Topic_Modeling_Guide_Notebook.ipynb

{
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  {
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   "source": [
    "### All Rights Reserved. This notebook is proprietary content of machinelearningplus.com. This can be shared solely for educational purposes, with due credits to machinelearningplus.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert\" style=\"background-color:#fff; color:white; padding:0px 10px; border-radius:5px;\"><h1 style='margin:15px 15px; color:#5d3a8e; font-size:40px'> Topic Modeling with Gensim (Python)</h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t0GCxAfUJMEl"
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   "source": [
    "Topic Modeling is a technique to extract the hidden topics from large volumes of text. Latent Dirichlet Allocation(LDA) is a popular algorithm for topic modeling with excellent implementations in the Python’s Gensim package. The challenge, however, is *how to extract good quality of topics that are clear, segregated and meaningful.* This depends heavily on the quality of text preprocessing and the strategy of finding the optimal number of topics. This tutorial attempts to tackle both of these problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> Content</h2>\n",
    "</div>\n",
    "\n",
    "1. Introduction\n",
    "2. Prerequisites – Download nltk stopwords and spacy model\n",
    "3. Import Packages\n",
    "4. What does LDA do?\n",
    "5. Prepare Stopwords\n",
    "6. Import Newsgroups Data\n",
    "7. Remove emails and newline characters\n",
    "8. Tokenize words and Clean-up text\n",
    "9. Creating Bigram and Trigram Models\n",
    "10. Remove Stopwords, Make Bigrams and Lemmatize\n",
    "11. Create the Dictionary and Corpus needed for Topic Modeling\n",
    "12. Building the Topic Model\n",
    "13. View the topics in LDA model\n",
    "14. Compute Model Perplexity and Coherence Score\n",
    "15. Visualize the topics-keywords\n",
    "16. Building LDA Mallet Model\n",
    "17. How to find the optimal number of topics for LDA?\n",
    "18. Finding the dominant topic in each sentence\n",
    "19. Find the most representative document for each topic\n",
    "20. Topic distribution across documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 1. Introduction</h2>\n",
    "</div>"
   ]
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   "cell_type": "markdown",
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    "id": "Olq16KXFJMEv"
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   "source": [
    "One of the primary applications of natural language processing is to automatically extract what topics people are discussing from large volumes of text. Some examples of large text could be feeds from social media, customer reviews of hotels, movies, etc, user feedbacks, news stories, e-mails of customer complaints etc.\n",
    "\n",
    "Knowing what people are talking about and understanding their problems and opinions is highly valuable to businesses, administrators, political campaigns. And it’s really hard to manually read through such large volumes and compile the topics.\n",
    "\n",
    "Thus is required an automated algorithm that can read through the text documents and automatically output the topics discussed.\n",
    "\n",
    "In this tutorial, we will take a real example of the ’20 Newsgroups’ dataset and use LDA to extract the naturally discussed topics.\n",
    "\n",
    "I will be using the Latent Dirichlet Allocation (LDA) from Gensim package along with the Mallet’s implementation (via Gensim). Mallet has an efficient implementation of the LDA. It is known to run faster and gives better topics segregation.\n",
    "\n",
    "We will also extract the volume and percentage contribution of each topic to get an idea of how important a topic is.\n",
    "\n",
    "Let’s begin!\n",
    "Topic Modeling with Gensim in Python. Photo by Jeremy Bishop."
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 2. Prerequisites – Download nltk stopwords and spacy model</h2>\n",
    "</div>\n",
    "\n",
    "We will need the stopwords from NLTK and spacy’s en model for text pre-processing. Later, we will be using the spacy model for lemmatization.\n",
    "\n",
    "Lemmatization is nothing but converting a word to its root word. For example: the lemma of the word ‘machines’ is ‘machine’. Likewise, ‘walking’ –> ‘walk’, ‘mice’ –> ‘mouse’ and so on."
   ]
  },
  {
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   "execution_count": 1,
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    "outputId": "5fefff2a-4f36-402a-8da0-aa86c0e5d55f"
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/ajaykumar/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
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       "True"
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   "source": [
    "# Run in python console\n",
    "import nltk; nltk.download('stopwords')\n",
    "\n",
    "# Run in terminal or command prompt\n",
    "#python3 -m spacy download en"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vTOmOEW0JMEw"
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   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 3. Import Packages</h2>\n",
    "</div>\n",
    "\n",
    "The core packages used in this tutorial are `re`, `gensim`, `spacy` and `pyLDAvis`. Besides this we will also using `matplotlib`,`numpy` and `pandas` for data handling and visualization. Let’s import them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install pyLDAvis\n",
    "# pip install gensim\n",
    "# pip install spacy==2.2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "2bHmxbxeJMEx"
   },
   "outputs": [],
   "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\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AQEEaKhVJMEx"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 4. What does LDA do?</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "EqXiW_NrJMEy"
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   "source": [
    "LDA’s approach to topic modeling is it considers each document as a collection of topics in a certain proportion. And each topic as a collection of keywords, again, in a certain proportion.\n",
    "\n",
    "Once you provide the algorithm with the number of topics, all it does it to rearrange the topics distribution within the documents and keywords distribution within the topics to obtain a good composition of topic-keywords distribution.\n",
    "\n",
    "When I say topic, what is it actually and how it is represented?\n",
    "\n",
    "A topic is nothing but a collection of dominant keywords that are typical representatives. Just by looking at the keywords, you can identify what the topic is all about.\n",
    "\n",
    "The following are key factors to obtaining good segregation topics:\n",
    "\n",
    "\n",
    "1)The quality of text processing.\n",
    "\n",
    "2)The variety of topics the text talks about.\n",
    "\n",
    "3)The choice of topic modeling algorithm.\n",
    "\n",
    "4)The number of topics fed to the algorithm.\n",
    "\n",
    "5)The algorithms tuning parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LU8bySG4JMEy"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 5. Prepare Stopwords</h2>\n",
    "</div>\n",
    "\n",
    "\n",
    "We have already downloaded the stopwords. Let's import them and make it available in `stop_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "kxK_dgu1JMEz"
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   "outputs": [],
   "source": [
    "# NLTK Stop words\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3gjPwI-UJME0"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 6. Import Newsgroups Data</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1N8yzen0_B_4"
   },
   "source": [
    "We will be using the 20-Newsgroups dataset for this exercise. This version of the dataset contains about 11k newsgroups posts from 20 different topics. This is available as [newsgroups.json](https://raw.githubusercontent.com/selva86/datasets/master/newsgroups.json).\n",
    "\n",
    "This is imported using `pandas.read_json` and the resulting dataset has 3 columns as shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rec.autos' 'comp.sys.mac.hardware' 'comp.graphics' 'sci.space'\n",
      " 'talk.politics.guns' 'sci.med' 'comp.sys.ibm.pc.hardware'\n",
      " 'comp.os.ms-windows.misc' 'rec.motorcycles' 'talk.religion.misc'\n",
      " 'misc.forsale' 'alt.atheism' 'sci.electronics' 'comp.windows.x'\n",
      " 'rec.sport.hockey' 'rec.sport.baseball' 'soc.religion.christian'\n",
      " 'talk.politics.mideast' 'talk.politics.misc' 'sci.crypt']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content</th>\n",
       "      <th>target</th>\n",
       "      <th>target_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: lerxst@wam.umd.edu (where's my thing)\\nS...</td>\n",
       "      <td>7</td>\n",
       "      <td>rec.autos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From: guykuo@carson.u.washington.edu (Guy Kuo)...</td>\n",
       "      <td>4</td>\n",
       "      <td>comp.sys.mac.hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>From: twillis@ec.ecn.purdue.edu (Thomas E Will...</td>\n",
       "      <td>4</td>\n",
       "      <td>comp.sys.mac.hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>From: jgreen@amber (Joe Green)\\nSubject: Re: W...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>From: jcm@head-cfa.harvard.edu (Jonathan McDow...</td>\n",
       "      <td>14</td>\n",
       "      <td>sci.space</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  target  \\\n",
       "0  From: lerxst@wam.umd.edu (where's my thing)\\nS...       7   \n",
       "1  From: guykuo@carson.u.washington.edu (Guy Kuo)...       4   \n",
       "2  From: twillis@ec.ecn.purdue.edu (Thomas E Will...       4   \n",
       "3  From: jgreen@amber (Joe Green)\\nSubject: Re: W...       1   \n",
       "4  From: jcm@head-cfa.harvard.edu (Jonathan McDow...      14   \n",
       "\n",
       "            target_names  \n",
       "0              rec.autos  \n",
       "1  comp.sys.mac.hardware  \n",
       "2  comp.sys.mac.hardware  \n",
       "3          comp.graphics  \n",
       "4              sci.space  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Dataset\n",
    "df = pd.read_json('https://raw.githubusercontent.com/selva86/datasets/master/newsgroups.json')\n",
    "print(df.target_names.unique())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_Pvuv-EDJME5"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 7. Remove emails and newline characters</h2>\n",
