDA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# Train_LDA_ Model\n",
    "t_list = tweetsInfo['topic_text'].tolist()\n",
    "corpdict = corpora.Dictionary(t_list)\n",
    "\n",
    "doc_term_matrix = [corpdict.doc2bow(doc) for doc in t_list]\n",
    "corpora.MmCorpus.serialize('corpus.mm', doc_term_matrix)\n",
    "\n",
    "Lda = gensim.models.ldamodel.LdaModel\n",
    "ldamodel = Lda(doc_term_matrix, num_topics=20, id2word = corpdict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Topics (Clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
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       "\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_el5481402220223805441900910269\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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\"score\", \"scott\", \"script\", \"scripture\", \"sculpture\", \"sculpture\", \"sculptureseminar\", \"sdpi\", \"searched\", \"seashell\", \"seating\", \"secondbest\", \"secret\", \"secretly\", \"secure\", \"security\", \"security\", \"security\", \"security\", \"security\", \"security\", \"security\", \"see\", \"see\", \"see\", \"see\", \"see\", \"see\", \"see\", \"see\", \"see\", \"see\", \"seeclearly\", \"seed\", \"seed\", \"seems\", \"self\", \"self\", \"selfcontrol\", \"selfies\", \"semester\", \"seminar\", \"sense\", \"seo\", \"seo\", \"seo\", \"sequentially\", \"serene\", \"seriously\", \"server\", \"servewaco\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"serviceability\", \"servicing\", \"serving\", \"serving\", \"session\", \"session\", \"session\", \"set\", \"set\", \"set\", \"set\", \"set\", \"set\", \"seth\", \"seva\", \"sfs1912\", \"shade\", \"share\", \"share\", \"share\", \"share\", \"sharjah\", \"shawl\", \"shawlpins\", \"sheikh\", \"shell\", \"shepherdsheart\", \"shift\", \"shin\", \"shine\", \"shine\", \"shine\", \"shine\", \"shocker\", \"shook\", \"shortsleeve\", \"shotblasting\", \"shout\", \"shubham\", \"shulkin\", \"sick\", \"sids\", \"silence\", \"silverhill\", \"single\", \"sir\", \"sixer\", \"sketch\", \"skill\", \"sleep\", \"sleep\", \"slovak\", \"smartmoney\", \"smartmoney\", \"smartmoney\", \"snake\", \"snow\", \"snowday\", \"snowmaggedon2019\", \"snowpocolypse\", \"snowy\", \"socialmediaagency\", \"sock\", \"socom\", \"softpacks\", \"softwaretesting\", \"solar\", \"solidwood\", \"solitude\", \"solo\", \"somehow\", \"son\", \"soror\", \"sostenibilit\", \"soulechoes\", \"soulful\", \"soulful\", \"soulful\", \"sounded\", \"space\", \"space\", \"space\", \"spain\", \"special\", \"special\", \"special\", \"special\", \"special\", \"special\", \"special\", \"special\", \"specialist\", \"specialistarindambhattacharyaholisollogisticscom\", \"specially\", \"specific\", \"specification\", \"speed\", \"speed\", \"speed\", \"spelpreneur\", \"spending\", \"spent\", \"spill\", \"spintex\", \"spiral\", \"spirit\", \"spiritual\", \"splendid\", \"splitter\", \"spotify\", \"spouse\", \"square\", \"squawk\", \"squirhill\", \"srl\", \"ss19\", \"stabilise\", \"stadium\", \"stadium\", \"stale\", \"stamp\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standup\", \"stare\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"start\", \"state\", \"state\", \"state\", \"state\", \"state\", \"station\", \"stats\", \"stavanger\", \"stay\", \"stay\", \"stay\", \"stay\", \"stay\", \"stay\", \"stay\", \"staying\", \"steam\", \"steel\", \"steelite\", \"steelite\", \"steelwork\", \"stepbystep\", 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\"survive\", \"sv\", \"svg\", \"swarn\", \"sweazer\", \"swing\", \"swiss\", \"sydney\", \"sydney\", \"symmetrical\", \"synchronicity\", \"syndicate\", \"syndicate\", \"syndicate\", \"syndicate\", \"syndicate\", \"syndicate\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"table\", \"tabletopgames\", \"taboo\", \"tackle\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"takeaway\", \"taker\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"talk\", \"tapa\", \"target\", \"target\", \"taught\", \"tax\", \"taxcredits\", \"tbox\", \"teacher\", \"teacher\", \"tech\", \"tech\", \"tech\", \"tech\", \"tech\", \"technews\", \"technical\", \"technical\", \"technician\", \"technician\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"tee\", \"template\", \"teshima\", \"test\", \"test\", \"testpyramid\", \"tex\", \"textile\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thanks\", \"thegodslayer\", \"theme\", \"theme\", \"themfirst\", \"theosis\", \"theosophy\", \"therapy\", \"thereby\", \"therevolution\", \"thermosteel\", \"third\", \"thirdparty\", \"thirdpartysupport\", \"thomas\", \"thr\", \"three\", \"threepointer\", \"throughout\", \"thug\", \"thx\", \"thx\", \"tide\", \"tied\", \"tile\", \"tile\", \"till\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"timeframe\", \"timetodobig\", \"titanium\", \"titleholders\", \"tku\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"togetherwerise\", \"told\", \"tomato\", \"tomorrow\", \"tomorrow\", \"tomorrow\", \"tomorrow\", \"tomorrow\", \"took\", \"tool\", \"tool\", \"tool\", \"tool\", \"top\", \"top\", \"top\", \"top\", \"top\", \"top\", \"topbrass\", \"topbrass\", \"topfair\", \"toronto\", \"totally\", \"tournament\", \"tournament\", \"towards\", \"towards\", \"track\", \"track\", \"track\", \"tracking\", \"tradefair\", \"tradefair\", \"tradefair\", \"tradeshow\", \"tradeshow\", \"tradeshow\", \"tradeshow\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"tradingtips\", \"traditional\", \"traditionalcrafts\", \"train\", \"train\", \"training\", \"training\", \"training\", \"training\", \"training\", \"transactional\", \"transparency\", \"transparency\", \"transport\", \"transportation\", \"travel\", \"travel\", \"travel\", \"travel\", \"travel\", \"travel\", \"travel\", \"travel\", \"treasure\", \"tree\", \"tremendous\", \"trendline\", \"trendy\", \"trendy\", \"triangle\", \"trick\", \"tried\", \"trojan\", \"tron\", \"tron\", \"tron\", \"trooper\", \"trophy\", \"truckingedit\", \"trump2020\", \"trumptowermoscow\", \"trx\", \"trx\", \"trx\", \"trx\", \"trx\", \"trx\", \"trx\", \"tshirt\", \"tube\", \"tuberculosis\", \"tubo\", \"tubuler\", \"tuesdaymotivation\", \"tulum\", \"turner\", \"twas\", \"twice\", \"twitch\", \"twitchstreamer\", \"twon\", \"tyre\", \"tyson\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"u\", \"uae\", \"ubc\", \"ubereats\", \"uidesign\", \"ukg\", \"ukg\", \"ukg\", \"ukraine\", \"unani\", \"unbeatable\", \"underestimated\", \"undermines\", \"understand\", \"understand\", \"understand\", \"understanding\", \"underway\", \"unemployed\", \"unicorn\", \"unity\", \"unless\", \"unless\", \"unlisted\", \"unlucky\", \"unsatisfied\", \"unternehmer\", \"unto\", \"update\", \"update\", \"update\", \"update\", \"update\", \"upon\", \"uptime\", \"upto\", \"upvc\", \"urban\", \"urban\", \"urban\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"usda\", \"use\", \"use\", \"use\", \"use\", \"usmail\", \"uttarakhand\", \"uttarakhand\", \"uttarakhand\", \"uttarakhand\", \"uxdesign\", \"vakt\", \"valentine\", \"valentine\", \"valentine\", \"valentine\", \"valentine\", \"valentine\", \"valentine\", \"valentinegift\", \"valet\", \"valoremcurrency\", \"valued\", \"vanpoli\", \"vantage\", \"varies\", \"variety\", \"various\", \"varying\", \"vega\", \"vega\", \"vega\", \"vega\", \"vega\", \"vega\", \"vegan\", \"vegan\", \"vehicle\", \"vendor\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venue\", \"verification\", \"verified\", \"verified\", \"verified\", \"verified\", \"verified\", \"versatile\", \"via\", \"via\", \"via\", \"via\", \"via\", \"via\", \"via\", \"via\", \"vic\", \"view\", \"view\", 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       "\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_el5481402220223805441900910269\", ldavis_el5481402220223805441900910269_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_el5481402220223805441900910269\", ldavis_el5481402220223805441900910269_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_el5481402220223805441900910269\", ldavis_el5481402220223805441900910269_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize Models\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "pyLDAvis.enable_notebook()\n",
    "corpora =  gensim.corpora.MmCorpus('corpus.mm')\n",
    "vis =pyLDAvis.gensim.prepare(ldamodel, corpora, corpdict)\n",
    "pyLDAvis.display(vis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try to Find Right Setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# Code to FInd Right LDA Setting\n",
    "#Refernece from https://markroxor.github.io/gensim/static/notebooks/gensim\n",
