test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['stem']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from __future__ import unicode_literals\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import stem,pos_tag\n",
    "import re\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "%pylab inline\n",
    "from textblob import TextBlob\n",
    "from sklearn.metrics import silhouette_score,confusion_matrix,accuracy_score,roc_curve\n",
    "import pandas as pd\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import label_binarize,Normalizer\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "import ftfy\n",
    "import pyLDAvis.sklearn\n",
    "pyLDAvis.enable_notebook()\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import statsmodels.api as sm\n",
    "from seaborn import lmplot\n",
    "import os  # for os.path.basename\n",
    "import matplotlib as mpl\n",
    "from sklearn.manifold import MDS,TSNE\n",
    "%pylab inline\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import sklearn\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n",
    "from sklearn.utils.extmath import density\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer\n",
    "from sklearn.decomposition import TruncatedSVD,PCA,NMF\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.decomposition.online_lda import LatentDirichletAllocation\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('dataset/test.csv',encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[['Business Description']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawdata=data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/scipy/sparse/sparsetools.py:21: DeprecationWarning: `scipy.sparse.sparsetools` is deprecated!\n",
      "scipy.sparse.sparsetools is a private module for scipy.sparse, and should not be used.\n",
      "  _deprecated()\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "\n",
    "# Gensim\n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# spacy for lemmatization\n",
    "import spacy\n",
    "\n",
    "# Plotting tools\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim  # don't skip this\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       024 Pharma, Inc. provides healthcare products ...\n",
       "1       1-800-Flowers.com, Inc., together with its sub...\n",
       "2       12 Retech Corporation, through its subsidiarie...\n",
       "3       1347 Property Insurance Holdings, Inc., throug...\n",
       "4       1847 Holdings LLC, through its subsidiaries, p...\n",
       "5       1867 Western Financial Corporation, through it...\n",
       "6       1mage Software Inc operates in the technology ...\n",
       "7       1PM Industries, Inc. provides consulting servi...\n",
       "8       1st Capital Bank provides various banking prod...\n",
       "9       1st Colonial Bancorp, Inc. operates as the ban...\n",
       "10      1st Constitution Bancorp operates as the bank ...\n",
       "11      1st NRG Corp. explores, develops, produces, an...\n",
       "12      1st Prestige Wealth Management provides wealth...\n",
       "13      1st Source Corporation operates as the bank ho...\n",
       "14      1ST SUMMIT BANCORP of Johnstown, Inc. operates...\n",
       "15      RM Investors, Inc. supplies fruits and vegetab...\n",
       "16      2050 Motors, Inc., a development stage company...\n",
       "17      22nd Century Group, Inc., a plant biotechnolog...\n",
       "18      24/7 Kid Doc, Inc. provides pediatric services...\n",
       "19      24Holdings Inc. does not have significant oper...\n",
       "20      2U, Inc. operates as an education technology c...\n",
       "21      30DC, Inc. provides digital media solutions. T...\n",
       "22      3D Entertainment Holdings, Inc. operates as a ...\n",
       "23      3D MakerJet, Inc. develops, imports, and sells...\n",
       "24      3D Pioneer Systems Inc. engages in 3D printing...\n",
       "25      3D Systems Corporation, through its subsidiari...\n",
       "26      3DShopping.com doing business as O2 Essential ...\n",
       "27      3DX Industries, Inc. manufactures and sells co...\n",
       "28      3M Company operates as a diversified technolog...\n",
       "29      3Pea International, Inc. provides prepaid card...\n",
       "                              ...                        \n",
       "8395    Viropro Inc., through its subsidiaries, engage...\n",
       "8396    Virtu Financial, Inc., together with its subsi...\n",
       "8397    Viskase Companies, Inc., together with its sub...\n",
       "8398    Vitalibis, Inc. focuses on the development, sa...\n",
       "8399    VMware, Inc. provides compute, cloud, mobility...\n",
       "8400    vTv Therapeutics Inc., a clinical-stage biopha...\n",
       "8401    Wake Forest Bancshares, Inc. operates as the h...\n",
       "8402    WCF Bancorp, Inc. operates as the savings and ...\n",
       "8403     As of December 29, 2017,  operates as a subsi...\n",
       "8404    Western Gas Equity Partners, LP, together with...\n",
       "8405    Western Gas Partners, LP acquires, develops, o...\n",
       "8406    Western Sierra Mining Corp. engages in the acq...\n",
       "8407    Westlake Chemical Corporation manufactures and...\n",
       "8408    Westmoreland Resource Partners, LP, together w...\n",
       "8409    WestMountain Distressed Debt, Inc. focuses on ...\n",
       "8410    Whiting USA Trust II holds a term net profits ...\n",
       "8411    WideOpenWest, Inc. operates as a cable operato...\n",
       "8412    Wikisoft Corp. provides online gaming solution...\n",
       "8413    William Penn Bancorp, Inc. operates as the hol...\n",
       "8414    Williams Partners L.P. operates as an energy i...\n",
       "8415    Williamsville Sears Management, Inc. does not ...\n",
       "8416    Wins Finance Holdings Inc., through its subsid...\n",
       "8417    Wrap Technologies, Inc. develops security prod...\n",
       "8418    XcelPlus International, Inc. distributes chemi...\n",
       "8419    Yacht Finders, Inc. does not have significant ...\n",
       "8420    Yappn Corp. provides language translation prod...\n",
       "8421    YaSheng Group, through its subsidiaries, desig...\n",
       "8422    Zenergy Brands, Inc., an energy and technology...\n",
       "8423    Zulu-Tek Inc provides Internet advertising ser...\n",
       "8424    Zurvita Holdings, Inc. markets health and well...\n",
       "Name: Business Description, Length: 8425, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawdata['Business Description']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['Inc.', 'Company', 'Corporation','inc','inc.'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'024 Pharma, Inc. provides healthcare products worldwide. Its products include vitamin and mineral supplements; stress release, joint, heart health, and weight-loss products; and skin care, hair, and anti-aging products. The company was formerly known as B Green Innovations, Inc. and changed its name to 024 Pharma, Inc. in October 2016. 024 Pharma, Inc. was incorporated in 2004 and is based in West Palm Beach, Florida.']\n"