    "</div>\n",
    "\n",
    "As you can see there are many emails, newline and extra spaces that is quite distracting. Let's get rid of them using [regular expressions](https://www.machinelearningplus.com/python/python-regex-tutorial-examples/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
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    "outputId": "f85bd407-2e06-4416-f0da-4a62dbebc92c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['From: (where s my thing) Subject: WHAT car is this!? Nntp-Posting-Host: '\n",
      " 'rac3.wam.umd.edu Organization: University of Maryland, College Park Lines: '\n",
      " '15 I was wondering if anyone out there could enlighten me on this car I saw '\n",
      " 'the other day. It was a 2-door sports car, looked to be from the late 60s/ '\n",
      " 'early 70s. It was called a Bricklin. The doors were really small. In '\n",
      " 'addition, the front bumper was separate from the rest of the body. This is '\n",
      " 'all I know. If anyone can tellme a model name, engine specs, years of '\n",
      " 'production, where this car is made, history, or whatever info you have on '\n",
      " 'this funky looking car, please e-mail. Thanks, - IL ---- brought to you by '\n",
      " 'your neighborhood Lerxst ---- ',\n",
      " 'From: (Guy Kuo) Subject: SI Clock Poll - Final Call Summary: Final call for '\n",
      " 'SI clock reports Keywords: SI,acceleration,clock,upgrade Article-I.D.: '\n",
      " 'shelley.1qvfo9INNc3s Organization: University of Washington Lines: 11 '\n",
      " 'NNTP-Posting-Host: carson.u.washington.edu A fair number of brave souls who '\n",
      " 'upgraded their SI clock oscillator have shared their experiences for this '\n",
      " 'poll. Please send a brief message detailing your experiences with the '\n",
      " 'procedure. Top speed attained, CPU rated speed, add on cards and adapters, '\n",
      " 'heat sinks, hour of usage per day, floppy disk functionality with 800 and '\n",
      " '1.4 m floppies are especially requested. I will be summarizing in the next '\n",
      " 'two days, so please add to the network knowledge base if you have done the '\n",
      " 'clock upgrade and haven t answered this poll. Thanks. Guy Kuo ']\n"
     ]
    }
   ],
   "source": [
    "# Convert to list\n",
    "data = df.content.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",
    "pprint(data[:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GMWsrdMYJME6"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 8. Tokenize words and Clean-up text </h2>\n",
    "</div>\n",
    "\n",
    "The sentences look better now, but you want to tokenize each sentence into a list of words, removing punctuations and unneccessary characters altogether.\n",
    "\n",
    "Gensim's `simple_preprocess()` is great for this. Additionally I have set `deacc=True` to remove the punctuations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    },
    "id": "hEawaeOJJME6",
    "outputId": "932f8566-a89c-4c10-a074-1f1fec6840e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['from', 'where', 'my', 'thing', 'subject', 'what', 'car', 'is', 'this', 'nntp', 'posting', 'host', 'rac', 'wam', 'umd', 'edu', 'organization', 'university', 'of', 'maryland', 'college', 'park', 'lines', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'saw', 'the', 'other', 'day', 'it', 'was', 'door', 'sports', 'car', 'looked', 'to', 'be', 'from', 'the', 'late', 'early', 'it', 'was', 'called', 'bricklin', 'the', 'doors', 'were', 'really', 'small', 'in', 'addition', 'the', 'front', 'bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', 'this', 'is', 'all', 'know', 'if', 'anyone', 'can', 'tellme', 'model', 'name', 'engine', 'specs', 'years', 'of', 'production', 'where', 'this', 'car', 'is', 'made', 'history', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', 'please', 'mail', 'thanks', 'il', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'lerxst']]\n"
     ]
    }
   ],
   "source": [
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence).encode('utf-8'), deacc=True))  # deacc=True removes punctuations\n",
    "\n",
    "data_words = list(sent_to_words(data))\n",
    "print(data_words[:1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZSg33gKvJME6"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 9. Create Bigram and Trigram Models</h2>\n",
    "</div>\n",
    "\n",
    "Bigrams are two words frequently occurring together in the document. Trigrams are 3 words frequently occurring.\n",
    "\n",
    "Some examples in our example are: 'front bumper', 'oil leak', 'maryland college park' etc.\n",
    "\n",
    "Gensim's `Phrases` model can build and implement the bigrams, trigrams, quadgrams and more. The two important arguments to `Phrases` are `min_count` and `threshold`. The higher the values of these param, the harder it is for words to be combined to bigrams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "yrwK3tkBJME7",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['from', 'where', 'my', 'thing', 'subject', 'what', 'car', 'is', 'this', 'nntp_posting_host', 'rac_wam_umd_edu', 'organization', 'university', 'of', 'maryland_college_park', 'lines', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'saw', 'the', 'other', 'day', 'it', 'was', 'door', 'sports', 'car', 'looked', 'to', 'be', 'from', 'the', 'late', 'early', 'it', 'was', 'called', 'bricklin', 'the', 'doors', 'were', 'really', 'small', 'in', 'addition', 'the', 'front_bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', 'this', 'is', 'all', 'know', 'if', 'anyone', 'can', 'tellme', 'model', 'name', 'engine', 'specs', 'years', 'of', 'production', 'where', 'this', 'car', 'is', 'made', 'history', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', 'please', 'mail', 'thanks', 'il', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'lerxst']\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": "markdown",
   "metadata": {
    "id": "mSKmvrrMJME7"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 10. Remove Stopwords, Make Bigrams and Lemmatize</h2>\n",
    "</div>\n",
    "\n",
    "\n",
    "The bigrams model is ready. Let's define the functions to remove the stopwords, make bigrams and lemmatization and call them sequentially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "pIAmYQLkJME7"
   },
   "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": "markdown",
   "metadata": {
    "id": "9vD4jR4UBygH"
   },
   "source": [
    "Let’s call the functions in order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "MGC-N8_EJME7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['car', 'nntp_posting', 'host', 'line', 'wonder', 'enlighten', 'car', 'see', 'day', 'door', 'sport', 'car', 'look', 'late', 'early', 'call', 'door', 'really', 'small', 'addition', 'front_bumper', 'separate', 'rest', 'body', 'know', 'year', 'production', 'car', 'make', 'history', 'info', 'funky', 'look', 'car', 'mail', 'thank', 'bring', 'neighborhood', 'lerxst']]\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",
    "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
    "# python3 -m spacy download en\n",
    "nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])\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])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R7uMrXUpJME8"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 11. Create the Dictionary and Corpus needed for Topic Modeling</h2>\n",
    "</div>\n",
    "\n",
    "The two main inputs to the LDA topic model are the dictionary(`id2word`) and the corpus. Let's create them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "DqlOgG27JME8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 5), (5, 1), (6, 2), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 2), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1), (32, 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": "markdown",
   "metadata": {
    "id": "LjFNNXzAJME9"
   },
   "source": [
    "Gensim creates a unique id for each word in the document. The produced corpus shown above is a mapping of (word_id, word_frequency).\n",
    "\n",
    "For example, (0, 1) above implies, word id 0 occurs once in the first document. Likewise, word id 1 occurs twice and so on.\n",
    "\n",
    "This is used as the input by the LDA model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vl0gK6hEJME-"
   },
   "source": [
    "If you want to see what word a given id corresponds to, pass the id as a key to the dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "RLtyx0RpJME-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'addition'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2word[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rUb3j6Q3JME_"
   },
   "source": [
    "Or, you can see a human readable form of the corpus itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "02ezXeDuJME_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1),\n",
       " (1, 1),\n",
       " (2, 1),\n",
       " (3, 1),\n",
       " (4, 5),\n",
       " (5, 1),\n",
       " (6, 2),\n",
       " (7, 1),\n",
       " (8, 1),\n",
       " (9, 1)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[:1][0][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "U6ZzD5UiJME_",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('addition', 1),\n",
       "  ('body', 1),\n",
       "  ('bring', 1),\n",
       "  ('call', 1),\n",
       "  ('car', 5),\n",
       "  ('day', 1),\n",
       "  ('door', 2),\n",
       "  ('early', 1),\n",
       "  ('enlighten', 1),\n",
       "  ('front_bumper', 1),\n",
       "  ('funky', 1),\n",
       "  ('history', 1),\n",
       "  ('host', 1),\n",
       "  ('info', 1),\n",
       "  ('know', 1),\n",
       "  ('late', 1),\n",
       "  ('lerxst', 1),\n",
       "  ('line', 1),\n",
       "  ('look', 2),\n",
       "  ('mail', 1),\n",
       "  ('make', 1),\n",
       "  ('neighborhood', 1),\n",
       "  ('nntp_posting', 1),\n",
       "  ('production', 1),\n",
       "  ('really', 1),\n",
       "  ('rest', 1),\n",
       "  ('see', 1),\n",
       "  ('separate', 1),\n",
       "  ('small', 1),\n",
       "  ('sport', 1),\n",
       "  ('thank', 1),\n",
       "  ('wonder', 1),\n",
       "  ('year', 1)]]"
      ]
     },
     "execution_count": 15,
     "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": "markdown",
   "metadata": {
    "id": "tpqMJzvFJMFA"
   },
   "source": [
    "Alright, without digressing further let's jump back on track with the next step: Building the topic model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DAuJo71iJMFA"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 12. Building the Topic Model</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rJ0KiNu8JMFA"
   },
   "source": [
    "We have everything required to train the LDA model. In addition to the corpus and dictionary, you need to provide the number of topics as well. \n",
    "\n",
    "Apart from that, `alpha` and `eta` are hyperparameters that affect sparsity of the topics. According to the gensim docs, both defaults to 1.0/num_topics prior.\n",
    "\n",
    "`chunksize` is the number of documents to be used in each training chunk.  `update_every` determines how often the model parameters should be updated and `passes` is the total number of training passes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "VAPlP5krJMFA"
   },
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=20, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YVvKis8MJMFA"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 13. View the topics in LDA model</h2>\n",
    "</div>\n",
    "\n",
    "The above LDA model is built with 20 different topics where each topic is a combination of keywords and each keyword contributes a certain weightage to the topic.\n",
    "\n",
    "You can see the keywords for each topic and the weightage(importance) of each keyword using `lda_model.print_topics()` as shown next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "_xrMa9_nJMFB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.040*\"evidence\" + 0.031*\"believe\" + 0.030*\"reason\" + 0.027*\"claim\" + '\n",
      "  '0.021*\"say\" + 0.021*\"sense\" + 0.019*\"faith\" + 0.019*\"exist\" + '\n",
      "  '0.014*\"people\" + 0.014*\"science\"'),\n",
      " (1,\n",
      "  '0.034*\"write\" + 0.023*\"article\" + 0.022*\"line\" + 0.021*\"know\" + '\n",
      "  '0.019*\"make\" + 0.019*\"go\" + 0.019*\"say\" + 0.018*\"get\" + 0.017*\"think\" + '\n",
      "  '0.016*\"see\"'),\n",
      " (2,\n",
      "  '0.091*\"season\" + 0.078*\"fan\" + 0.061*\"trade\" + 0.044*\"vote\" + 0.042*\"pen\" + '\n",
      "  '0.038*\"star\" + 0.030*\"helmet\" + 0.025*\"devil\" + 0.024*\"remind\" + '\n",
      "  '0.021*\"penalty\"'),\n",
      " (3,\n",
      "  '0.133*\"technology\" + 0.036*\"technical\" + 0.035*\"industry\" + '\n",
      "  '0.035*\"development\" + 0.032*\"develop\" + 0.029*\"resource\" + 0.027*\"medical\" '\n",
      "  '+ 0.024*\"schedule\" + 0.022*\"solid\" + 0.021*\"nuclear\"'),\n",
      " (4,\n",
      "  '0.035*\"kill\" + 0.021*\"side\" + 0.019*\"attack\" + 0.018*\"israeli\" + '\n",
      "  '0.017*\"fire\" + 0.017*\"bike\" + 0.017*\"death\" + 0.017*\"war\" + 0.017*\"man\" + '\n",
      "  '0.015*\"woman\"'),\n",
      " (5,\n",
      "  '0.092*\"ground\" + 0.043*\"task\" + 0.032*\"regularly\" + 0.030*\"hook\" + '\n",
      "  '0.026*\"crack\" + 0.026*\"mph\" + 0.021*\"wall\" + 0.021*\"paint\" + 0.020*\"volt\" + '\n",
      "  '0.020*\"impressive\"'),\n",
      " (6,\n",
      "  '0.082*\"community\" + 0.063*\"concept\" + 0.057*\"air\" + 0.054*\"significant\" + '\n",
      "  '0.044*\"aware\" + 0.029*\"consideration\" + 0.029*\"role\" + 0.027*\"approve\" + '\n",
      "  '0.026*\"co\" + 0.023*\"percentage\"'),\n",
      " (7,\n",
      "  '0.076*\"input\" + 0.052*\"mount\" + 0.051*\"material\" + 0.050*\"signal\" + '\n",
      "  '0.043*\"disease\" + 0.039*\"door\" + 0.037*\"be\" + 0.036*\"circuit\" + '\n",
      "  '0.036*\"output\" + 0.034*\"workstation\"'),\n",
      " (8,\n",
      "  '0.109*\"space\" + 0.044*\"launch\" + 0.040*\"mouse\" + 0.038*\"engine\" + '\n",
      "  '0.036*\"orbit\" + 0.032*\"mission\" + 0.027*\"satellite\" + 0.026*\"plane\" + '\n",
      "  '0.024*\"flight\" + 0.024*\"earth\"'),\n",
      " (9,\n",
      "  '0.037*\"state\" + 0.029*\"government\" + 0.027*\"law\" + 0.025*\"right\" + '\n",
      "  '0.024*\"public\" + 0.020*\"issue\" + 0.018*\"talk\" + 0.018*\"people\" + '\n",
      "  '0.014*\"rule\" + 0.013*\"country\"'),\n",
      " (10,\n",
      "  '0.075*\"drive\" + 0.045*\"card\" + 0.038*\"driver\" + 0.023*\"speed\" + '\n",
      "  '0.021*\"memory\" + 0.021*\"system\" + 0.020*\"fast\" + 0.018*\"device\" + '\n",
      "  '0.018*\"instal\" + 0.017*\"problem\"'),\n",
      " (11,\n",
      "  '0.132*\"team\" + 0.106*\"game\" + 0.084*\"play\" + 0.077*\"win\" + 0.042*\"year\" + '\n",
      "  '0.033*\"score\" + 0.029*\"wing\" + 0.029*\"goal\" + 0.027*\"run\" + '\n",
      "  '0.019*\"originator\"'),\n",
      " (12,\n",
      "  '0.166*\"gun\" + 0.042*\"weight\" + 0.038*\"hot\" + 0.032*\"cycle\" + 0.030*\"cop\" + '\n",
      "  '0.028*\"water\" + 0.026*\"club\" + 0.026*\"battery\" + 0.025*\"police\" + '\n",
      "  '0.025*\"cool\"'),\n",
      " (13,\n",
      "  '0.150*\"key\" + 0.071*\"chip\" + 0.041*\"bit\" + 0.030*\"block\" + 0.029*\"family\" + '\n",
      "  '0.024*\"secure\" + 0.023*\"recommend\" + 0.022*\"listen\" + 0.022*\"secret\" + '\n",
      "  '0.020*\"doctor\"'),\n",
      " (14,\n",
      "  '0.106*\"crime\" + 0.071*\"criminal\" + 0.069*\"controller\" + '\n",
      "  '0.055*\"united_state\" + 0.032*\"relation\" + 0.030*\"electrical\" + '\n",
      "  '0.023*\"disarm\" + 0.018*\"honor\" + 0.016*\"custom\" + 0.013*\"approximately\"'),\n",
      " (15,\n",
      "  '0.043*\"year\" + 0.030*\"car\" + 0.025*\"high\" + 0.025*\"new\" + 0.021*\"buy\" + '\n",
      "  '0.021*\"power\" + 0.019*\"price\" + 0.019*\"money\" + 0.018*\"sell\" + 0.018*\"pay\"'),\n",
      " (16,\n",
      "  '0.029*\"use\" + 0.028*\"line\" + 0.018*\"system\" + 0.018*\"thank\" + 0.014*\"need\" '\n",
      "  '+ 0.014*\"also\" + 0.014*\"program\" + 0.013*\"file\" + 0.013*\"mail\" + '\n",
      "  '0.012*\"include\"'),\n",
      " (17,\n",
      "  '0.099*\"image\" + 0.080*\"patient\" + 0.075*\"color\" + 0.069*\"screen\" + '\n",
      "  '0.061*\"display\" + 0.058*\"graphic\" + 0.042*\"function\" + 0.029*\"object\" + '\n",
      "  '0.021*\"blind\" + 0.018*\"mirror\"'),\n",
      " (18,\n",
      "  '0.025*\"group\" + 0.016*\"child\" + 0.014*\"report\" + 0.013*\"become\" + '\n",
      "  '0.012*\"many\" + 0.012*\"cause\" + 0.011*\"result\" + 0.011*\"part\" + 0.011*\"die\" '\n",
      "  '+ 0.011*\"speak\"'),\n",
      " (19,\n",
      "  '0.032*\"armenian\" + 0.028*\"direct\" + 0.024*\"murder\" + 0.023*\"occupy\" + '\n",
      "  '0.023*\"turkish\" + 0.022*\"terrorism\" + 0.021*\"turk\" + 0.020*\"jewish\" + '\n",
      "  '0.020*\"tax\" + 0.018*\"greek\"')]\n"
     ]
    }
   ],
   "source": [
    "# Print the Keyword in the 10 topics\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y9ZOlWfRJMFB"
   },
   "source": [
    "How to interpret this?\n",
    "\n",
    "Topic 0 is a represented as - 0.040*\"evidence\" + 0.030*\"believe\" + 0.030*\"reason\" + 0.027*\"claim\" + '\n",
    "  '0.021*\"sense\" + 0.021*\"say\" + 0.019*\"faith\" + 0.019*\"exist\" + '\n",
    "  '0.014*\"people\" + 0.014*\"science\".\n",
    "\n",
    "It means the top 10 keywords that contribute to this topic are: 'evidence', 'believe', 'reason'.. and so on and the weight of 'evidence' on topic 0 is 0.040. \n",
    "\n",
    "The weights reflect how important a keyword is to that topic.\n",
    "\n",
    "Looking at these keywords, can you guess what this topic could be? You may summarise it either are 'cars' or 'automobiles'.\n",
    "\n",
    "Likewise, can you go through the remaining topic keywords and judge what the topic is?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EgvO-m-H5DH3"
   },
   "source": [
    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)\n",
    "\n",
    "Inferring Topic from Keywords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "efQl15CYJMFB"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 14. Compute Model Perplexity and Coherence Score</h2>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "41dafJVjJMFC"
   },
   "source": [
    "Model perplexity and [topic coherence](https://rare-technologies.com/what-is-topic-coherence/) provide a convenient measure to judge how good a given topic model is. In my experience, topic coherence score, in particular, has been more helpful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "wuud9M0VJMFC",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity:  -13.643598637589042\n",
      "\n",
      "Coherence Score:  0.44868845329425444\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": "markdown",
   "metadata": {
    "id": "987_oCkWJMFC"
   },
   "source": [
    "There you have a coherence score of 0.44."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UqNrGJbJJMFC"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 15. Visualize the topics-keywords</h2>\n",
    "</div>\n",
    "\n",
    "Now that the LDA model is built, the next step is to examine the produced topics and the associated keywords. There is no better tool than pyLDAvis package’s interactive chart and is designed to work well with jupyter notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "VwiiQ6bEJMFD"
   },
   "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_el2133462644063285659230788\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el2133462644063285659230788_data = {\"mdsDat\": {\"x\": [-0.3288735907561209, -0.29519323579662754, -0.21909103248268003, -0.20254315871104098, -0.1387035195216867, -0.23248963931436784, -0.060680925049895605, -0.08805203370724915, 0.01533529098638167, 0.0719084120002017, 0.031236807189630216, 0.11940399758255311, 0.11930598450110239, 0.1467775160381627, 0.15161823457111354, 0.16784276122758554, 0.16230829400059993, 0.1844189201843828, 0.1928600953982268, 0.2026108216597275], \"y\": [-0.08607165558148602, 0.08672397627969822, -0.020998531744466507, -0.12952897326299026, 0.28573955193238354, -0.241185294960675, 0.04394729960144862, 0.22702277765206663, -0.10981449037389412, 0.0448752114959982, 0.0750466555046003, 0.003556016113659238, 0.005275391891707212, -0.033549349528647396, -0.019673994196886372, -0.023634940682017498, -0.014715059224277797, -0.027260967142893718, -0.035457316342815175, -0.03029630743051014], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 