    "def evaluate_graph(dictionary, corpus, texts, limit):\n",
    "    \"\"\"\n",
    "    Function to display num_topics - LDA graph using c_v coherence\n",
    "    \n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    limit : topic limit\n",
    "    \n",
    "    Returns:\n",
    "    -------\n",
    "    lm_list : List of LDA topic models\n",
    "    c_v : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "    c_v = []\n",
    "    lm_list = []\n",
    "    for num_topics in range(1, limit):\n",
    "        lm = gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary)\n",
    "        lm_list.append(lm)\n",
    "        cm = CoherenceModel(model=lm, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        c_v.append(cm.get_coherence())\n",
    "        \n",
    "    # Show graph\n",
    "    x = range(1, limit)\n",
    "    plt.plot(x, c_v)\n",
    "    plt.xlabel(\"num_topics\")\n",
    "    plt.ylabel(\"Coherence score\")\n",
    "    plt.legend((\"c_v\"), loc='best')\n",
    "    plt.show()\n",
    "    \n",
    "    return lm_list, c_v\n",
    "\n",
    "lmlist, c_v = evaluate_graph(dictionary=corpdict, corpus=corpora, texts=t_list, limit=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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_el5481402219597152405386190107\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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\"reservation\", \"reservation\", \"reservation\", \"reservation\", \"reservationpolitics\", \"retrosuperfuture\", \"reward\", \"rework\", \"ripple\", \"ripple\", \"ripple\", \"ripple\", \"ripple\", \"sabka\", \"said\", \"said\", \"sale\", \"sale\", \"sale\", \"sale\", \"sale\", \"sarries\", \"satin\", \"sc\", \"scenario\", \"school\", \"school\", \"scifi\", \"screen\", \"sdpi\", \"self\", \"seo\", \"seo\", \"service\", \"service\", \"service\", \"service\", \"service\", \"shade\", \"shade\", \"sharp\", \"shell\", \"shipment\", \"smart\", \"soccer\", \"soccer\", \"soccer\", \"south\", \"special\", \"special\", \"special\", \"special\", \"special\", \"spill\", \"spread\", \"stand\", \"stand\", \"stand\", \"stand\", \"stand\", \"stock\", \"stock\", \"stock\", \"store\", \"store\", \"store\", \"streamlining\", \"sts\", \"sundaymotivation\", \"supply\", \"syndicate\", \"syndicate\", \"syndicate\", \"take\", \"take\", \"take\", \"take\", \"take\", \"talk\", \"talk\", \"talk\", \"team\", \"team\", \"team\", \"team\", \"team\", \"tech\", \"tech\", \"tech\", \"technology\", \"technology\", \"technology\", \"technology\", \"teshima\", \"thank\", \"thank\", \"thank\", \"thank\", \"thank\", \"tile\", \"time\", \"time\", \"time\", \"time\", \"time\", \"titanium\", \"today\", \"today\", \"today\", \"today\", \"today\", \"topbrass\", \"topbrass\", \"total\", \"total\", \"total\", \"tournament\", \"track\", \"trading\", \"trading\", \"trading\", \"trading\", \"trading\", \"trooper\", \"truck\", \"twon\", \"u\", \"u\", \"u\", \"u\", \"u\", \"unani\", \"upvc\", \"usd\", \"usd\", \"usd\", \"usd\", \"usd\", \"use\", \"use\", \"use\", \"use\", \"uttarakhand\", \"uttarakhand\", \"uttarakhand\", \"va\", \"valentine\", \"valentine\", \"valentine\", \"value\", \"value\", \"value\", \"value\", \"vega\", \"vega\", \"vega\", \"vega\", \"vegan\", \"vendor\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"venezuela\", \"verified\", \"verified\", \"verified\", \"view\", \"view\", \"volkswagen\", \"wa\", \"wa\", \"wa\", \"wa\", \"wa\", \"waiting\", \"waiting\", \"want\", \"want\", \"want\", \"want\", \"want\", \"warmth\", \"warmth\", \"warranty\", \"water\", \"water\", \"whatsapp\", \"whatsapp\", \"whatsapp\", \"whatsapp\", \"wing\", \"wondrous\", \"wood\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worldwide\", \"worth\", \"worth\", \"worth\", \"worth\", \"xbt\", \"xrp\", \"xrp\", \"xrp\", \"xrp\", \"xrp\", \"york\", \"york\", \"zebra\", \"zeta\", \"zurich\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 2, 1, 3, 4]};\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "4      22.718800        1       1  0.033450 -0.011423\n",
       "1      21.217037        1       2  0.027553 -0.059803\n",
       "0      19.065811        1       3 -0.080013  0.069475\n",
       "2      18.652388        1       4 -0.064048 -0.050598\n",
       "3      18.345970        1       5  0.083059  0.052349, topic_info=     Category        Freq            Term       Total  loglift  logprob\n",