     ]
    }
   ],
   "source": [
    "# Convert to list\n",
    "data = rawdata['Business Description'].values.tolist()\n",
    "\n",
    "# Remove Emails\n",
    "data = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in data]\n",
    "\n",
    "# Remove new line characters\n",
    "data = [re.sub('\\s+', ' ', sent) for sent in data]\n",
    "\n",
    "# Remove distracting single quotes\n",
    "data = [re.sub(\"\\'\", \"\", sent) for sent in data]\n",
    "\n",
    "\n",
    "\n",
    "pprint(data[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "#         sentence = list(sentence)\n",
    "#         sentence.append([x.encode('utf-8') for x in sentence])\n",
    "        sentence = sentence.encode('utf-8')\n",
    "        #print(sentence.encode('utf-8'))\n",
    "        yield((gensim.utils.simple_preprocess(str(sentence), deacc=True)))\n",
    " # deacc=True removes punctuations\n",
    "\n",
    "data_words=list(sent_to_words(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[u'pharma', u'inc', u'provides', u'healthcare', u'products', u'worldwide', u'its', u'products', u'include', u'vitamin', u'and', u'mineral', u'supplements', u'stress', u'release', u'joint', u'heart', u'health', u'and', u'weight_loss', u'products', u'and', u'skin', u'care', u'hair', u'and', u'anti_aging', u'products', u'the', u'company', u'was', u'formerly_known', u'as', u'green', u'innovations', u'inc', u'and', u'changed', u'its', u'name', u'to', u'pharma', u'inc', u'in', u'october', u'pharma', u'inc', u'was', u'incorporated', u'in', u'and', u'is', u'based', u'in', u'west_palm_beach_florida']\n"
     ]
    }
   ],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "\n",
    "# Faster way to get a sentence clubbed as a trigram/bigram\n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "# See trigram example\n",
    "print(trigram_mod[bigram_mod[data_words[0]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions for stopwords, bigrams, trigrams and lemmatization\n",
    "def remove_stopwords(texts):\n",
    "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "\n",
    "def make_bigrams(texts):\n",
    "    return [bigram_mod[doc] for doc in texts]\n",
    "\n",
    "def make_trigrams(texts):\n",
    "    return [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    return texts_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[u'pharma', u'provide', u'healthcare', u'product', u'worldwide', u'product', u'include', u'vitamin', u'mineral', u'supplement', u'stress', u'release', u'joint', u'heart', u'health', u'weight_loss', u'product', u'skin', u'care', u'hair', u'anti_aging', u'product', u'company', u'green', u'innovation', u'change', u'name', u'pharma', u'october', u'pharma', u'incorporate', u'base', u'west_palm', u'beach_florida']]\n"
     ]
    }
   ],
   "source": [
    "# Remove Stop Words\n",
    "data_words_nostops = remove_stopwords(data_words)\n",
    "\n",
    "# Form Bigrams\n",
    "data_words_bigrams = make_bigrams(data_words_nostops)\n",
    "\n",
    "\n",
    "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
    "# python3 -m spacy download en\n",
    "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "\n",
    "\n",
    "# Do lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "print(data_lemmatized[:1])\n",
    "\n",
    "# for dg in data_words_bigrams:\n",
    "#     dg=[x.decode('utf-8') for x in dg]\n",
    "#     dw_bigram.append(dg)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 3), (19, 4), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1), (27, 1), (28, 1)]]\n"
     ]
    }
   ],
   "source": [
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Create Corpus\n",
    "texts = data_lemmatized\n",
    "\n",
    "# Term Document Frequency\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "# View\n",
    "print(corpus[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(u'anti_aging', 1),\n",
       "  (u'base', 1),\n",
       "  (u'beach_florida', 1),\n",
       "  (u'care', 1),\n",
       "  (u'change', 1),\n",
       "  (u'company', 1),\n",
       "  (u'green', 1),\n",
       "  (u'hair', 1),\n",
       "  (u'health', 1),\n",
       "  (u'healthcare', 1),\n",
       "  (u'heart', 1),\n",
       "  (u'include', 1),\n",
       "  (u'incorporate', 1),\n",
       "  (u'innovation', 1),\n",
       "  (u'joint', 1),\n",
       "  (u'mineral', 1),\n",
       "  (u'name', 1),\n",
       "  (u'october', 1),\n",
       "  (u'pharma', 3),\n",
       "  (u'product', 4),\n",
       "  (u'provide', 1),\n",
       "  (u'release', 1),\n",
       "  (u'skin', 1),\n",
       "  (u'stress', 1),\n",
       "  (u'supplement', 1),\n",
       "  (u'vitamin', 1),\n",
       "  (u'weight_loss', 1),\n",
       "  (u'west_palm', 1),\n",
       "  (u'worldwide', 1)]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Human readable format of corpus (term-frequency)\n",
    "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=32, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(23,\n",
      "  u'0.000*\"wengen\" + 0.000*\"lawson\" + 0.000*\"andean\" + 0.000*\"emeaa\" + 0.000*\"iberian\" + 0.000*\"laureate\" + 0.000*\"sylvan\" + 0.000*\"mmwave\" + 0.000*\"laxai\" + 0.000*\"plainfield\"'),\n",
      " (8,\n",
      "  u'0.006*\"fraud_prevention\" + 0.000*\"sylvan\" + 0.000*\"uhd\" + 0.000*\"andean\" + 0.000*\"emeaa\" + 0.000*\"iberian\" + 0.000*\"laureate\" + 0.000*\"lawson\" + 0.000*\"wengen\" + 0.000*\"laxai\"'),\n",
      " (31,\n",
      "  u'0.142*\"cancer\" + 0.090*\"human\" + 0.051*\"blood\" + 0.045*\"medicine\" + 0.038*\"anti\" + 0.032*\"surgery\" + 0.031*\"heart\" + 0.030*\"prevent\" + 0.022*\"adult\" + 0.020*\"biotech\"'),\n",
      " (7,\n",
      "  u'0.059*\"module\" + 0.052*\"africa\" + 0.043*\"middle_east\" + 0.042*\"asia_pacific\" + 0.041*\"series\" + 0.038*\"processor\" + 0.037*\"europe\" + 0.035*\"signal\" + 0.034*\"original_equipment\" + 0.031*\"wire\"'),\n",
      " (20,\n",
      "  u'0.158*\"nevada\" + 0.092*\"usa\" + 0.090*\"las_vegas\" + 0.074*\"transfer\" + 0.045*\"tennessee\" + 0.038*\"north_carolina\" + 0.030*\"top\" + 0.025*\"iron\" + 0.019*\"result\" + 0.015*\"sound\"'),\n",
      " (2,\n",
      "  u'0.079*\"assembly\" + 0.068*\"specific\" + 0.062*\"advanced\" + 0.056*\"orally_administered\" + 0.040*\"bioscience\" + 0.038*\"limited\" + 0.026*\"conventional\" + 0.024*\"scientific\" + 0.024*\"antibody\" + 0.020*\"certain\"'),\n",
      " (6,\n",