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\"int\", \"intentionally\", \"interest\", \"interest\", \"interested\", \"interface\", \"interpretation\", \"invalid\", \"israeli\", \"issue\", \"issue\", \"ist\", \"jewish\", \"jewish\", \"key\", \"kill\", \"kind\", \"km\", \"know\", \"know\", \"land\", \"land\", \"large\", \"large\", \"large\", \"large\", \"large\", \"last\", \"last\", \"last\", \"launch\", \"law\", \"law_enforcement\", \"lead\", \"lead\", \"lead\", \"leadership\", \"leather\", \"leave\", \"leave\", \"legal\", \"lemieux\", \"less\", \"less\", \"less\", \"less\", \"less\", \"let\", \"letter\", \"letter\", \"license\", \"lie\", \"lie\", \"lie\", \"life\", \"life\", \"life\", \"lift\", \"light\", \"like\", \"line\", \"line\", \"list\", \"listen\", \"little\", \"load\", \"logic\", \"longer\", \"look\", \"look\", \"look\", \"loop\", \"lose\", \"lose\", \"low\", \"low\", \"lunar\", \"machine\", \"machine\", \"mail\", \"make\", \"make\", \"make\", \"make\", \"man\", \"man\", \"man\", \"many\", \"many\", \"many\", \"many\", \"many\", \"mask\", \"massacre\", \"material\", \"maybe\", \"mean\", \"mean\", \"mean\", \"medical\", \"meet\", \"member\", \"memory\", \"message\", \"message\", \"mid\", \"middle\", \"military\", \"military\", \"milk\", \"minor\", \"mirror\", \"mission\", \"mode\", \"model\", \"model\", \"money\", \"month\", \"moon\", \"moral\", \"motto\", \"mount\", \"mouse\", \"move\", \"move\", \"move\", \"mph\", \"much\", \"much\", \"murder\", \"murder\", \"murder\", \"muslim\", \"national\", \"need\", \"need\", \"need\", \"need\", \"neighborhood\", \"network\", \"never\", \"new\", \"new\", \"new\", \"next\", \"next\", \"next\", \"next\", \"nhl\", \"nice\", \"nice\", \"nicely\", \"night\", \"nntp_posting\", \"northern\", \"nuclear\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"object\", \"object\", \"objective\", \"observation\", \"observer\", \"occupy\", \"occur\", \"odd\", \"offense\", \"official\", \"official\", \"official\", \"often\", \"often\", \"often\", \"oil\", \"oiler\", \"old\", \"old\", \"old\", \"openwindow\", \"orbit\", \"organization\", \"organization\", \"originator\", \"other\", \"other\", \"other\", \"other\", \"other\", \"output\", \"owner\", \"ozone\", \"pack\", \"paint\", \"part\", \"part\", \"part\", \"part\", \"part\", \"participation\", \"pass\", \"pass\", \"patient\", \"pay\", \"pc\", \"peace\", \"pen\", \"penalty\", \"penguin\", \"people\", \"people\", \"people\", \"people\", \"percentage\", \"performance\", \"period\", \"peripheral\", \"person\", \"person\", \"person\", \"personnel\", \"pgp\", \"phase\", \"philly\", \"physician\", \"pick\", \"pick\", \"picture\", \"picture\", \"pitch\", \"plane\", \"planet\", \"planetary\", \"plausible\", \"play\", \"play\", \"player\", \"please_respond\", \"plug\", \"point\", \"point\", \"point\", \"point\", \"pointer\", \"police\", \"police\", \"pop\", \"popular\", \"possibly\", \"postscript\", \"power\", \"power\", \"preach\", \"precedent\", \"preferably\", \"preliminary\", \"present\", \"present\", \"presentation\", \"pretend\", \"pretty\", \"price\", \"priority\", \"private\", \"problem\", \"problem\", \"problem\", \"proceed\", \"production\", \"program\", \"prohibit\", \"project\", \"project\", \"property\", \"proposal\", \"protect\", \"protective\", \"prove\", \"prove\", \"prove\", \"prove\", \"provide\", \"provide\", \"provide\", \"provide\", \"pseudo\", \"pub\", \"public\", \"publish\", \"publish\", \"publisher\", \"pull\", \"purchase\", \"put\", \"question\", \"question\", \"question\", \"question\", \"quick\", \"radio\", \"ram\", \"rape\", \"rate\", \"rate\", \"ray\", \"rbi\", \"reactor\", \"really\", \"reason\", \"reason\", \"reason\", \"recieve\", \"recognition\", \"recommend\", \"record\", \"record\", \"record\", \"record\", \"record\", \"red\", \"region\", \"regularly\", \"reject\", \"reject\", \"relate\", \"relation\", \"religion\", \"religious\", \"religious\", \"remember\", \"remind\", \"remote\", \"report\", \"research\", \"research\", \"reserve\", \"reserve_university\", \"resolution\", \"resource\", \"respect\", \"result\", \"result\", \"retire\", \"revelation\", \"revolver\", \"ride\", \"rider\", \"right\", \"right\", \"ring\", \"risk\", \"road\", \"road\", \"rocket\", \"role\", \"roman\", \"room\", \"round\", \"round\", \"route\", \"routine\", \"rsa\", \"rule\", \"rule\", \"run\", \"run\", \"run\", \"russian\", \"sabre\", \"safety\", \"safety\", \"safety\", \"sale\", \"sample\", \"satellite\", \"saturn\", \"say\", \"say\", \"scare\", \"schedule\", \"schedule\", \"science\", \"scope\", \"scope\", \"score\", \"screen\", \"scripture\", \"scsi\", \"search\", \"season\", \"second\", \"second\", \"second\", \"second\", \"second\", \"secrecy\", \"secret\", \"section\", \"secure\", \"security\", \"security\", \"see\", \"see\", \"seem\", \"seem\", \"self\", \"self_defense\", \"sell\", \"semi_auto\", \"send\", \"sense\", \"sense\", \"serdar_argic\", \"serious\", \"session\", \"set\", \"set\", \"seventh\", \"shape\", \"sharp\", \"shoot\", \"shuttle\", \"side\", \"signal\", \"significant\", \"size\", \"slaughter\", \"slow\", \"software\", \"software\", \"solar\", \"solar_terresterial\", \"soldier\", \"soldier\", \"solid\", \"son\", \"son\", \"sorry\", \"sorry\", \"soul\", \"source\", \"source\", \"source\", \"soviet_union\", \"space\", \"space_shuttle\", \"space_station\", \"speak\", \"special\", \"specification\", \"specify\", \"speed\", \"spend\", \"spend\", \"stab\", \"staff\", \"staff\", \"star\", \"starter\", \"stat\", \"state\", \"statement\", \"statement\", \"static\", \"steam\", \"step\", \"still\", \"stop\", \"stop\", \"straight\", \"street\", \"strength\", \"strip\", \"stroke\", \"study\", \"succeed\", \"sufficiently\", \"summarize\", \"summarize\", \"sunlight\", \"support\", \"support\", \"support\", \"support\", \"sustain\", \"switch\", \"system\", \"system\", \"system\", \"tail\", \"take\", \"talk\", \"talk\", \"tap\", \"tape\", \"task\", \"tax\", \"team\", \"technical\", \"technical\", \"technology\", \"telescope\", \"television\", \"tell\", \"telnet\", \"term\", \"terrorism\", \"th\", \"thank\", \"thermal\", \"thesis\", \"thick\", \"thing\", \"thing\", \"think\", \"think\", \"third\", \"third\", \"third\", \"third\", \"tie\", \"time\", \"time\", \"total\", \"total\", \"touch\", \"trace\", \"trade\", \"tradition\", \"transport\", \"treatment\", \"trick\", \"true\", \"true\", \"true\", \"true\", \"trust\", \"truth\", \"try\", \"try\", \"turk\", \"turkish\", \"type\", \"ultimate\", \"unhappy\", \"united_state\", \"unusual\", \"upcoming\", \"upgrade\", \"use\", \"use\", \"use\", \"use\", \"user\", \"vehicle\", \"vehicle\", \"version\", \"version\", \"video\", \"view\", \"view\", \"view\", \"village\", \"village\", \"visualization\", \"volt\", \"voltage\", \"vote\", \"voting\", \"wait\", \"wait\", \"wall\", \"want\", \"want\", \"war\", \"warrant\", \"waste\", \"water\", \"wave\", \"way\", \"way\", \"way\", \"weight\", \"well\", \"well\", \"white\", \"win\", \"window\", \"wing\", \"withdraw\", \"woman\", \"woman\", \"woman\", \"wonder\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"workstation\", \"write\", \"write\", \"year\", \"year\", \"year\", \"year\", \"year\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 17, 19, 10, 16, 1, 5, 11, 20, 14, 12, 18, 9, 4, 13, 8, 3, 7, 15, 6]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el2133462644063285659230788\", ldavis_el2133462644063285659230788_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_el2133462644063285659230788\", ldavis_el2133462644063285659230788_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_el2133462644063285659230788\", ldavis_el2133462644063285659230788_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "1     -0.328874 -0.086072       1        1  36.891814\n",
       "16    -0.295193  0.086724       2        1  17.645877\n",
       "18    -0.219091 -0.020999       3        1   9.087457\n",
       "9     -0.202543 -0.129529       4        1   6.546767\n",
       "15    -0.138704  0.285740       5        1   6.486947\n",
       "0     -0.232490 -0.241185       6        1   5.870546\n",
       "4     -0.060681  0.043947       7        1   3.738310\n",
       "10    -0.088052  0.227023       8        1   3.659688\n",
       "19     0.015335 -0.109814       9        1   1.636868\n",
       "13     0.071908  0.044875      10        1   1.597497\n",
       "11     0.031237  0.075047      11        1   1.331517\n",
       "17     0.119404  0.003556      12        1   0.993768\n",
       "8      0.119306  0.005275      13        1   0.875789\n",
       "3      0.146778 -0.033549      14        1   0.716643\n",
       "12     0.151618 -0.019674      15        1   0.709163\n",
       "7      0.167843 -0.023635      16        1   0.608651\n",
       "2      0.162308 -0.014715      17        1   0.563443\n",
       "6      0.184419 -0.027261      18        1   0.413067\n",
       "14     0.192860 -0.035457      19        1   0.350880\n",
       "5      0.202611 -0.030296      20        1   0.275309, topic_info=              Term          Freq         Total Category  logprob  loglift\n",
       "184          write  14017.000000  14017.000000  Default  30.0000  30.0000\n",
       "32            year   4523.000000   4523.000000  Default  29.0000  29.0000\n",
       "286            say   8930.000000   8930.000000  Default  28.0000  28.0000\n",
       "623            key   2627.000000   2627.000000  Default  27.0000  27.0000\n",
       "125         people   7999.000000   7999.000000  Default  26.0000  26.0000\n",
       "...            ...           ...           ...      ...      ...      ...\n",
       "13566       wiring      0.042323      1.240388  Topic20 -11.1757   2.5172\n",
       "16377  ground_wire      0.042318      1.240353  Topic20 -11.1758   2.5171\n",
       "10976      breaker      0.042317      1.240356  Topic20 -11.1759   2.5171\n",
       "3361       connect      0.042319      1.240435  Topic20 -11.1758   2.5171\n",
       "4701      physical      0.042317      1.240513  Topic20 -11.1759   2.5170\n",
       "\n",
       "[841 rows x 6 columns], token_table=      Topic      Freq      Term\n",
       "term                           \n",
       "2923     14  0.960158  abstract\n",
       "1211      3  0.998748    accept\n",
       "1004     16  0.978621  accuracy\n",
       "1578      1  0.035538    action\n",
       "1578      4  0.739474    action\n",
       "...     ...       ...       ...\n",
       "32        1  0.173767      year\n",
       "32        3  0.004200      year\n",
       "32        5  0.672298      year\n",
       "32        9  0.012601      year\n",
       "32       11  0.136847      year\n",
       "\n",
       "[1098 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 17, 19, 10, 16, 1, 5, 11, 20, 14, 12, 18, 9, 4, 13, 8, 3, 7, 15, 6])"
      ]
     },
     "execution_count": 19,
     "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": "markdown",
   "metadata": {
    "id": "lvtNAgXwJMFD"
   },
   "source": [
    "So how to infer pyLDAvis’s output?\n",
    "\n",
    "Each bubble on the left-hand side plot represents a topic. The larger the bubble, the more prevalent is that topic.\n",
    "\n",