       "term                                                                   \n",
       "250   Default   38.000000           offer   38.000000  30.0000  30.0000\n",
       "456   Default  179.000000         quality  179.000000  29.0000  29.0000\n",
       "2850  Default   57.000000             btc   57.000000  28.0000  28.0000\n",
       "2528  Default   78.000000         bitcoin   78.000000  27.0000  27.0000\n",
       "2109  Default   19.000000            gold   19.000000  26.0000  26.0000\n",
       "2808  Default   36.000000  cryptocurrency   36.000000  25.0000  25.0000\n",
       "2875  Default   15.000000               r   15.000000  24.0000  24.0000\n",
       "15    Default  198.000000         service  198.000000  23.0000  23.0000\n",
       "2727  Default   14.000000              oz   14.000000  22.0000  22.0000\n",
       "2910  Default   15.000000       syndicate   15.000000  21.0000  21.0000\n",
       "2851  Default   37.000000          crypto   37.000000  20.0000  20.0000\n",
       "2906  Default   16.000000       freepicks   16.000000  19.0000  19.0000\n",
       "2903  Default   16.000000      draftkings   16.000000  18.0000  18.0000\n",
       "2020  Default   26.000000        ambiente   26.000000  17.0000  17.0000\n",
       "2904  Default   16.000000         fanduel   16.000000  16.0000  16.0000\n",
       "33    Default   40.000000             new   40.000000  15.0000  15.0000\n",
       "2902  Default   16.000000             dfs   16.000000  14.0000  14.0000\n",
       "2569  Default   18.000000             per   18.000000  13.0000  13.0000\n",
       "2905  Default   18.000000           forex   18.000000  12.0000  12.0000\n",
       "2811  Default   39.000000        ethereum   39.000000  11.0000  11.0000\n",
       "117   Default   11.000000        gambling   11.000000  10.0000  10.0000\n",
       "2799  Default   16.000000             buy   16.000000   9.0000   9.0000\n",
       "476   Default   36.000000         product   36.000000   8.0000   8.0000\n",
       "2913  Default   11.000000        verified   11.000000   7.0000   7.0000\n",
       "223   Default   36.000000             day   36.000000   6.0000   6.0000\n",
       "1597  Default   17.000000       venezuela   17.000000   5.0000   5.0000\n",
       "2901  Default   12.000000          5dimes   12.000000   4.0000   4.0000\n",
       "53    Default   27.000000            love   27.000000   3.0000   3.0000\n",
       "2835  Default   21.000000        currency   21.000000   2.0000   2.0000\n",
       "131   Default   12.000000            vega   12.000000   1.0000   1.0000\n",
       "...       ...         ...             ...         ...      ...      ...\n",
       "5257   Topic5    2.731951     uttarakhand    4.275555   1.2479  -6.8725\n",
       "1967   Topic5    1.954989     picoftheday    2.856524   1.3165  -7.2072\n",
       "456    Topic5   49.925735         quality  179.245697   0.4175  -3.9670\n",
       "5207   Topic5    2.330020        approves    3.582735   1.2655  -7.0317\n",
       "4460   Topic5    1.952059       blackhawk    2.857064   1.3149  -7.2087\n",
       "33     Topic5   14.767710             new   40.033020   0.6985  -5.1851\n",
       "293    Topic5    7.028873            life   15.090288   0.9317  -5.9275\n",
       "3432   Topic5    3.307852        handmade    5.757078   1.1416  -6.6813\n",
       "53     Topic5   10.367691            love   27.990320   0.7026  -5.5389\n",
       "5187   Topic5   16.771542     reservation   54.885818   0.5102  -5.0579\n",
       "1321   Topic5   36.558651           price  196.564255   0.0137  -4.2786\n",
       "15     Topic5   27.908009         service  198.899307  -0.2681  -4.5486\n",
       "360    Topic5   10.195380               u   38.388458   0.3699  -5.5556\n",
       "2729   Topic5    8.884586             usd   33.008160   0.3833  -5.6932\n",
       "769    Topic5    4.271469            read    9.992751   0.8459  -6.4256\n",
       "279    Topic5    4.835965       education   13.038876   0.7039  -6.3015\n",
       "460    Topic5    4.758101         project   13.689474   0.6390  -6.3177\n",
       "412    Topic5    6.589697           today   27.479282   0.2678  -5.9920\n",
       "155    Topic5    5.252466            sale   18.038328   0.4620  -6.2189\n",