      "  u'0.251*\"llc\" + 0.062*\"dallas_texas\" + 0.052*\"cannabis\" + 0.040*\"tech\" + 0.035*\"recovery\" + 0.034*\"room\" + 0.033*\"legal\" + 0.025*\"educational\" + 0.024*\"staffing\" + 0.023*\"supermarket\"'),\n",
      " (10,\n",
      "  u'0.302*\"resource\" + 0.040*\"item\" + 0.040*\"mechanical\" + 0.033*\"precision\" + 0.029*\"gift\" + 0.028*\"hot\" + 0.027*\"rock\" + 0.027*\"laser\" + 0.022*\"fresh\" + 0.018*\"cut\"'),\n",
      " (24,\n",
      "  u'0.057*\"pre\" + 0.053*\"liability\" + 0.052*\"south_carolina\" + 0.048*\"tablet\" + 0.043*\"class\" + 0.042*\"protect\" + 0.036*\"surgical\" + 0.036*\"identify\" + 0.035*\"pain\" + 0.030*\"loss\"'),\n",
      " (19,\n",
      "  u'0.213*\"asset\" + 0.113*\"capital\" + 0.077*\"equity\" + 0.069*\"portfolio\" + 0.055*\"debt\" + 0.048*\"real_estate\" + 0.038*\"invest\" + 0.034*\"investment\" + 0.034*\"security\" + 0.031*\"institutional\"'),\n",
      " (18,\n",
      "  u'0.099*\"health\" + 0.089*\"medical\" + 0.074*\"care\" + 0.051*\"healthcare\" + 0.028*\"risk\" + 0.027*\"hospital\" + 0.019*\"patient\" + 0.018*\"contractor\" + 0.017*\"universal\" + 0.015*\"long_term\"'),\n",
      " (27,\n",
      "  u'0.165*\"energy\" + 0.115*\"oil\" + 0.098*\"gas\" + 0.067*\"production\" + 0.057*\"texas\" + 0.049*\"water\" + 0.045*\"exploration\" + 0.025*\"petroleum\" + 0.024*\"produce\" + 0.020*\"lease\"'),\n",
      " (9,\n",
      "  u'0.209*\"holding\" + 0.061*\"mineral\" + 0.055*\"intend\" + 0.049*\"business\" + 0.044*\"ltd\" + 0.039*\"significant_operation\" + 0.032*\"limit\" + 0.029*\"september\" + 0.029*\"combination\" + 0.025*\"strategic\"'),\n",
      " (22,\n",
      "  u'0.118*\"network\" + 0.071*\"security\" + 0.039*\"program\" + 0.032*\"internet\" + 0.025*\"user\" + 0.024*\"travel\" + 0.024*\"national\" + 0.021*\"february\" + 0.020*\"time\" + 0.018*\"build\"'),\n",
      " (25,\n",
      "  u'0.047*\"industrial\" + 0.044*\"product\" + 0.041*\"equipment\" + 0.040*\"use\" + 0.038*\"manufacture\" + 0.029*\"system\" + 0.027*\"control\" + 0.025*\"industry\" + 0.025*\"segment\" + 0.024*\"component\"'),\n",
      " (17,\n",
      "  u'0.073*\"solution\" + 0.060*\"system\" + 0.042*\"technology\" + 0.035*\"software\" + 0.030*\"application\" + 0.029*\"device\" + 0.028*\"datum\" + 0.027*\"provide\" + 0.024*\"mobile\" + 0.023*\"offer\"'),\n",
      " (30,\n",
      "  u'0.283*\"service\" + 0.069*\"segment\" + 0.063*\"provide\" + 0.050*\"management\" + 0.034*\"customer\" + 0.026*\"offer\" + 0.024*\"manage\" + 0.020*\"storage\" + 0.017*\"transportation\" + 0.015*\"partner\"'),\n",
      " (5,\n",
      "  u'0.221*\"product\" + 0.048*\"sell\" + 0.047*\"market\" + 0.046*\"brand\" + 0.035*\"retail\" + 0.027*\"store\" + 0.024*\"offer\" + 0.023*\"home\" + 0.023*\"manufacture\" + 0.022*\"consumer\"'),\n",
      " (11,\n",
      "  u'0.092*\"name\" + 0.088*\"company\" + 0.077*\"base\" + 0.073*\"change\" + 0.061*\"formerly_known\" + 0.056*\"corp\" + 0.055*\"group\" + 0.051*\"development\" + 0.034*\"incorporate\" + 0.030*\"focus\"'),\n",
      " (16,\n",
      "  u'0.142*\"company\" + 0.049*\"operate\" + 0.044*\"found\" + 0.039*\"subsidiary\" + 0.038*\"state\" + 0.035*\"also\" + 0.031*\"include\" + 0.027*\"united\" + 0.026*\"base\" + 0.023*\"well\"')]\n"
     ]
    }
   ],
   "source": [
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(u'\\nPerplexity: ', -15.462186059669284)\n",
      "(u'\\nCoherence Score: ', 0.4757341931159688)\n"
     ]
    }
   ],
   "source": [
    "# Compute Perplexity\n",
    "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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_el17331210548364325938813174\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el17331210548364325938813174_data = {\"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [17, 30, 18, 6, 31, 12, 26, 15, 23, 27, 28, 19, 13, 5, 29, 1, 10, 22, 16, 2, 4, 20, 14, 32, 11, 8, 25, 7, 3, 21, 9, 24], \"token.table\": {\"Topic\": [2, 5, 4, 2, 8, 1, 6, 2, 6, 11, 17, 19, 15, 13, 6, 11, 12, 12, 20, 1, 2, 3, 7, 8, 9, 2, 3, 4, 5, 14, 24, 2, 14, 24, 9, 29, 18, 15, 14, 7, 17, 16, 26, 10, 10, 14, 16, 27, 7, 10, 10, 21, 21, 3, 15, 29, 1, 2, 3, 5, 7, 8, 29, 1, 26, 5, 3, 4, 13, 24, 29, 16, 1, 6, 3, 7, 13, 1, 19, 5, 1, 6, 4, 11, 4, 26, 29, 29, 2, 22, 17, 12, 9, 9, 2, 30, 3, 6, 26, 14, 17, 24, 2, 3, 7, 3, 2, 7, 14, 7, 15, 10, 11, 20, 28, 28, 2, 2, 2, 4, 1, 2, 3, 6, 11, 9, 18, 29, 13, 11, 5, 27, 13, 12, 29, 24, 20, 18, 13, 24, 13, 6, 12, 10, 2, 10, 2, 4, 15, 4, 9, 2, 3, 9, 1, 7, 10, 1, 2, 3, 14, 15, 17, 18, 5, 5, 25, 1, 2, 3, 6, 8, 8, 15, 24, 8, 25, 26, 20, 28, 9, 22, 2, 5, 10, 7, 11, 2, 3, 18, 24, 24, 12, 13, 9, 2, 14, 16, 30, 24, 1, 1, 2, 4, 7, 9, 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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "16     21.896847        1       1  0.329734 -0.189386\n",
       "29     10.753078        1       2  0.304887  0.022869\n",
       "17      8.421264        1       3  0.257593  0.102253\n",
       "5       7.965384        1       4  0.228304  0.163461\n",
       "30      7.196625        1       5  0.239647  0.160001\n",
       "11      6.169342        1       6  0.186049 -0.347020\n",
       "25      5.861608        1       7  0.228921  0.178033\n",
       "14      3.059638        1       8  0.097034 -0.223258\n",
       "22      2.851379        1       9  0.026590 -0.005093\n",
       "26      2.575371        1      10  0.059330  0.114296\n",
       "27      2.397911        1      11  0.051096 -0.065065\n",
       "18      2.354675        1      12  0.040816 -0.000090\n",
       "12      2.054959        1      13  0.046488  0.091941\n",
       "4       1.971282        1      14  0.069561 -0.013498\n",
       "28      1.689789        1      15  0.029352  0.034969\n",
       "0       1.645554        1      16 -0.053606 -0.038530\n",
       "9       1.526668        1      17 -0.016621 -0.036355\n",
       "21      1.278948        1      18 -0.043960 -0.000986\n",
       "15      1.232783        1      19 -0.048021 -0.016647\n",
       "1       1.143013        1      20 -0.089941 -0.001081\n",
       "3       0.802420        1      21 -0.133278 -0.001578\n",
       "19      0.747354        1      22 -0.090776 -0.007097\n",
       "13      0.675058        1      23 -0.119180  0.011496\n",
       "31      0.656282        1      24 -0.160924  0.007340\n",
       "10      0.595534        1      25 -0.156585  0.001217\n",
       "7       0.579698        1      26 -0.150308  0.028785\n",
       "24      0.575329        1      27 -0.162068  0.003112\n",
       "6       0.470494        1      28 -0.148022  0.000148\n",
       "2       0.451390        1      29 -0.177148  0.004245\n",
       "20      0.387662        1      30 -0.173935  0.003475\n",
       "8       0.007586        1      31 -0.235378  0.009010\n",
       "23      0.005071        1      32 -0.235652  0.009033, topic_info=      Category          Freq            Term         Total  loglift  logprob\n",