    "A good topic model will have fairly big, non-overlapping bubbles scattered throughout the chart instead of being clustered in one quadrant.\n",
    "\n",
    "A model with too many topics, will typically have many overlaps, small sized bubbles clustered in one region of the chart.\n",
    "\n",
    "Alright, if you move the cursor over one of the bubbles, the words and bars on the right-hand side will update. These words are the salient keywords that form the selected topic.\n",
    "\n",
    "We have successfully built a good looking topic model.\n",
    "\n",
    "Given our prior knowledge of the number of natural topics in the document, finding the best model was fairly straightforward.\n",
    "\n",
    "Upnext, we will improve upon this model by using Mallet’s version of LDA algorithm and then we will focus on how to arrive at the optimal number of topics given any large corpus of text."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wKDDvZbFJMFE"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 16. Building LDA Mallet Model</h2>\n",
    "</div>\n",
    "\n",
    "So far you have seen Gensim’s inbuilt version of the LDA algorithm. Mallet’s version, however, often gives a better quality of topics.\n",
    "\n",
    "Gensim provides a wrapper to implement Mallet's LDA from within gensim itself. You only need to [download](http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip) the zipfile, unzip it and provide the path to mallet in the unzipped directory to `gensim.models.wrappers.LdaMallet`. See how I have done this below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ.update({'MALLET_HOME':r'C:/Users/Lenovo/Desktop/mallet-2.0.8'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "axMNiS5wJMFE"
   },
   "outputs": [],
   "source": [
    "## Download File: http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
    "mallet_path = 'C:/Users/Lenovo/Desktop/mallet-2.0.8/bin/mallet' # update this path\n",
    "ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=20, id2word=id2word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "vkmCE0EzJMFF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(14,\n",
      "  [('drive', 0.034160504468538765),\n",
      "   ('card', 0.026970013156103978),\n",
      "   ('problem', 0.026130744454021686),\n",
      "   ('system', 0.024928548745633536),\n",
      "   ('driver', 0.018804155514222202),\n",
      "   ('run', 0.017579276867939937),\n",
      "   ('work', 0.015242934264845983),\n",
      "   ('disk', 0.015220251326951867),\n",
      "   ('bit', 0.014040738556457832),\n",
      "   ('memory', 0.013723177425940208)]),\n",
      " (18,\n",
      "  [('line', 0.02928489135385687),\n",
      "   ('good', 0.028347131795407658),\n",
      "   ('buy', 0.026632371459957668),\n",
      "   ('price', 0.02585537068295689),\n",
      "   ('sell', 0.023953058435817055),\n",
      "   ('cost', 0.01596870562387804),\n",
      "   ('pay', 0.015459636149291321),\n",
      "   ('sale', 0.015191704846877261),\n",
      "   ('offer', 0.012753529994909306),\n",
      "   ('call', 0.012565978083219463)]),\n",
      " (6,\n",
      "  [('window', 0.020890399084636455),\n",
      "   ('image', 0.017891196560452134),\n",
      "   ('file', 0.015464096251863667),\n",
      "   ('program', 0.014597274713082071),\n",
      "   ('display', 0.013799798897403003),\n",
      "   ('software', 0.012187510835269234),\n",
      "   ('application', 0.012170174404493602),\n",
      "   ('version', 0.011996810096737283),\n",
      "   ('run', 0.01133802572726327),\n",
      "   ('color', 0.011112652127180055)]),\n",
      " (4,\n",
      "  [('game', 0.031754338224520125),\n",
      "   ('team', 0.030393737048123416),\n",
      "   ('year', 0.029535511690703953),\n",
      "   ('play', 0.025432775835723107),\n",
      "   ('player', 0.018964687166391058),\n",
      "   ('win', 0.016264417139388358),\n",
      "   ('good', 0.014485169447177275),\n",
      "   ('season', 0.01230820756494254),\n",
      "   ('run', 0.009838193121637745),\n",
      "   ('hit', 0.009419546605823373)]),\n",
      " (9,\n",
      "  [('car', 0.041619492985478714),\n",
      "   ('bike', 0.014250553777996553),\n",
      "   ('ride', 0.010952498154073344),\n",
      "   ('drive', 0.010066453359586513),\n",
      "   ('engine', 0.008663549101649027),\n",
      "   ('turn', 0.008048240216588728),\n",
      "   ('speed', 0.007605217819345311),\n",
      "   ('front', 0.0075806054639428995),\n",
      "   ('road', 0.007432931331528427),\n",
      "   ('mile', 0.007112970711297071)]),\n",
      " (19,\n",
      "  [('gun', 0.022815035249938),\n",
      "   ('people', 0.018227229248591773),\n",
      "   ('state', 0.015765047649413683),\n",
      "   ('government', 0.01417082934778758),\n",
      "   ('law', 0.009529882736387147),\n",
      "   ('job', 0.008927622489106175),\n",
      "   ('make', 0.00823679455840153),\n",
      "   ('crime', 0.008183653948347327),\n",
      "   ('year', 0.00811279980160839),\n",
      "   ('weapon', 0.007652247847805293)]),\n",
      " (15,\n",
      "  [('make', 0.05305789275398719),\n",
      "   ('thing', 0.04772070827577546),\n",
      "   ('time', 0.03884633094729792),\n",
      "   ('good', 0.037234710536230065),\n",
      "   ('work', 0.028381263342961194),\n",
      "   ('point', 0.026267319686885178),\n",
      "   ('problem', 0.024132445895600485),\n",
      "   ('give', 0.02331617062246222),\n",
      "   ('bad', 0.021369668048055592),\n",
      "   ('put', 0.016974339654234165)]),\n",
      " (0,\n",
      "  [('people', 0.02214538150568254),\n",
      "   ('drug', 0.013627927080420025),\n",
      "   ('man', 0.013144847575703644),\n",
      "   ('article', 0.0130431466273423),\n",
      "   ('make', 0.011644758587373827),\n",
      "   ('show', 0.01027179578449569),\n",
      "   ('write', 0.010195520073224683),\n",
      "   ('number', 0.010017543413592333),\n",
      "   ('find', 0.009636164857237294),\n",
      "   ('food', 0.009585314383056622)]),\n",
      " (11,\n",
      "  [('power', 0.01920617807085726),\n",
      "   ('ground', 0.012536669759045207),\n",
      "   ('line', 0.011283002783140686),\n",
      "   ('current', 0.010706315974224606),\n",
      "   ('high', 0.009277135621693453),\n",
      "   ('wire', 0.009151768924103002),\n",
      "   ('water', 0.00912669558458491),\n",
      "   ('work', 0.008123762003861295),\n",
      "   ('low', 0.008123762003861295),\n",
      "   ('light', 0.007547075194945215)]),\n",
      " (3,\n",
      "  [('space', 0.017264917771103318),\n",
      "   ('system', 0.011131881287937661),\n",
      "   ('year', 0.010438764151141543),\n",
      "   ('project', 0.010291739303942365),\n",
      "   ('launch', 0.009220558274348365),\n",
      "   ('program', 0.008275398542353658),\n",
      "   ('design', 0.006910167818361303),\n",
      "   ('technology', 0.006742139421562244),\n",
      "   ('cost', 0.006637121673562832),\n",
      "   ('base', 0.00653210392556342)])]\n",
      "\n",
      "Coherence Score:  0.5429767939089214\n"
     ]
    }
   ],
   "source": [
    "# Show Topics\n",
    "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": "markdown",
   "metadata": {
    "id": "9VklPkhfJMFG"
   },
   "source": [
    "Just by changing the LDA algorithm, we increased the coherence score from .448 to .54. Not bad!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QbLZKoXbJMFG"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 17. How to find the optimal number of topics for LDA?</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PFtNpYa_JMFG"
   },
   "source": [
    "My approach to finding the optimal number of topics is to build many LDA models with different values of number of topics (k) and pick the one that gives the highest coherence value.\n",
    "\n",
    "Choosing a ‘k’ that marks the end of a rapid growth of topic coherence usually offers meaningful and interpretable topics. Picking an even higher value can sometimes provide more granular sub-topics.\n",
    "\n",
    "If you see the same keywords being repeated in multiple topics, it’s probably a sign that the ‘k’ is too large.\n",
    "\n",
    "The `compute_coherence_values()` (see below) trains multiple LDA models and provides the models and their corresponding coherence scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "uMFCxJkwJMFG"
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "D0-aktieJMFG"
   },
   "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": 49,
   "metadata": {
    "id": "RL2BqYroJMFH"
   },
   "outputs": [
    {
     "data": {
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+8/gKOqd04t4rJ9Kpk0paRdoDlbnKSfvVXzewcts+HrhmCgN7d010OCLSQtSDkJOSX1TGr1/fyOVTcvj8hAHHPkFE2gwlCDlhBypruPnxFQzq05XbZ41JdDgi0sJ0i0lO2E//XMDO8kqe+MZ0MrpoASCR9kY9CDkhz63cwTPvb2fup0dyxtA+iQ5HREKgBCHHbce+w/zo2dVMHtKbb18wMtHhiEhIlCDkuNTVO999fAV19c4vvzKJ1BT9ExJpr0L9321mM81snZltNLNbY+yfY2YlZrYi+Pl61L7rzWxD8HN9mHFK/B58q5B3Npdx+6yxDM3snuhwRCREoQ1Sm1kK8ABwEVAMLDOzBe6+psmhj7v73Cbn9gV+CuQBDiwPzt0bVrxybB9sL+e+V9bxufH9mX1GTqLDEZGQhdmDmApsdPdCd68G5gOXxnnuZ4FX3L0sSAqvADNDilPicLi6jpvmv09m93R+cdl4LQAk0gGEmSAGAduitouDtqYuN7NVZvaUmQ0+nnPN7EYzyzez/JKSkpaKW2K4a+EaNu+p4L4rJ9K7W1qiwxGRVhBmgoj1FdObbD8H5Lr7BOBV4JHjOBd3f9Dd89w9Lzs7+6SClaN7dc0ufr90K/9wznBmjMxKdDgi0krCTBDFwOCo7RxgR/QB7l7q7lXB5m+BM+I9V1pHyYEqfvj0KsYM6MktF49OdDgi0orCTBDLgFFmNszM0oCrgAXRB5hZ9OQ9s4C1we8vARebWR8z6wNcHLRJK3J3fvDUSg5W1fIfV00iPVULAIl0JKFVMbl7rZnNJfLBngI85O4FZnYHkO/uC4CbzGwWUAuUAXOCc8vM7E4iSQbgDncvCytWie3RJVt4Y10Jd1w6llH9MhIdjoi0MnM/4tZ+m5SXl+f5+fmJDqPd2LDrAF/41d+YMSKTh+acqaolkXbKzJa7e16sfXoMVo5QVVvHTfNX0CM9lbtnT1RyEOmgNJurHOHel9ezdud+/uf6PLIz0hMdjogkiHoQ8gmLNu7hwbcKuXbaED5zer9EhyMiCaQEIY32HarmlidWMiK7Oz/6nBYAEunolCAEiJS0/suzqymtqOI/rppM1zSVtIp0dEoQAsBTy4tZuPojvnfRqYwb1CvR4YhIEogrQZhZVzM7NexgJDG2lFZw+4ICpg3vy43nDk90OCKSJI6ZIMzsi8AK4MVge5KZLWj+LGkrauvqufnxFaR0Mu67chIpnVTSKiIR8fQgbicydfc+AHdfAeSGF5K0pl+9tpH3t+7jrsvGM7B310SHIyJJJJ4EUevu5aFHIq1u+ZYyfvXaBr48ZRBfnDgw0eGISJKJ50G5D8zsGiDFzEYBNwGLww1LwnagsoabH1/BoD5d+dmssYkOR0SSUDw9iG8DY4Eq4A9AOXBzmEFJ+G5fsIbtew9z/5WTyOjSOdHhiEgSarYHEawr/TN3/wHwo9YJScL2wqqdPP1eMTddMJK83L6JDkdEklSzPQh3r+PjRXykHdix7zC3PbOKSYN78+3PjEp0OCKSxOIZg3g/KGt9EqhoaHT3Z0KLSkJRX+/c8sRKauudX35lEp1T9JykiBxdPAmiL1AKXBDV5oASRBvz27cLWVJYyt2XTyA3q3uiwxGRJHfMBOHuN7RGIBKuD7aXc8/L65g5tj9X5OUkOhwRaQPieZI6x8yeNbPdZrbLzJ42s7g+YcxsppmtM7ONZnZrM8fNNjM3s7xgu7OZPWJmq81srZndFv9fSZo6XF3Hd+a/T9/uafzfL4/XAkAiEpd4bkI/DCwABgKDgOeCtmYFFVAPAJcAY4CrzeyIOaTNLIPIsxXvRDVfAaS7+3gig+TfMLPcOGKVGH6xcC2bSiq494pJ9OmeluhwRKSNiCdBZLv7w+5eG/zMA7LjOG8qsNHdC929GpgPXBrjuDuBu4HKqDYHuptZKtAVqAb2x/Ga0sRrH+7isaVb+PrZwzh7VFaiwxGRNiSeBLHHzK41s5Tg51oig9bHMgjYFrVdHLQ1MrPJwGB3f77JuU8RqZjaCWwF7nH3sqYvYGY3mlm+meWXlJTEEVLHUnKgin9+ahWn9c/gBzM1Ga+IHJ94EsTfAVcCHxH5wJ4dtB1LrBvd