       "77     Topic5    5.714372              ha   23.161247   0.2963  -6.1346\n",
       "1090   Topic5    4.262256          please   12.153003   0.6480  -6.4278\n",
       "202    Topic5    5.344477          market   20.956705   0.3294  -6.2015\n",
       "150    Topic5    3.914342            high   11.516560   0.6166  -6.5129\n",
       "472    Topic5    4.642591           india   20.200893   0.2253  -6.3423\n",
       "533    Topic5    4.586169              wa   21.074818   0.1707  -6.3545\n",
       "228    Topic5    3.964418            team   13.029318   0.5059  -6.5002\n",
       "223    Topic5    4.832883             day   36.573597  -0.3281  -6.3021\n",
       "2528   Topic5    5.054530         bitcoin   78.125633  -1.0423  -6.2573\n",
       "285    Topic5    4.028172           value   15.121300   0.3730  -6.4842\n",
       "272    Topic5    3.981618            time   16.654358   0.2648  -6.4959\n",
       "\n",
       "[410 rows x 6 columns], token_table=      Topic      Freq           Term\n",
       "term                                \n",
       "2901      1  0.081521         5dimes\n",
       "2901      2  0.081521         5dimes\n",
       "2901      3  0.407605         5dimes\n",
       "2901      4  0.407605         5dimes\n",
       "3444      2  0.914790           abra\n",
       "5082      5  0.934972  accreditation\n",
       "4851      5  0.934984      achieving\n",
       "916       2  0.822212       adoption\n",
       "2525      1  0.258260        airdrop\n",
       "2525      2  0.430434        airdrop\n",
       "2525      4  0.258260        airdrop\n",
       "1813      4  0.939925        airline\n",
       "4180      3  0.908259        alcohol\n",
       "1159      1  0.679791       allergan\n",
       "48        1  0.531109         always\n",
       "48        2  0.227618         always\n",
       "48        3  0.075873         always\n",
       "48        5  0.151745         always\n",
       "2020      1  0.151600       ambiente\n",
       "2020      2  0.113700       ambiente\n",
       "2020      3  0.037900       ambiente\n",
       "2020      4  0.568501       ambiente\n",
       "2020      5  0.151600       ambiente\n",
       "1977      1  0.230037       analysis\n",
       "1977      2  0.460074       analysis\n",
       "1977      4  0.230037       analysis\n",
       "1977      5  0.076679       analysis\n",
       "1773      2  0.686500     appreciate\n",
       "5207      3  0.279116       approves\n",
       "5207      5  0.558233       approves\n",
       "...     ...       ...            ...\n",
       "297       1  0.680225          water\n",
       "297       2  0.226742          water\n",
       "2876      1  0.209284       whatsapp\n",
       "2876      2  0.104642       whatsapp\n",
       "2876      3  0.627853       whatsapp\n",
       "2876      5  0.104642       whatsapp\n",
       "3301      3  0.680053           wing\n",
       "2593      2  0.685116       wondrous\n",
       "2312      5  0.702610           wood\n",
       "2364      1  0.152633          world\n",
       "2364      2  0.610532          world\n",
       "2364      3  0.076317          world\n",
       "2364      4  0.076317          world\n",
       "2364      5  0.076317          world\n",
       "801       1  0.814927      worldwide\n",
       "1016      2  0.093263          worth\n",
       "1016      3  0.279789          worth\n",
       "1016      4  0.559578          worth\n",
       "1016      5  0.093263          worth\n",
       "3579      2  0.686537            xbt\n",
       "2857      1  0.175822            xrp\n",
       "2857      2  0.205125            xrp\n",
       "2857      3  0.322340            xrp\n",
       "2857      4  0.263732            xrp\n",
       "2857      5  0.029304            xrp\n",
       "2915      2  0.280124           york\n",
       "2915      5  0.700310           york\n",
       "5096      3  0.908267          zebra\n",
       "1784      2  0.914781           zeta\n",
       "2872      2  0.685527         zurich\n",
       "\n",
       "[764 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 2, 1, 3, 4])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   "source": [
    "pyLDAvis.gensim.prepare(lmlist[4], corpora, corpdict)"
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