       "term                                                                        \n",
       "193    Default  18177.000000         service  18177.000000  30.0000  30.0000\n",
       "6969   Default  28176.000000         company  28176.000000  29.0000  29.0000\n",
       "2801   Default  16403.000000         product  16403.000000  28.0000  28.0000\n",
       "901    Default   4301.000000      technology   4301.000000  27.0000  27.0000\n",
       "27302  Default   5993.000000            loan   5993.000000  26.0000  26.0000\n",
       "12109  Default   7803.000000            base   7803.000000  25.0000  25.0000\n",
       "24448  Default   4039.000000            name   4039.000000  24.0000  24.0000\n",
       "19155  Default   5853.000000          system   5853.000000  23.0000  23.0000\n",
       "22820  Default   2804.000000          energy   2804.000000  22.0000  22.0000\n",
       "13023  Default   4960.000000        solution   4960.000000  21.0000  21.0000\n",
       "4379   Default   8668.000000         operate   8668.000000  20.0000  20.0000\n",
       "13675  Default  10260.000000         provide  10260.000000  19.0000  19.0000\n",
       "28566  Default   2263.000000         holding   2263.000000  18.0000  18.0000\n",
       "29020  Default   5262.000000        business   5262.000000  17.0000  17.0000\n",
       "19203  Default   4746.000000         segment   4746.000000  16.0000  16.0000\n",
       "11388  Default   3219.000000          change   3219.000000  15.0000  15.0000\n",
       "29978  Default   5946.000000           state   5946.000000  14.0000  14.0000\n",
       "21246  Default   4568.000000      management   4568.000000  13.0000  13.0000\n",
       "28232  Default   2381.000000         network   2381.000000  12.0000  12.0000\n",
       "5813   Default   6385.000000      subsidiary   6385.000000  11.0000  11.0000\n",
       "21100  Default   4321.000000      commercial   4321.000000  10.0000  10.0000\n",
       "18549  Default   7996.000000           found   7996.000000   9.0000   9.0000\n",
       "19827  Default   2100.000000        property   2100.000000   8.0000   8.0000\n",
       "3036   Default   3851.000000         develop   3851.000000   7.0000   7.0000\n",
       "11770  Default   2169.000000       treatment   2169.000000   6.0000   6.0000\n",
       "28450  Default   2671.000000  formerly_known   2671.000000   5.0000   5.0000\n",
       "29990  Default   2898.000000           group   2898.000000   4.0000   4.0000\n",
       "31471  Default   1952.000000             oil   1952.000000   3.0000   3.0000\n",
       "23286  Default   3154.000000         account   3154.000000   2.0000   2.0000\n",
       "8751   Default   2389.000000       financial   2389.000000   1.0000   1.0000\n",
       "...        ...           ...             ...           ...      ...      ...\n",
       "11839  Topic32      0.001042        undercar      1.231306   2.8144 -10.4505\n",
       "11524  Topic32      0.001042              ed      1.231306   2.8144 -10.4505\n",
       "11510  Topic32      0.001042            half      1.231306   2.8144 -10.4505\n",
       "11511  Topic32      0.001042         ritchie      1.231306   2.8144 -10.4505\n",
       "11512  Topic32      0.001042        powerbar      1.231306   2.8144 -10.4505\n",
       "11513  Topic32      0.001042       sweetener      1.231306   2.8144 -10.4505\n",
       "11514  Topic32      0.001042            halo      1.231306   2.8144 -10.4505\n",
       "11515  Topic32      0.001042      marineland      1.231306   2.8144 -10.4505\n",
       "11516  Topic32      0.001042        arcalyst      1.231306   2.8144 -10.4505\n",
       "11517  Topic32      0.001042      bearingarm      1.231306   2.8144 -10.4505\n",
       "11518  Topic32      0.001042          phonic      1.231306   2.8144 -10.4505\n",
       "11519  Topic32      0.001042        concerta      1.231306   2.8144 -10.4505\n",
       "11520  Topic32      0.001042        herdsoft      1.231306   2.8144 -10.4505\n",
       "11521  Topic32      0.001042              em      1.231306   2.8144 -10.4505\n",
       "11522  Topic32      0.001042   significantly      1.231306   2.8144 -10.4505\n",
       "11523  Topic32      0.001042      plinabulin      1.231306   2.8144 -10.4505\n",
       "11525  Topic32      0.001042              eg      1.231306   2.8144 -10.4505\n",
       "11508  Topic32      0.001042             aer      1.231306   2.8144 -10.4505\n",
       "11526  Topic32      0.001042       accucheck      1.231306   2.8144 -10.4505\n",
       "11527  Topic32      0.001042              ec      1.231306   2.8144 -10.4505\n",
       "11528  Topic32      0.001042              eb      1.231306   2.8144 -10.4505\n",
       "11529  Topic32      0.001042           goose      1.231306   2.8144 -10.4505\n",
       "11530  Topic32      0.001042           chime      1.231306   2.8144 -10.4505\n",
       "11531  Topic32      0.001042              et      1.231306   2.8144 -10.4505\n",
       "11532  Topic32      0.001042           maisy      1.231306   2.8144 -10.4505\n",
       "11533  Topic32      0.001042              ev      1.231306   2.8144 -10.4505\n",
       "11534  Topic32      0.001042              ep      1.231306   2.8144 -10.4505\n",
       "11535  Topic32      0.001042   rhinosinusiti      1.231306   2.8144 -10.4505\n",
       "11536  Topic32      0.001042           space    312.668304  -2.7227 -10.4505\n",
       "11537  Topic32      0.001042           nucor      1.231306   2.8144 -10.4505\n",
       "\n",
       "[1401 rows x 6 columns], token_table=       Topic      Freq                  Term\n",
       "term                                        \n",
       "9829       2  0.996629                accept\n",
       "5907       5  0.998880                access\n",
       "26176      4  0.999035             accessory\n",
       "23286      2  0.999694               account\n",
       "9632       8  0.972165                   ach\n",
       "6080       1  0.244416               acquire\n",
       "6080       6  0.755246               acquire\n",
       "26114      2  0.059605           acquisition\n",
       "26114      6  0.549213           acquisition\n",
       "26114     11  0.107501           acquisition\n",
       "26114     17  0.282057           acquisition\n",
       "31071     19  0.991052                  acre\n",
       "13013     15  0.941190            activation\n",
       "30231     13  0.995138                active\n",
       "29816      6  0.817436              activity\n",
       "29816     11  0.139755              activity\n",
       "29816     12  0.042190              activity\n",
       "7422      12  0.991225                 acute\n",
       "9677      20  0.982882                    ad\n",
       "12802      1  0.603273              addition\n",
       "12802      2  0.193341              addition\n",