3rjTrBNwP3BLjOOmAnVEbmsNA24xsyPmoXb3B909z93zsrPj6dR0HO7OPz+1kgOVtfzn1ZNJT9UCQCJyfOKpYtoKzDqBaxcDg6O2c4AdUdsZwDjgjWDQtD+wwMxmAdcAL7p7DbDbzBYBeUDhCcTRIf1+6RZeX1fC7V8cw+h+GYkOR0TaoHiqmB4xs95R233M7KE4rr0MGGVmw8wsDbiKyGA3AO5e7u5Z7p7r7rnAUmCWu+cTua10gUV0B6YBHx7X36wD27j7AD9/YS3njc7m+hm5iQ5HRNqoeG4xTXD3fQ0b7r4XmHysk9y9FpgLvASsBZ5w9wIzuyPoJTTnAaAH8AGRRPOwu6+KI9YOr6q2jpv+uILu6an8+xUTVNIqIicsniepO5lZnyAxYGZ94zwPd18ILGzS9pOjHHt+1O8HiZS6ynG67+X1rNm5n99dl8cpGV0SHY6ItGHxfNDfCyw2s6eC7SuAu8ILSU7U8i17efDtQq45awgXjumX6HBEpI2LZ5D6UTPLJzIXkwFfdvc1oUcmx+03b26id9fO/Pjzpyc6FBFpB+IZpB4BbHL3XwOrgQujB60lOWwrO8Sra3dxzVlD6JYW1x1AEZFmxTNI/TRQZ2Yjgd8ReS7hD6FGJcftsaVbMDOunTY00aGISDsRT4KoDyqSvgz8h7t/FxgQblhyPA5V1zL/3a3MHNefAb26JjocEWkn4kkQNWZ2NXAd0DAlhhYxTiLPvr+d/ZW13KBnHkSkBcWTIG4ApgN3uftmMxsG/D7csCRe7s68RUWMG9STM4b2SXQ4ItKOxFPFtIbIdNwN25uBfw0zKInf4k2lbNh9kHuumKiH4kSkRWlR4jbu4UVFZHZP4wsTNCwkIi1LCaIN21p6iL9+GClt7dJZs7WKSMuKO0EEk+ZJEnl0SREpZnz1LJW2ikjLi+dBuRlmtobIhHuY2UQz+3+hRybNqqiq5fH8bVwyfgD9e2nOJRFpefH0IO4HPkuwipy7rwTODTMoObZn3t/Ogcpa5qi0VURCEtctJnff1qSpLoRYJE6R0tbNTMjpxZQhmvVERMIRT4LYZmYzADezNDP7PsHtJkmMv23cw6aSCubMyFVpq4iEJp4E8U3gW8AgIsuITgq2JUHmLSoiq0can1dpq4iEKJ4H5fYAX22FWCQORXsqeG3dbr59wSjSU1XaKiLhCXNNasxsppmtM7ONZnZrM8fNNjM3s7yotglmtsTMCsxstZmpVAd4dMkWUsy49qwhiQ5FRNq5eBYOOGJNajM75prUZpZCZG3pi4jcmlpmZguaLjZkZhlEpvJ4J6otlch8T19z95VmlgnUxPMXas8OVtXyZP42Pj9hAKf0VL4UkXDFMwbRycwaZ4E7jjWppwIb3b3Q3auB+cClMY67E7gbqIxquxhYFZTU4u6l7t7hK6eeea+YA1UqbRWR1hFPgmhYk/pOM7sTWEzkA/1YBgHR5bHFQVujoCcy2N2f55NGE6maesnM3jOzf471AmZ2o5nlm1l+SUlJHCG1XfX1zrzFRUwc3JvJQzRrq4iE75gJwt0fBWYDu4DdRNakfiyOa8eqv/TGnWadiDyEd0uM41KBs4kMjp8NXGZmn4kR24PunufuednZ2XGE1Ha9vXEPhSUVWvNBRFpNvIsXfwjsbTjezIa4+9ZjnFMMDI7azgF2RG1nAOOAN4Ja/v7AAjObFZz7ZlBBhZktBKYAf40z3nZn3qLNZGek87nxKm0VkdYRTxXTt4n0Hl4hsqLcC3y8slxzlgGjzGyYmaUBVwELGna6e7m7Z7l7rrvnAkuBWe6eD7wETDCzbsGA9XnAmiNfomMoLDnI6+tK+OpZQ0hL1QS8ItI64ulBfAc41d1Lj+fC7l5rZnOJfNinAA+5e4GZ3QHku/uCZs7da2b3EUkyDix09xeO5/Xbk0eXbKFzinGNSltFpBXFkyC2AeUncnF3XwgsbNL2k6Mce36T7d+jpU05UFnDk/nb+MKEgZySodJWEWk98SSIQiLjBC8AVQ2N7n5faFFJo6eWF1NRXafSVhFpdfEkiK3BT1rwI62kvt55ZHERk4f0ZuJgzdoqIq0rnrmYfgaRFeXcvSL8kKTBm+tLKCo9xPcuPjXRoYhIBxRPFdN0rSiXGA8vLqJfz3QuGdc/0aGISAcUT83kL9GKcq1u4+6DvLW+hGvPGkrnFJW2ikjr04pySerRJUWkpXTiapW2ikiCxFXmGr2iHJGZV7WiXIj2V9bw1PJivjhxIFk90hMdjoh0UFpRLgk9mV/MIZW2ikiCNduDCNZ0+Jq7a0W5VlIXlLbmDe3D+JxeiQ5HRDqwZnsQwRoMsdZwkJC8sW43W8sOMedTuYkORUQ6uHjGIBaZ2a+Bx4HG5yDc/b3QourA5i0uon/PLnx2rEpbRSSx4kkQM4I/74hqc+CClg+nY9uw6wBvb9jDDz57qkpbRSTh4nmS+tOtEYjAI0uKSEvtxFVnDj7msSIiYYvnSep+ZvY/ZvaXYHuMmf19+KF1LOWHa3h6+XYunTiQTJW2ikgSiOc+xjwiazoMDLbXAzeHFVBH9WT+Ng7X1HG9SltFJEnEkyCy3P0JoB4iCwGhJ6lbVF2988iSIqbm9mXcIJW2ikhyiCdBVJhZJpGBacxsGie4gJDE9tqHu9lWdlilrSKSVOJJEN8jspb0CDNbBDwKfDuei5vZTDNbZ2YbzezWZo6bbWZuZnlN2oeY2UEz+348r9dWzVu8mQG9unDxmH6JDkVEpFE8VUzvmdl5wKmAAevcveZY5wVPYT8AXERkio5lZrbA3dc0OS6DyPxO78S4zP3AX475t2jD1u86wKKNpfzzzFNJVWmriCSReD+RpgITgSnA1WZ2XZznbHT3QnevBuYT+6nsO4G7gcroRjP7EpHlTgvijLFNmre4iPTUTlx1pmZtFZHkEk+Z62PAPcDZwJnBT16zJ0UMAqKnCS8O2qKvPRkY7O7PN2nvDvwQ+NkxYrvRzPLNLL+kpCSOkJJL+aEannmvmC9NGkTf7lrNVUSSSzxPUucBY9zdj/PaFqOt8Rpm1onILaQ5MY77GXC/ux80i3WZ4GLuDwIPAuTl5R1vfAn3eP5WKmvqVdoqIkkpngTxAdAf2Hmc1y4Goh8JzgF2RG1nAOOAN4Ik0B9YYGazgLOA2WZ2N9AbqDezSnf/9XHGkLQis7Zu4axhfRkzsGeiwxEROcJRE4SZPUfkG38GsMbM3gWqGva7+6xjXHsZMMrMhgHbgauAa6LOLweyol7vDeD77p4PnBPVfjtwsD0lB4BX1+5i+77D/J8vnJ7oUEREYmquB3HPyVzY3WvNbC6Rp7BTgIfcvcDM7gDy3X3ByVy/rZu3qIhBvbty4ekqbRWR5HTUBOHubzb8bmb9iAxOA7zr7rvjubi7LwQWNmn7yVGOPf8o7bfH81ptyYcf7WdJYSm3XnKaSltFJGnFU8V0JfAucAVwJfCOmc0OO7D27JHFRXTprFlbRSS5xTNI/SPgzIZeg5llA68CT4UZWHu1t6KaZ9/fzmWTB9G7m0pbRSR5xXN/o1OTW0qlcZ4nMTyev02lrSLSJsTTg3jRzF4C/hhsf4V2Pv1FWGrr6nl0cRHTh2dyWn+VtopIcotnLqYfmNmXiTxJbcCD7v5s6JG1Q6+s2cWO8kp+OmtsokMRETmm5p6DGAn0c/dF7v4M8EzQfq6ZjXD3Ta0VZHvx8GKVtopI29HcWMIvgQMx2g8F++Q4FOwo593NZVw/YygpnY4+fYiISLJoLkHkuvuqpo3Bk865oUXUTj2yuIiunVP4Sp5mbRWRtqG5BNGlmX1dWzqQ9qysopo/rdjBZVMG0atb50SHIyISl+YSxDIz+4emjWb298Dy8EJqf/747laqa+uZo9JWEWlDmqtiuhl41sy+yscJIQ9IAy4LO7D2oqaunt8v3cLZI7MY3S8j0eGIiMStubmYdgEzzOzTRKblBnjB3V9rlcjaiZcLdrGzvJI7Lx137INFRJJIPM9BvJAkdDIAAA54SURBVA683gqxtEvzFm9mSN9ufPq0UxIdiojIcdGUGSH6YHs5y4r2ct10lbaKSNujBBGieYuL6JaWwhV5mrVVRNoeJYiQ7DlYxYIVO7h8Sg69uqq0VUTaHiWIkMx/dyvVdfVcP2NookMRETkhoSYIM5tpZuvMbKOZ3drMcbPNzM0sL9i+yMyWm9nq4M8LwoyzpdXU1fPY0i2cMyqLkaeotFVE2qbQEoSZpQAPAJcAY4CrzWxMjOMygJuAd6Ka9wBfdPfxwPXAY2HFGYYXP/iIXfuruOFTuYkORUTkhIXZg5gKbHT3QnevBuYDl8Y47k7gbqCyocHd33f3HcFmAdDFzNJDjLVFzVtcxNDMbpw/WqWtItJ2hZkgBgHboraLg7ZGZjYZGOzuzzdzncuB9929qukOM7vRzPLNLL+kpKQlYj5pq4r3sXzLXq6fnksnlbaKSBsWZoKI9enojTvNOgH3A7cc9QJmY4F/A74Ra7+7P+juee6el52dfZLhtox5i4vonpbC7LycRIciInJSwkwQxUD0AwA5wI6o7QwiU3i8YWZFwDRgQdRAdQ7wLHBdW1mcqORAFc+v3MnsM3Lo2UWlrSLStoWZIJYBo8xsmJmlAVcBCxp2unu5u2e5e6675wJLgVnunm9mvYEXgNvcfVGIMbaoPwalrddp1lYRaQdCSxDuXgvMBV4C1gJPuHuBmd1hZrOOcfpcYCTwf8xsRfCT1CO+1bWRWVvPG53NiOweiQ5HROSkHXOyvpPh7guBhU3afnKUY8+P+v3nwM/DjK2l/eWDnew+UMW/zc5NdCgiIi1CT1K3kHmLixiW1Z3zRiXHYLmIyMlSgmgBK7bt4/2t+7h++lCVtopIu6EE0QIeWVxEj/RULj9Dpa0i0n4oQZyk3QcqeX7VDmafkUOGSltFpB1RgjhJf3hnKzV1zvUqbRWRdkYJ4iRESlu38ulTsxmW1T3R4YiItCgliJOwcPVO9hysYs6nhiU6FBGRFqcEcRIeXlzE8OzunDMyK9GhiIi0OCWIE/Te1r2s3LaPOTM0a6uItE9KECdo3qIiMtJT+fIUlbaKSPukBHECdu2vZOHqnVyRN5ge6aHOViIikjBKECfgf5duoc6d66YPTXQoIiKhUYI4TlW1dfzvO1u54NRTyFVpq4i0Y0oQx+n5lTsprahmzqdyEx2KiEiolCCOg7szb3ERI0/pwdkqbRWRdk4J4ji8t3Uvq7eXc/2MXMxU2ioi7ZsSxHF4eFERGV1S+fLkQYkORUQkdKEmCDObaWbrzGyjmd3azHGzzczNLC+q7bbgvHVm9tkw44zHzvLD/OWDj7jqzMF0V2mriHQAoX3SmVkK8ABwEVAMLDOzBe6+pslxGcBNwDtRbWOAq4CxwEDgVTMb7e51YcV7LP+7dCv17lw3PTdRIYiItKowexBTgY3uXuju1cB84NIYx90J3A1URrVdCsx39yp33wxsDK6XEJU1dfzh3a1ceHo/BvftlqgwRERaVZgJYhCwLWq7OGhrZGaTgcHu/vzxnhucf6OZ5ZtZfklJSctEHcNzK3dQVlHNDVrzQUQ6kDATRKwyH2/cadYJuB+45XjPbWxwf9Dd89w9Lzs7+4QDbU5Daevofj2YPiIzlNcQEUlGYSaIYmBw1HYOsCNqOwMYB7xhZkXANGBBMFB9rHNbTf6WvRTs2M+cGcNU2ioiHUqYCWIZMMrMhplZGpFB5wUNO9293N2z3D3X3XOBpcAsd88PjrvKzNLNbBgwCng3xFiPat6iInp17cyXJg9MxMuLiCRMaFVM7l5rZnOBl4AU4CF3LzCzO4B8d1/QzLkFZvYEsAaoBb6ViAqmHfsO82LBR3z97GF0S1Npq4h0LKF+6rn7QmBhk7afHOXY85ts3wXcFVpwcfj90i24O9dO06ytItLx6Enqo6isqeOP727lojEqbRWRjkkJ4igWrNjB3kM1zJkxLNGhiIgkhBJEDO7Ow4uLOK1/BtOG9010OCIiCaEEEcO7m8tYu3M/czRrq4h0YEoQMcxbXETvbp25dJJmbRWRjksJoont+w7zUsFHXHXmELqmpSQ6HBGRhFGCaOKxJVsA+Np0lbaKSMemBBHlcHUd85dt5bNj+zOod9dEhyMiklBKEFH+vGI7+w7VMEeztoqIKEE0aJi19fQBPZk6TKWtIiJKEIGlhWV8+NEBblBpq4gIoATRaN7izfTp1plZkzRrq4gIKEEAsK3sEK+s2cXVU4fQpbNKW0VEQAkCgMeWbsHMNGuriEiUDp8gDlXXMv/drcwc25+BKm0VEWnU4VfBOVBZyzmjs7lBpa0iIp/Q4RNEv55deOCaKYkOQ0Qk6YR6i8nMZprZOjPbaGa3xtj/TTNbbWYrzOxvZjYmaO9sZo8E+9aa2W1hxikiIkcKLUGYWQrwAHAJMAa4uiEBRPmDu49390nA3cB9QfsVQLq7jwfOAL5hZrlhxSoiIkcKswcxFdjo7oXuXg3MBy6NPsDd90dtdge8YRfQ3cxSga5ANRB9rIiIhCzMBDEI2Ba1XRy0fYKZfcvMNhHpQdwUND8FVAA7ga3APe5eFuPcG80s38zyS0pKWjp+EZEOLcwEEWu+Cj+iwf0Bdx8B/BD4cdA8FagDBgLDgFvMbHiMcx909zx3z8vOzm65yEVEJNQEUQwMjtrOAXY0c/x84EvB79cAL7p7jbvvBhYBeaFEKSIiMYWZIJYBo8xsmJmlAVcBC6IPMLNRUZufBzYEv28FLrCI7sA04MMQYxURkSZCew7C3WvNbC7wEpACPOTuBWZ2B5Dv7guAuWZ2IVAD7AWuD05/AHgY+IDIraqH3X1VWLGKiMiRzP2IYYE2ycxKgC2JjuMYsoA9iQ4iDm0lTmg7sSrOltVW4oTkj3Wou8ccxG03CaItMLN8d0/6sZS2Eie0nVgVZ8tqK3FC24q1qQ4/WZ+IiMSmBCEiIjEpQbSuBxMdQJzaSpzQdmJVnC2rrcQJbSvWT9AYhIiIxKQehIiIxKQEISIiMSlBtBIzK4pa+yI/0fE0MLOHzGy3mX0Q1dbXzF4xsw3Bn30SGWMQU6w4bzez7cF7usLMPpfIGIOYBpvZ68E6JgVm9p2gPane02biTMb3tIuZvWtmK4NYfxa0DzOzd4L39PFgxoZkjHOemW2Oek8nJTLO46ExiFZiZkVAnrsn1QMzZnYucBB41N3HBW13A2Xu/q/BQk993P2HSRjn7cBBd78nkbFFM7MBwAB3f8/MMoDlROYYm0MSvafNxHklyfeeGtDd3Q+aWWfgb8B3gO8Bz7j7fDP7b2Clu/9XEsb5TeB5d38qUbGdKPUgOjh3fwtoOpX6pcAjwe+P8PEkiglzlDiTjrvvdPf3gt8PAGuJTHOfVO9pM3EmHY84GGx2Dn4cuIDI0gCQHO/p0eJss5QgWo8DL5vZcjO7MdHBHEM/d98JkQ8S4JQEx9OcuWa2KrgFlfBbYdGCVRAnA++QxO9pkzghCd9TM0sxsxXAbuAVYBOwz91rg0NirjfT2prG6e4N7+ldwXt6v5mlJzDE46IE0Xo+5e5TiCzB+q3glomcnP8CRgCTiCwudW9iw/mYmfUAngZubrJyYlKJEWdSvqfuXhcsTZxDZL2Y02Md1rpRxQigSZxmNg64DTgNOBPoS2TtmzZBCaKVuPuO4M/dwLNE/pEnq13BPeqGe9W7ExxPTO6+K/gPWQ/8liR5T4P7z08D/+vuzwTNSfeexoozWd/TBu6+D3iDyBIAvS2yLDEce72ZVhUV58zgdp67exWRWaqT6j1tjhJEKzCz7sFAIMH6FhcTmco8WS3g46nXrwf+nMBYjqrhAzdwGUnwngYDlf8DrHX3+6J2JdV7erQ4k/Q9zTaz3sHvXYELiYyZvA7MDg5Lhvc0VpwfRn0xMCLjJAl/T+OlKqZWYJHlUp8NNlOBP7j7XQkMqZGZ/RE4n8iUxLuAnwJ/Ap4AhhBZvOmKWGuCt6ajxHk+kVshDhQB32i4z58oZnY28DawGqgPmv+FyP39pHlPm4nzapLvPZ1AZBA6hciX2ifc/Y7g/9V8Irdt3geuDb6lJ1ucrwHZRNa2WQF8M2owO6kpQYiISEy6xSQiIjEpQYiISExKECIiEpMShIiIxKQEISIiMSlBSIdkZm5m90Ztfz+Y/K8lX+OGqBk8q+3j2Xz/9QSuNdjMHm/J+ESORWWu0iGZWSWRqSTOdPc9ZvZ9oIe73x7S6xWRhLP5ijRHPQjpqGqJrBX83aY7gvn7Z0dtHwz+PN/M3jSzJ8xsvZn9q5l9NVgDYLWZjYj3xc0sy8wWBBO4LQ7m7MHMfm5mj1hkrYYNZvZ3QfvIYBI4zCw1mPTtg+D8fwra/93M1gRt/3Yyb44IRJ7qFemoHgBWBetfxGsikYniyoBC4HfuPtUiC+58G7g5zuvcCbzj7rPM7GJgHpAX7BsPzAB6Au+Z2QtNzv1HYCAw0d3rLLIYUT/gc8BYd/eGKR9EToZ6ENJhBbOXPgrcdBynLQsmX6siMuX0y0H7aiD3OK5zNvBYEMfLwMBgni6AP7l7ZTCx41tEZgGNdiHw3+5eF5xfRiRh1QO/NbPLgIrjiEUkJiUI6eh+Cfw90D2qrZbg/0YwwVr0UpbRc/3UR23Xc3w9cmtmu+nAYNNta9rm7jVEeiB/Ai4HmvY6RI6bEoR0aMG37yeIJIkGRcAZwe+XElkZrKW9BXwVwMwuBIrdveFb/5fMLN3MsoBzgKZrmL8M/KOZpQTn9w1mC+7p7s8TGVeZHELM0sFoDEIksijO3Kjt3wJ/NrN3gb8Szu2anwAPm9kqImtt3xC1bxnwF2Aw8FN339UwXXzgN8AoIuMntUQW+XkeeCZYrawTkfWaRU6KylxFkoiZ/RzY4+6/THQsIrrFJCIiMakHISIiMakHISIiMSlBiIhITEoQIiISkxKEiIjEpAQhIiIx/X+CrrbMdWNa0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show graph\n",
    "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": 50,
   "metadata": {
    "id": "SRYNvTVRJMFH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Topics = 2  has Coherence Value of 0.3838\n",
      "Num Topics = 8  has Coherence Value of 0.475\n",
      "Num Topics = 14  has Coherence Value of 0.5303\n",
      "Num Topics = 20  has Coherence Value of 0.5284\n",
      "Num Topics = 26  has Coherence Value of 0.5365\n",
      "Num Topics = 32  has Coherence Value of 0.5257\n",
      "Num Topics = 38  has Coherence Value of 0.5357\n"
     ]
    }
   ],
   "source": [
    "# Print the coherence scores\n",
    "for m, cv in zip(x, coherence_values):\n",
    "    print(\"Num Topics =\", m, \" has Coherence Value of\", round(cv, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JYI58TziJMFI"
   },
   "source": [
    "If the coherence score seems to keep increasing, it may make better sense to pick the model that gave the highest CV before flattening out. This is exactly the case here.\n",
    "\n",
    "So for further steps I will choose the model with 20 topics itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "JS53wUsOJMFI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.041*\"file\" + 0.020*\"image\" + 0.019*\"program\" + 0.016*\"include\" + '\n",
      "  '0.016*\"software\" + 0.015*\"version\" + 0.012*\"system\" + 0.012*\"source\" + '\n",
      "  '0.010*\"format\" + 0.010*\"graphic\"'),\n",
      " (1,\n",
      "  '0.041*\"car\" + 0.014*\"bike\" + 0.014*\"buy\" + 0.011*\"ride\" + 0.009*\"drive\" + '\n",
      "  '0.009*\"price\" + 0.009*\"engine\" + 0.008*\"turn\" + 0.008*\"speed\" + '\n",
      "  '0.007*\"sell\"'),\n",