       "12802      3  0.169275              addition\n",
       "12802      7  0.012237              addition\n",
       "12802      8  0.021618              addition\n",
       "4192       9  0.994669            additional\n",
       "4236       2  0.121866          additionally\n",
       "4236       3  0.365599          additionally\n",
       "4236       4  0.068881          additionally\n",
       "4236       5  0.296718          additionally\n",
       "4236      14  0.137762          additionally\n",
       "...      ...       ...                   ...\n",
       "21921     15  0.997758               website\n",
       "7678      20  0.992907                weight\n",
       "21427      1  0.568569                  well\n",
       "21427      2  0.142142                  well\n",
       "21427      3  0.000623                  well\n",
       "21427      4  0.127041                  well\n",
       "21427      5  0.115364                  well\n",
       "21427      7  0.046083                  well\n",
       "10088     18  0.940958             west_palm\n",
       "15378      1  0.998531               western\n",
       "4093      10  0.991392                 wheel\n",
       "10071     10  0.993104                 whole\n",
       "7482       4  0.960753             wholesale\n",
       "7482       5  0.038430             wholesale\n",
       "32807     13  0.993321            wholesaler\n",
       "34071      8  0.976579            wholly_own\n",
       "8714      16  0.994340                  wind\n",
       "11496      7  0.993116                window\n",
       "8715       4  0.993541                  wine\n",
       "27834      2  0.072326                  wire\n",
       "27834     26  0.918543                  wire\n",
       "8819       2  0.992406         wire_transfer\n",
       "16496     13  0.994321             wisconsin\n",
       "16321     17  0.990007                 woman\n",
       "33191     10  0.992333                  wood\n",
       "30711      5  0.996360                  work\n",
       "24005     12  0.992330  workers_compensation\n",
       "15554      8  0.979067                 wound\n",
       "22581     19  0.993610                  wyom\n",
       "30671     19  0.932344          zero_balance\n",
       "\n",
       "[1518 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[17, 30, 18, 6, 31, 12, 26, 15, 23, 27, 28, 19, 13, 5, 29, 1, 10, 22, 16, 2, 4, 20, 14, 32, 11, 8, 25, 7, 3, 21, 9, 24])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mallet_path = '/Users/onion8/Downloads/utilities/mallet-2.0.8/bin/mallet' # update this path\n",
    "ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=32, id2word=id2word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(24,\n",
      "  [(u'product', 0.06374388718298647),\n",
      "   (u'company', 0.025929716820197884),\n",
      "   (u'base', 0.019959058341862845),\n",
      "   (u'industry', 0.017002160809735017),\n",
      "   (u'market', 0.015182531559194815),\n",
      "   (u'produce', 0.013988399863527807),\n",
      "   (u'natural', 0.013874673035369043),\n",
      "   (u'cannabis', 0.011770726714431934),\n",
      "   (u'sell', 0.011258955987717503),\n",
      "   (u'green', 0.008984419424542249)]),\n",
      " (4,\n",
      "  [(u'product', 0.29394907968698336),\n",
      "   (u'company', 0.07803372644108894),\n",
      "   (u'include', 0.06728755648627796),\n",
      "   (u'market', 0.05406150115727984),\n",
      "   (u'comprise', 0.02722363055218781),\n",
      "   (u'offer', 0.025956133583158823),\n",
      "   (u'sell', 0.02551526507219222),\n",
      "   (u'found', 0.018902237407693156),\n",
      "   (u'distributor', 0.018681803152209853),\n",
      "   (u'headquartered', 0.017910283258018297)]),\n",
      " (21,\n",
      "  [(u'service', 0.11294416243654823),\n",
      "   (u'financial', 0.060279187817258884),\n",
      "   (u'management', 0.057695794053662076),\n",
      "   (u'investment', 0.04110768672951414),\n",
      "   (u'provide', 0.035668963016678754),\n",
      "   (u'offer', 0.0260605511240029),\n",
      "   (u'corporate', 0.019307469180565626),\n",
      "   (u'advisory', 0.01704133430021755),\n",
      "   (u'brokerage', 0.014548585931834663),\n",
      "   (u'private', 0.014321972443799855)]),\n",
      " (30,\n",
      "  [(u'product', 0.039542352468887994),\n",
      "   (u'system', 0.03717382577278201),\n",
      "   (u'design', 0.029506222400642313),\n",
      "   (u'electronic', 0.028542753914090727),\n",
      "   (u'application', 0.023484544359694902),\n",
      "   (u'control', 0.021959052589321557),\n",
      "   (u'manufacturer', 0.02071457246085909),\n",
      "   (u'device', 0.02067442794058611),\n",
      "   (u'manufacture', 0.017784022480931354),\n",
      "   (u'market', 0.01690084303492573)]),\n",
      " (29,\n",
      "  [(u'technology', 0.15931444725837798),\n",
      "   (u'company', 0.12379998822074327),\n",
      "   (u'development', 0.09617763119147182),\n",
      "   (u'base', 0.0869898109429295),\n",
      "   (u'develop', 0.07797867954532069),\n",
      "   (u'focus', 0.04228753165675246),\n",
      "   (u'found', 0.027504564461982448),\n",
      "   (u'proprietary', 0.019906943871841686),\n",
      "   (u'engage', 0.019023499617174155),\n",
      "   (u'acquisition', 0.015018552329348019)]),\n",
      " (11,\n",
      "  [(u'system', 0.04973455086005521),\n",
      "   (u'equipment', 0.034954342747929495),\n",
      "   (u'vehicle', 0.032406031004459546),\n",
      "   (u'manufacture', 0.01864514758972181),\n",
      "   (u'component', 0.015629645359949033),\n",
      "   (u'part', 0.015502229772775536),\n",
      "   (u'control', 0.014865151836908048),\n",
      "   (u'automotive', 0.013760883414737737),\n",
      "   (u'engine', 0.012953918029305586),\n",
      "   (u'aircraft', 0.0121894245062646)]),\n",
      " (26,\n",
      "  [(u'corporation', 0.1901700414588716),\n",
      "   (u'company', 0.1308057441567025),\n",
      "   (u'change', 0.10514931202307276),\n",
      "   (u'formerly_known', 0.08856576338400529),\n",
      "   (u'corp', 0.07925253860481885),\n",
      "   (u'base', 0.07708946704320134),\n",
      "   (u'found', 0.03587093673015682),\n",
      "   (u'incorporate', 0.03112419635882954),\n",
      "   (u'hold', 0.013759538544733522),\n",
      "   (u'subsidiary', 0.01369945322357748)]),\n",
      " (5,\n",
      "  [(u'operate', 0.08890526186529134),\n",
      "   (u'company', 0.0367163995824891),\n",
      "   (u'state', 0.028304782955731564),\n",
      "   (u'restaurant', 0.026708417756492908),\n",
      "   (u'united', 0.021428132866703507),\n",
      "   (u'travel', 0.01706882789955179),\n",
      "   (u'hotel', 0.01627064529993246),\n",
      "   (u'subsidiary', 0.015533861361822313),\n",
      "   (u'approximately', 0.015349665377294775),\n",
      "   (u'location', 0.013446306870510223)]),\n",
      " (19,\n",
      "  [(u'system', 0.0477739377922342),\n",
      "   (u'medical', 0.025360845700345597),\n",
      "   (u'device', 0.02475096564342346),\n",
      "   (u'diagnostic', 0.020024395202276887),\n",
      "   (u'research', 0.019312868469201057),\n",
      "   (u'offer', 0.01819475503151047),\n",
      "   (u'testing', 0.016161821508436675),\n",
      "   (u'test', 0.014637121366131328),\n",
      "   (u'develop', 0.014586298028054483),\n",
      "   (u'provide', 0.013112421223825981)]),\n",
      " (15,\n",
      "  [(u'company', 0.23961701069385724),\n",
      "   (u'california', 0.09437950758517781),\n",
      "   (u'found', 0.07560308380999751),\n",
      "   (u'state', 0.07529221586669983),\n",
      "   (u'headquartered', 0.0541531957224571),\n",
      "   (u'united', 0.049987565282268095),\n",
      "   (u'home', 0.049054961452375034),\n",
      "   (u'include', 0.035936334245212635),\n",
      "   (u'unite', 0.020579457846306888),\n",
      "   (u'headquarter', 0.020206416314349666)])]\n",
      "(u'\\nCoherence Score: ', 0.5645784412233072)\n"
     ]
    }
   ],
   "source": [
    "pprint(ldamallet.show_topics(formatted=False))\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_ldamallet = CoherenceModel(model=ldamallet, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_ldamallet = coherence_model_ldamallet.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_ldamallet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    \"\"\"\n",
    "    Compute c_v coherence for various number of topics\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    texts : List of input texts\n",
    "    limit : Max num of topics\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    model_list : List of LDA topic models\n",
    "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for num_topics in range(start, limit, step):\n",
    "        model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=num_topics, id2word=id2word)\n",
    "        model_list.append(model)\n",
    "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_values.append(coherencemodel.get_coherence())\n",
    "\n",
    "    return model_list, coherence_values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can take a long time to run.\n",
    "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=2, limit=40, step=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c3c13d9d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "limit=40; start=2; step=6;\n",
    "x = range(start, limit, step)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend((\"coherence_values\"), loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(u'Num Topics =', 2, u' has Coherence Value of', 0.3947)\n",
      "(u'Num Topics =', 8, u' has Coherence Value of', 0.544)\n",
      "(u'Num Topics =', 14, u' has Coherence Value of', 0.5372)\n",
      "(u'Num Topics =', 20, u' has Coherence Value of', 0.5542)\n",
      "(u'Num Topics =', 26, u' has Coherence Value of', 0.5643)\n",
      "(u'Num Topics =', 32, u' has Coherence Value of', 0.5665)\n",
      "(u'Num Topics =', 38, u' has Coherence Value of', 0.5675)\n"
     ]
    }
   ],
   "source": [
    "for m, cv in zip(x, coherence_values):\n",
    "    print(\"Num Topics =\", m, \" has Coherence Value of\", round(cv, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(16,\n",
      "  u'0.053*\"food\" + 0.036*\"product\" + 0.024*\"beverage\" + 0.023*\"restaurant\" + 0.020*\"brand\" + 0.020*\"sell\" + 0.017*\"market\" + 0.014*\"distribute\" + 0.014*\"offer\" + 0.011*\"produce\"'),\n",
      " (22,\n",
      "  u'0.293*\"product\" + 0.128*\"include\" + 0.123*\"company\" + 0.059*\"comprise\" + 0.057*\"offer\" + 0.047*\"provide\" + 0.035*\"found\" + 0.034*\"headquartered\" + 0.024*\"line\" + 0.023*\"addition\"'),\n",
      " (19,\n",
      "  u'0.095*\"loan\" + 0.048*\"account\" + 0.044*\"banking\" + 0.038*\"bank\" + 0.036*\"credit\" + 0.036*\"deposit\" + 0.030*\"commercial\" + 0.029*\"business\" + 0.023*\"saving\" + 0.019*\"check\"'),\n",
      " (23,\n",
      "  u'0.075*\"product\" + 0.030*\"home\" + 0.025*\"construction\" + 0.022*\"building\" + 0.019*\"offer\" + 0.018*\"sell\" + 0.017*\"commercial\" + 0.016*\"market\" + 0.016*\"light\" + 0.016*\"include\"'),\n",
      " (25,\n",
      "  u'0.063*\"solution\" + 0.057*\"software\" + 0.031*\"application\" + 0.027*\"datum\" + 0.027*\"platform\" + 0.021*\"enterprise\" + 0.021*\"cloud\" + 0.020*\"enable\" + 0.016*\"information\" + 0.015*\"manage\"'),\n",
      " (15,\n",
      "  u'0.091*\"holding\" + 0.061*\"operate\" + 0.036*\"international\" + 0.036*\"company\" + 0.032*\"global\" + 0.027*\"subsidiary\" + 0.025*\"american\" + 0.024*\"base\" + 0.017*\"hotel\" + 0.016*\"travel\"'),\n",
      " (20,\n",
      "  u'0.061*\"commercial\" + 0.053*\"real_estate\" + 0.044*\"residential\" + 0.041*\"operate\" + 0.038*\"company\" + 0.035*\"mortgage\" + 0.022*\"investment\" + 0.019*\"pennsylvania\" + 0.018*\"property\" + 0.018*\"land\"'),\n",
      " (28,\n",
      "  u'0.166*\"technology\" + 0.108*\"company\" + 0.104*\"development\" + 0.076*\"develop\" + 0.062*\"base\" + 0.023*\"focus\" + 0.022*\"found\" + 0.021*\"proprietary\" + 0.019*\"research\" + 0.016*\"license\"'),\n",
      " (10,\n",
      "  u'0.104*\"system\" + 0.035*\"security\" + 0.032*\"design\" + 0.027*\"provide\" + 0.022*\"support\" + 0.020*\"maintenance\" + 0.019*\"offer\" + 0.018*\"engineering\" + 0.018*\"safety\" + 0.017*\"government\"'),\n",
      " (2,\n",
      "  u'0.071*\"service\" + 0.066*\"management\" + 0.050*\"solution\" + 0.035*\"provide\" + 0.024*\"payment\" + 0.022*\"offer\" + 0.021*\"business\" + 0.016*\"client\" + 0.015*\"consulting\" + 0.015*\"process\"'),\n",
      " (29,\n",
      "  u'0.052*\"water\" + 0.052*\"energy\" + 0.021*\"fuel\" + 0.019*\"industry\" + 0.018*\"industrial\" + 0.017*\"plant\" + 0.017*\"clean\" + 0.015*\"environmental\" + 0.014*\"waste\" + 0.014*\"process\"'),\n",
      " (24,\n",
      "  u'0.056*\"system\" + 0.046*\"equipment\" + 0.035*\"power\" + 0.032*\"industrial\" + 0.022*\"manufacture\" + 0.022*\"component\" + 0.020*\"control\" + 0.015*\"industry\" + 0.013*\"solar\" + 0.013*\"engine\"'),\n",
      " (1,\n",
      "  u'0.074*\"network\" + 0.043*\"communication\" + 0.032*\"mobile\" + 0.026*\"internet\" + 0.025*\"wireless\" + 0.024*\"solution\" + 0.022*\"provide\" + 0.021*\"access\" + 0.017*\"datum\" + 0.015*\"voice\"'),\n",
      " (21,\n",
      "  u'0.065*\"brand\" + 0.049*\"store\" + 0.037*\"retail\" + 0.027*\"sell\" + 0.026*\"accessory\" + 0.025*\"operate\" + 0.023*\"retailer\" + 0.014*\"consumer\" + 0.014*\"apparel\" + 0.014*\"commerce\"'),\n",
      " (14,\n",
      "  u'0.201*\"company\" + 0.200*\"corporation\" + 0.104*\"state\" + 0.081*\"found\" + 0.077*\"united\" + 0.032*\"subsidiary\" + 0.031*\"headquartered\" + 0.028*\"include\" + 0.022*\"base\" + 0.021*\"primarily\"'),\n",