      " (2,\n",
      "  '0.020*\"man\" + 0.018*\"people\" + 0.014*\"life\" + 0.014*\"love\" + 0.010*\"church\" '\n",
      "  '+ 0.009*\"world\" + 0.009*\"die\" + 0.009*\"religion\" + 0.009*\"christian\" + '\n",
      "  '0.008*\"live\"'),\n",
      " (3,\n",
      "  '0.026*\"gun\" + 0.022*\"law\" + 0.021*\"state\" + 0.012*\"people\" + '\n",
      "  '0.012*\"government\" + 0.010*\"crime\" + 0.010*\"case\" + 0.009*\"weapon\" + '\n",
      "  '0.008*\"firearm\" + 0.007*\"public\"'),\n",
      " (4,\n",
      "  '0.057*\"good\" + 0.047*\"time\" + 0.029*\"lot\" + 0.023*\"find\" + 0.023*\"make\" + '\n",
      "  '0.022*\"work\" + 0.021*\"thing\" + 0.019*\"long\" + 0.016*\"book\" + 0.015*\"great\"'),\n",
      " (5,\n",
      "  '0.033*\"drive\" + 0.027*\"card\" + 0.022*\"system\" + 0.019*\"problem\" + '\n",
      "  '0.018*\"driver\" + 0.014*\"disk\" + 0.013*\"work\" + 0.013*\"speed\" + '\n",
      "  '0.012*\"computer\" + 0.012*\"run\"'),\n",
      " (6,\n",
      "  '0.019*\"power\" + 0.012*\"ground\" + 0.012*\"high\" + 0.011*\"current\" + '\n",
      "  '0.009*\"wire\" + 0.009*\"line\" + 0.009*\"low\" + 0.009*\"unit\" + 0.008*\"light\" + '\n",
      "  '0.008*\"work\"'),\n",
      " (7,\n",
      "  '0.014*\"people\" + 0.014*\"armenian\" + 0.011*\"government\" + 0.010*\"israeli\" + '\n",
      "  '0.010*\"attack\" + 0.010*\"war\" + 0.009*\"kill\" + 0.008*\"turkish\" + '\n",
      "  '0.007*\"state\" + 0.007*\"land\"'),\n",
      " (8,\n",
      "  '0.058*\"people\" + 0.048*\"make\" + 0.028*\"thing\" + 0.018*\"good\" + 0.018*\"bad\" '\n",
      "  '+ 0.015*\"give\" + 0.015*\"talk\" + 0.014*\"feel\" + 0.014*\"point\" + '\n",
      "  '0.013*\"problem\"'),\n",
      " (9,\n",
      "  '0.037*\"key\" + 0.014*\"encryption\" + 0.011*\"government\" + 0.011*\"system\" + '\n",
      "  '0.011*\"chip\" + 0.011*\"bit\" + 0.010*\"entry\" + 0.009*\"technology\" + '\n",
      "  '0.009*\"file\" + 0.009*\"public\"'),\n",
      " (10,\n",
      "  '0.036*\"window\" + 0.027*\"problem\" + 0.023*\"line\" + 0.019*\"set\" + '\n",
      "  '0.016*\"call\" + 0.014*\"application\" + 0.014*\"work\" + 0.014*\"change\" + '\n",
      "  '0.013*\"screen\" + 0.013*\"display\"'),\n",
      " (11,\n",
      "  '0.265*\"write\" + 0.186*\"article\" + 0.114*\"line\" + 0.040*\"organization\" + '\n",
      "  '0.022*\"read\" + 0.015*\"hear\" + 0.014*\"opinion\" + 0.010*\"host\" + '\n",
      "  '0.007*\"remember\" + 0.007*\"news\"'),\n",
      " (12,\n",
      "  '0.010*\"study\" + 0.010*\"food\" + 0.009*\"problem\" + 0.008*\"doctor\" + '\n",
      "  '0.008*\"year\" + 0.008*\"day\" + 0.008*\"patient\" + 0.008*\"eat\" + 0.007*\"effect\" '\n",
      "  '+ 0.007*\"test\"'),\n",
      " (13,\n",
      "  '0.025*\"question\" + 0.019*\"claim\" + 0.019*\"true\" + 0.015*\"exist\" + '\n",
      "  '0.013*\"word\" + 0.012*\"argument\" + 0.012*\"evidence\" + 0.012*\"answer\" + '\n",
      "  '0.012*\"reason\" + 0.011*\"truth\"'),\n",
      " (14,\n",
      "  '0.015*\"space\" + 0.014*\"year\" + 0.012*\"work\" + 0.011*\"cost\" + '\n",
      "  '0.010*\"program\" + 0.008*\"launch\" + 0.008*\"project\" + 0.006*\"money\" + '\n",
      "  '0.006*\"plan\" + 0.006*\"job\"'),\n",
      " (15,\n",
      "  '0.019*\"system\" + 0.013*\"science\" + 0.009*\"point\" + 0.009*\"moral\" + '\n",
      "  '0.009*\"theory\" + 0.009*\"make\" + 0.008*\"case\" + 0.008*\"result\" + '\n",
      "  '0.008*\"base\" + 0.008*\"important\"'),\n",
      " (16,\n",
      "  '0.035*\"mail\" + 0.030*\"post\" + 0.025*\"send\" + 0.021*\"information\" + '\n",
      "  '0.020*\"list\" + 0.020*\"group\" + 0.019*\"address\" + 0.015*\"message\" + '\n",
      "  '0.015*\"email\" + 0.013*\"internet\"'),\n",
      " (17,\n",
      "  '0.016*\"start\" + 0.016*\"people\" + 0.015*\"time\" + 0.015*\"happen\" + '\n",
      "  '0.015*\"day\" + 0.012*\"leave\" + 0.010*\"fire\" + 0.010*\"child\" + 0.009*\"hear\" + '\n",
      "  '0.009*\"home\"'),\n",
      " (18,\n",
      "  '0.364*\"ax\" + 0.146*\"line\" + 0.078*\"host\" + 0.044*\"organization\" + '\n",
      "  '0.027*\"reply\" + 0.026*\"nntp_posting\" + 0.010*\"advance\" + 0.008*\"keyword\" + '\n",
      "  '0.007*\"originator\" + 0.006*\"_\"'),\n",
      " (19,\n",
      "  '0.032*\"game\" + 0.031*\"team\" + 0.029*\"year\" + 0.026*\"play\" + 0.019*\"player\" '\n",
      "  '+ 0.018*\"win\" + 0.012*\"good\" + 0.012*\"season\" + 0.010*\"hit\" + 0.009*\"run\"')]\n"
     ]
    }
   ],
   "source": [
    "# Select the model and print the topics\n",
    "optimal_model = model_list[3]\n",
    "model_topics = optimal_model.show_topics(formatted=False)\n",
    "pprint(optimal_model.print_topics(num_words=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aqEb9HL-JMFI"
   },
   "source": [
    "Those were the topics for the chosen LDA model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8elTXI8hJMFJ"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 18. Finding the dominant topic in each sentence</h2>\n",
    "</div>\n",
    "\n",
    "One of the practical application of topic modeling is to determine what topic a given document is about.\n",
    "\n",
    "To find that, we find the topic number that has the highest percentage contribution in that document.\n",
    "\n",
    "The `format_topics_sentences()` function below nicely aggregates this information in a presentable table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "2va-5pS2JMFJ"
   },
   "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>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1836</td>\n",
       "      <td>car, bike, buy, ride, drive, price, engine, tu...</td>\n",
       "      <td>From: (where s my thing) Subject: WHAT car is ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1491</td>\n",
       "      <td>drive, card, system, problem, driver, disk, wo...</td>\n",
       "      <td>From: (Guy Kuo) Subject: SI Clock Poll - Final...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1176</td>\n",
       "      <td>drive, card, system, problem, driver, disk, wo...</td>\n",
       "      <td>From: (Thomas E Willis) Subject: PB questions....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>drive, card, system, problem, driver, disk, wo...</td>\n",
       "      <td>From: (Joe Green) Subject: Re: Weitek P9000 ? ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.1588</td>\n",
       "      <td>window, problem, line, set, call, application,...</td>\n",
       "      <td>From: (Jonathan McDowell) Subject: Re: Shuttle...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3454</td>\n",
       "      <td>gun, law, state, people, government, crime, ca...</td>\n",
       "      <td>From: (Foxvog Douglas) Subject: Re: Rewording ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.1024</td>\n",
       "      <td>mail, post, send, information, list, group, ad...</td>\n",
       "      <td>From: (brian manning delaney) Subject: Brain T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.4401</td>\n",
       "      <td>drive, card, system, problem, driver, disk, wo...</td>\n",
       "      <td>From: (GRUBB) Subject: Re: IDE vs SCSI Organiz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>window, problem, line, set, call, application,...</td>\n",
       "      <td>From: Subject: WIn 3.0 ICON HELP PLEASE! Organ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.2136</td>\n",
       "      <td>drive, card, system, problem, driver, disk, wo...</td>\n",
       "      <td>From: (Stan Kerr) Subject: Re: Sigma Designs D...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Document_No  Dominant_Topic  Topic_Perc_Contrib  \\\n",
       "0            0             1.0              0.1836   \n",
       "1            1             5.0              0.1491   \n",
       "2            2             5.0              0.1176   \n",
       "3            3             5.0              0.0926   \n",
       "4            4            10.0              0.1588   \n",
       "5            5             3.0              0.3454   \n",
       "6            6            16.0              0.1024   \n",
       "7            7             5.0              0.4401   \n",
       "8            8            10.0              0.0995   \n",
       "9            9             5.0              0.2136   \n",
       "\n",
       "                                            Keywords  \\\n",
       "0  car, bike, buy, ride, drive, price, engine, tu...   \n",
       "1  drive, card, system, problem, driver, disk, wo...   \n",
       "2  drive, card, system, problem, driver, disk, wo...   \n",
       "3  drive, card, system, problem, driver, disk, wo...   \n",
       "4  window, problem, line, set, call, application,...   \n",
       "5  gun, law, state, people, government, crime, ca...   \n",
       "6  mail, post, send, information, list, group, ad...   \n",
       "7  drive, card, system, problem, driver, disk, wo...   \n",
       "8  window, problem, line, set, call, application,...   \n",
       "9  drive, card, system, problem, driver, disk, wo...   \n",
       "\n",
       "                                                Text  \n",
       "0  From: (where s my thing) Subject: WHAT car is ...  \n",
       "1  From: (Guy Kuo) Subject: SI Clock Poll - Final...  \n",
       "2  From: (Thomas E Willis) Subject: PB questions....  \n",
       "3  From: (Joe Green) Subject: Re: Weitek P9000 ? ...  \n",
       "4  From: (Jonathan McDowell) Subject: Re: Shuttle...  \n",
       "5  From: (Foxvog Douglas) Subject: Re: Rewording ...  \n",
       "6  From: (brian manning delaney) Subject: Brain T...  \n",
       "7  From: (GRUBB) Subject: Re: IDE vs SCSI Organiz...  \n",
       "8  From: Subject: WIn 3.0 ICON HELP PLEASE! Organ...  \n",
       "9  From: (Stan Kerr) Subject: Re: Sigma Designs D...  "
      ]
     },
     "execution_count": 52,
     "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.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pRwcvOu2JMFK"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 19. Find the most representative document for each topic</h2>\n",
    "</div>\n",
    "\n",
    "Sometimes just the topic keywords may not be enough to make sense of what a topic is about. So, to help with understanding the topic, you can find the documents a given topic has contributed to the most and infer the topic by reading that document. Whew!!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "QsU-DC9_JMFK",
    "scrolled": true
   },
   "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>0.7107</td>\n",
       "      <td>file, image, program, include, software, versi...</td>\n",
       "      <td>From: (David B. Lewis) Subject: comp.windows.x...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5422</td>\n",