      " (12,\n",
      "  u'0.517*\"service\" + 0.129*\"provide\" + 0.078*\"offer\" + 0.064*\"management\" + 0.026*\"include\" + 0.023*\"addition\" + 0.021*\"business\" + 0.021*\"customer\" + 0.015*\"comprise\" + 0.009*\"range\"'),\n",
      " (9,\n",
      "  u'0.245*\"company\" + 0.095*\"california\" + 0.068*\"found\" + 0.058*\"state\" + 0.049*\"headquartered\" + 0.036*\"base\" + 0.022*\"operate\" + 0.018*\"llc\" + 0.018*\"united\" + 0.017*\"city\"'),\n",
      " (11,\n",
      "  u'0.189*\"segment\" + 0.090*\"operate\" + 0.051*\"customer\" + 0.038*\"sale\" + 0.030*\"state\" + 0.029*\"distribution\" + 0.026*\"relate\" + 0.023*\"two_segment\" + 0.023*\"retail\" + 0.023*\"primarily\"'),\n",
      " (3,\n",
      "  u'0.039*\"medium\" + 0.036*\"digital\" + 0.028*\"marketing\" + 0.027*\"content\" + 0.025*\"video\" + 0.024*\"online\" + 0.021*\"platform\" + 0.020*\"entertainment\" + 0.017*\"mobile\" + 0.016*\"website\"'),\n",
      " (31,\n",
      "  u'0.131*\"company\" + 0.048*\"state\" + 0.048*\"united\" + 0.035*\"found\" + 0.033*\"headquarter\" + 0.029*\"europe\" + 0.024*\"production\" + 0.024*\"international\" + 0.023*\"canada\" + 0.022*\"operate\"')]\n"
     ]
    }
   ],
   "source": [
    "optimal_model = model_list[5]\n",
    "model_topics = optimal_model.show_topics(formatted=False)\n",
    "pprint(optimal_model.print_topics(num_words=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Dominant_Topic</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8410</th>\n",
       "      <td>8410</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.2132</td>\n",
       "      <td>oil, energy, gas, texas, natural_gas, approxim...</td>\n",
       "      <td>Whiting USA Trust II holds a term net profits ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8411</th>\n",
       "      <td>8411</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3245</td>\n",
       "      <td>network, communication, mobile, internet, wire...</td>\n",
       "      <td>WideOpenWest, Inc. operates as a cable operato...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8412</th>\n",
       "      <td>8412</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0982</td>\n",
       "      <td>medium, digital, marketing, content, video, on...</td>\n",
       "      <td>Wikisoft Corp. provides online gaming solution...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8413</th>\n",
       "      <td>8413</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.3150</td>\n",
       "      <td>loan, account, banking, bank, credit, deposit,...</td>\n",
       "      <td>William Penn Bancorp, Inc. operates as the hol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8414</th>\n",
       "      <td>8414</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.4530</td>\n",
       "      <td>oil, energy, gas, texas, natural_gas, approxim...</td>\n",
       "      <td>Williams Partners L.P. operates as an energy i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8415</th>\n",
       "      <td>8415</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0887</td>\n",
       "      <td>company, base, new_york, corp, found, business...</td>\n",
       "      <td>Williamsville Sears Management, Inc. does not ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8416</th>\n",
       "      <td>8416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0975</td>\n",
       "      <td>insurance, financial, investment, service, man...</td>\n",
       "      <td>Wins Finance Holdings Inc., through its subsid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8417</th>\n",
       "      <td>8417</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.1361</td>\n",
       "      <td>system, security, design, provide, support, ma...</td>\n",
       "      <td>Wrap Technologies, Inc. develops security prod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8418</th>\n",
       "      <td>8418</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.1149</td>\n",
       "      <td>water, energy, fuel, industry, industrial, pla...</td>\n",
       "      <td>XcelPlus International, Inc. distributes chemi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8419</th>\n",
       "      <td>8419</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.1806</td>\n",
       "      <td>company, base, new_york, corp, found, business...</td>\n",
       "      <td>Yacht Finders, Inc. does not have significant ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8420</th>\n",
       "      <td>8420</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.1694</td>\n",
       "      <td>company, base, new_york, corp, found, business...</td>\n",
       "      <td>Yappn Corp. provides language translation prod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8421</th>\n",
       "      <td>8421</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.2845</td>\n",
       "      <td>food, product, beverage, restaurant, brand, se...</td>\n",
       "      <td>YaSheng Group, through its subsidiaries, desig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8422</th>\n",
       "      <td>8422</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>water, energy, fuel, industry, industrial, pla...</td>\n",
       "      <td>Zenergy Brands, Inc., an energy and technology...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8423</th>\n",
       "      <td>8423</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.1359</td>\n",
       "      <td>company, change, formerly_known, group, base, ...</td>\n",
       "      <td>Zulu-Tek Inc provides Internet advertising ser...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8424</th>\n",
       "      <td>8424</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.1615</td>\n",
       "      <td>product, health, company, care, market, offer,...</td>\n",
       "      <td>Zurvita Holdings, Inc. markets health and well...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Document_No  Dominant_Topic  Topic_Perc_Contrib  \\\n",
       "8410         8410            30.0              0.2132   \n",
       "8411         8411             1.0              0.3245   \n",
       "8412         8412             3.0              0.0982   \n",
       "8413         8413            19.0              0.3150   \n",
       "8414         8414            30.0              0.4530   \n",
       "8415         8415            17.0              0.0887   \n",
       "8416         8416             0.0              0.0975   \n",
       "8417         8417            10.0              0.1361   \n",
       "8418         8418            29.0              0.1149   \n",
       "8419         8419            17.0              0.1806   \n",
       "8420         8420            17.0              0.1694   \n",
       "8421         8421            16.0              0.2845   \n",
       "8422         8422            29.0              0.1357   \n",
       "8423         8423            26.0              0.1359   \n",
       "8424         8424            18.0              0.1615   \n",
       "\n",
       "                                               Keywords  \\\n",
       "8410  oil, energy, gas, texas, natural_gas, approxim...   \n",
       "8411  network, communication, mobile, internet, wire...   \n",
       "8412  medium, digital, marketing, content, video, on...   \n",
       "8413  loan, account, banking, bank, credit, deposit,...   \n",
       "8414  oil, energy, gas, texas, natural_gas, approxim...   \n",
       "8415  company, base, new_york, corp, found, business...   \n",
       "8416  insurance, financial, investment, service, man...   \n",
       "8417  system, security, design, provide, support, ma...   \n",
       "8418  water, energy, fuel, industry, industrial, pla...   \n",
       "8419  company, base, new_york, corp, found, business...   \n",
       "8420  company, base, new_york, corp, found, business...   \n",
       "8421  food, product, beverage, restaurant, brand, se...   \n",
       "8422  water, energy, fuel, industry, industrial, pla...   \n",
       "8423  company, change, formerly_known, group, base, ...   \n",
       "8424  product, health, company, care, market, offer,...   \n",
       "\n",
       "                                                   Text  \n",
       "8410  Whiting USA Trust II holds a term net profits ...  \n",
       "8411  WideOpenWest, Inc. operates as a cable operato...  \n",
       "8412  Wikisoft Corp. provides online gaming solution...  \n",
       "8413  William Penn Bancorp, Inc. operates as the hol...  \n",
       "8414  Williams Partners L.P. operates as an energy i...  \n",
       "8415  Williamsville Sears Management, Inc. does not ...  \n",
       "8416  Wins Finance Holdings Inc., through its subsid...  \n",
       "8417  Wrap Technologies, Inc. develops security prod...  \n",
       "8418  XcelPlus International, Inc. distributes chemi...  \n",
       "8419  Yacht Finders, Inc. does not have significant ...  \n",
       "8420  Yappn Corp. provides language translation prod...  \n",
       "8421  YaSheng Group, through its subsidiaries, desig...  \n",
       "8422  Zenergy Brands, Inc., an energy and technology...  \n",
       "8423  Zulu-Tek Inc provides Internet advertising ser...  \n",
       "8424  Zurvita Holdings, Inc. markets health and well...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data):\n",
    "    # Init output\n",
    "    sent_topics_df = pd.DataFrame()\n",
    "\n",
    "    # Get main topic in each document\n",
    "    for i, row in enumerate(ldamodel[corpus]):\n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "        # Get the Dominant topic, Perc Contribution and Keywords for each document\n",
    "        for j, (topic_num, prop_topic) in enumerate(row):\n",
    "            if j == 0:  # => dominant topic\n",
    "                wp = ldamodel.show_topic(topic_num)\n",
    "                topic_keywords = \", \".join([word for word, prop in wp])\n",
    "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
    "            else:\n",
    "                break\n",
    "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
    "\n",
    "    # Add original text to the end of the output\n",
    "    contents = pd.Series(texts)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
    "    return(sent_topics_df)\n",
    "\n",
    "\n",
    "df_topic_sents_keywords = format_topics_sentences(ldamodel=optimal_model, corpus=corpus, texts=data)\n",
    "\n",
    "# Format\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
    "\n",
    "# Show\n",
    "df_dominant_topic.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Topic_Num</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5352</td>\n",
       "      <td>insurance, financial, investment, service, man...</td>\n",
       "      <td>Cincinnati Financial Corporation provides prop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4772</td>\n",
       "      <td>network, communication, mobile, internet, wire...</td>\n",
       "      <td>Ooma, Inc. provides communications solutions a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.4455</td>\n",
       "      <td>service, management, solution, provide, paymen...</td>\n",
       "      <td>Resources Connection, Inc. provides business c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5301</td>\n",
       "      <td>medium, digital, marketing, content, video, on...</td>\n",
       "      <td>Viacom, Inc. operates media brands that create...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5505</td>\n",
       "      <td>health, care, healthcare, provide, medical, ce...</td>\n",
       "      <td>WellCare Health Plans, Inc. provides managed c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Topic_Num  Topic_Perc_Contrib  \\\n",
       "0        0.0              0.5352   \n",
       "1        1.0              0.4772   \n",
       "2        2.0              0.4455   \n",
       "3        3.0              0.5301   \n",
       "4        4.0              0.5505   \n",
       "\n",
       "                                            Keywords  \\\n",
       "0  insurance, financial, investment, service, man...   \n",
       "1  network, communication, mobile, internet, wire...   \n",
       "2  service, management, solution, provide, paymen...   \n",
       "3  medium, digital, marketing, content, video, on...   \n",
       "4  health, care, healthcare, provide, medical, ce...   \n",
       "\n",
       "                                                Text  \n",
       "0  Cincinnati Financial Corporation provides prop...  \n",
       "1  Ooma, Inc. provides communications solutions a...  \n",
       "2  Resources Connection, Inc. provides business c...  \n",
       "3  Viacom, Inc. operates media brands that create...  \n",
       "4  WellCare Health Plans, Inc. provides managed c...  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
    "\n",
    "sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
    "\n",
    "for i, grp in sent_topics_outdf_grpd:\n",
    "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
    "                                             grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n",
    "                                            axis=0)\n",
    "\n",
    "# Reset Index    \n",
    "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Format\n",
    "sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n",
    "\n",
    "# Show\n",
    "sent_topics_sorteddf_mallet.head()"
   ]
  }
 ],
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