       "      <td>car, bike, buy, ride, drive, price, engine, tu...</td>\n",
       "      <td>From: (richard welty) Subject: rec.autos: Freq...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6271</td>\n",
       "      <td>man, people, life, love, church, world, die, r...</td>\n",
       "      <td>From: (Family Magazine Sysops) Subject: WITNES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.7178</td>\n",
       "      <td>gun, law, state, people, government, crime, ca...</td>\n",
       "      <td>From: (Larry Cipriani) Subject: Gun Talk -- St...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.6449</td>\n",
       "      <td>good, time, lot, find, make, work, thing, long...</td>\n",
       "      <td>From: (Jeff Mason) Subject: Marvel, DC, Valian...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Topic_Num  Topic_Perc_Contrib  \\\n",
       "0        0.0              0.7107   \n",
       "1        1.0              0.5422   \n",
       "2        2.0              0.6271   \n",
       "3        3.0              0.7178   \n",
       "4        4.0              0.6449   \n",
       "\n",
       "                                            Keywords  \\\n",
       "0  file, image, program, include, software, versi...   \n",
       "1  car, bike, buy, ride, drive, price, engine, tu...   \n",
       "2  man, people, life, love, church, world, die, r...   \n",
       "3  gun, law, state, people, government, crime, ca...   \n",
       "4  good, time, lot, find, make, work, thing, long...   \n",
       "\n",
       "                                                Text  \n",
       "0  From: (David B. Lewis) Subject: comp.windows.x...  \n",
       "1  From: (richard welty) Subject: rec.autos: Freq...  \n",
       "2  From: (Family Magazine Sysops) Subject: WITNES...  \n",
       "3  From: (Larry Cipriani) Subject: Gun Talk -- St...  \n",
       "4  From: (Jeff Mason) Subject: Marvel, DC, Valian...  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group top 5 sentences under each topic\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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SAsfgPE4JMFK"
   },
   "source": [
    "The tabular output above had 20 rows, one each for a topic. It has the topic number, the keywords and the most representative document. The `Perc_Contribution` column is nothing but the percentage contribution of the topic in the given document."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DuzxtBiAJMFL"
   },
   "source": [
    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 20. Topic distribution across documents</h2>\n",
    "</div>\n",
    "\n",
    "Finally, we want to understand the volume and distribution of topics in order to judge how widely it was discussed. The below table exposes that information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "0cFm7x2wJMFL"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Keywords</th>\n",
       "      <th>Num_Documents</th>\n",
       "      <th>Perc_Documents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>car, bike, buy, ride, drive, price, engine, tu...</td>\n",
       "      <td>729.0</td>\n",
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       "    <tr>\n",
       "      <th>1.0</th>\n",
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       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>625.0</td>\n",
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       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>584.0</td>\n",
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       "    <tr>\n",
       "      <th>4.0</th>\n",
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       "    <tr>\n",
       "      <th>5.0</th>\n",
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       "      <td>gun, law, state, people, government, crime, ca...</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>9.0</th>\n",
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       "      <td>0.0416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>car, bike, buy, ride, drive, price, engine, tu...</td>\n",
       "      <td>560.0</td>\n",
       "      <td>0.0495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>13.0</td>\n",
       "      <td>question, claim, true, exist, word, argument, ...</td>\n",
       "      <td>521.0</td>\n",
       "      <td>0.0460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>ax, line, host, organization, reply, nntp_post...</td>\n",
       "      <td>483.0</td>\n",
       "      <td>0.0427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>14.0</td>\n",
       "      <td>space, year, work, cost, program, launch, proj...</td>\n",
       "      <td>378.0</td>\n",
       "      <td>0.0334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>drive, card, system, problem, driver, disk, wo...</td>\n",
       "      <td>525.0</td>\n",
       "      <td>0.0464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>people, armenian, government, israeli, attack,...</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.0207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>file, image, program, include, software, versi...</td>\n",
       "      <td>575.0</td>\n",
       "      <td>0.0508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>car, bike, buy, ride, drive, price, engine, tu...</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.0232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>power, ground, high, current, wire, line, low,...</td>\n",
       "      <td>341.0</td>\n",
       "      <td>0.0301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>window, problem, line, set, call, application,...</td>\n",
       "      <td>1032.0</td>\n",
       "      <td>0.0912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Dominant_Topic                                     Topic_Keywords  \\\n",
       "0.0              1.0  car, bike, buy, ride, drive, price, engine, tu...   \n",
       "1.0              5.0  drive, card, system, problem, driver, disk, wo...   \n",
       "2.0              5.0  drive, card, system, problem, driver, disk, wo...   \n",
       "3.0              5.0  drive, card, system, problem, driver, disk, wo...   \n",
       "4.0             10.0  window, problem, line, set, call, application,...   \n",
       "5.0              3.0  gun, law, state, people, government, crime, ca...   \n",
       "6.0             16.0  mail, post, send, information, list, group, ad...   \n",
       "7.0              5.0  drive, card, system, problem, driver, disk, wo...   \n",
       "8.0             10.0  window, problem, line, set, call, application,...   \n",
       "9.0              5.0  drive, card, system, problem, driver, disk, wo...   \n",
       "10.0             1.0  car, bike, buy, ride, drive, price, engine, tu...   \n",
       "11.0            13.0  question, claim, true, exist, word, argument, ...   \n",
       "12.0            18.0  ax, line, host, organization, reply, nntp_post...   \n",
       "13.0            14.0  space, year, work, cost, program, launch, proj...   \n",
       "14.0             5.0  drive, card, system, problem, driver, disk, wo...   \n",
       "15.0             7.0  people, armenian, government, israeli, attack,...   \n",
       "16.0             0.0  file, image, program, include, software, versi...   \n",
       "17.0             1.0  car, bike, buy, ride, drive, price, engine, tu...   \n",
       "18.0             6.0  power, ground, high, current, wire, line, low,...   \n",
       "19.0            10.0  window, problem, line, set, call, application,...   \n",
       "\n",
       "      Num_Documents  Perc_Documents  \n",
       "0.0           729.0          0.0644  \n",
       "1.0          1002.0          0.0886  \n",
       "2.0           625.0          0.0552  \n",
       "3.0           584.0          0.0516  \n",
       "4.0           256.0          0.0226  \n",
       "5.0          1433.0          0.1267  \n",
       "6.0           557.0          0.0492  \n",
       "7.0           475.0          0.0420  \n",
       "8.0           270.0          0.0239  \n",
       "9.0           471.0          0.0416  \n",
       "10.0          560.0          0.0495  \n",
       "11.0          521.0          0.0460  \n",
       "12.0          483.0          0.0427  \n",
       "13.0          378.0          0.0334  \n",
       "14.0          525.0          0.0464  \n",
       "15.0          234.0          0.0207  \n",
       "16.0          575.0          0.0508  \n",
       "17.0          263.0          0.0232  \n",
       "18.0          341.0          0.0301  \n",
       "19.0         1032.0          0.0912  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Documents for Each Topic\n",
    "topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n",
    "\n",
    "# Percentage of Documents for Each Topic\n",
    "topic_contribution = round(topic_counts/topic_counts.sum(), 4)\n",
    "\n",
    "# Topic Number and Keywords\n",
    "topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\n",
    "\n",
    "# Concatenate Column wise\n",
    "df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\n",
    "\n",
    "# Change Column names\n",
    "df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\n",
    "\n",
    "# Show\n",
    "df_dominant_topics.head(20)"
   ]
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    "<div class=\"alert alert-info\" style=\"background-color:#5d3a8e; color:white; padding:0px 10px; border-radius:5px;\"><h2 style='margin:10px 5px'> 21. Conclusion</h2>\n",
    "</div>\n",
    "\n",
    "We started with understanding what topic modeling can do. We built a basic topic model using Gensim’s LDA and visualize the topics using pyLDAvis. Then we built mallet’s LDA implementation. You saw how to find the optimal number of topics using coherence scores and how you can come to a logical understanding of how to choose the optimal model.\n",
    "\n",
    "Finally we saw how to aggregate and present the results to generate insights that may be in a more actionable.\n",
    "\n",
    "Hope you enjoyed reading this. I would appreciate if you leave your thoughts in the comments section below.\n",
    "\n",
    "Edit: I see some of you are experiencing errors while using the LDA Mallet and I don’t have a solution for some of the issues. So, I’ve implemented a workaround and more useful [topic model visualizations](https://www.machinelearningplus.com/nlp/topic-modeling-visualization-how-to-present-results-lda-models/). Hope you will find it helpful."
   ]
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