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LDA Topic Modeling.pynb
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LDA Topic Modeling.pynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"display_name": "topic_model",
"language": "python",
"name": "topic_model"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
},
"colab": {
"name": "Introduction to Topic Modeling.ipynb",
"provenance": []
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "iiu0XFEYefDI"
},
"source": [
"## Introduction\n",
"** **\n",
"Topic Models, in a nutshell, are a type of statistical language models used for uncovering hidden structure in a collection of texts. In a practical and more intuitively, you can think of it as a task of:\n",
"\n",
"- **Dimensionality Reduction**, where rather than representing a text T in its feature space as {Word_i: count(Word_i, T) for Word_i in Vocabulary}, you can represent it in a topic space as {Topic_i: Weight(Topic_i, T) for Topic_i in Topics}\n",
"- **Unsupervised Learning**, where it can be compared to clustering, as in the case of clustering, the number of topics, like the number of clusters, is an output parameter. By doing topic modeling, we build clusters of words rather than clusters of texts. A text is thus a mixture of all the topics, each having a specific weight\n",
"- **Tagging**, abstract “topics” that occur in a collection of documents that best represents the information in them.\n",
"\n",
"There are several existing algorithms you can use to perform the topic modeling. The most common of it are, Latent Semantic Analysis (LSA/LSI), Probabilistic Latent Semantic Analysis (pLSA), and Latent Dirichlet Allocation (LDA)\n",
"\n",
"In this tutorial, we’ll take a closer look at LDA, and implement our first topic model using the sklearn implementation in python 2.7\n",
"\n",
"### Theoretical Overview\n",
"LDA is a generative probabilistic model that assumes each topic is a mixture over an underlying set of words, and each document is a mixture of over a set of topic probabilities.\n",
"\n",
"![LDA_Model](https://github.com/chdoig/pytexas2015-topic-modeling/blob/master/images/lda-4.png?raw=true)\n",
"\n",
"We can describe the generative process of LDA as, given the M number of documents, N number of words, and prior K number of topics, the model trains to output:\n",
"\n",
"- `psi`, the distribution of words for each topic K\n",
"- `phi`, the distribution of topics for each document i\n",
"\n",
"#### Parameters of LDA\n",
"\n",
"- `Alpha parameter` is Dirichlet prior concentration parameter that represents document-topic density — with a higher alpha, documents are assumed to be made up of more topics and result in more specific topic distribution per document.\n",
"- `Beta parameter` is the same prior concentration parameter that represents topic-word density — with high beta, topics are assumed to made of up most of the words and result in a more specific word distribution per topic.\n",
"\n",
"**To read more: https://towardsdatascience.com/end-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0lB085VIefDP"
},
"source": [
"** **\n",
"### LDA Implementation\n",
"\n",
"1. [Loading data](#load_data)\n",
"2. [Data cleaning](#clean_data)\n",
"3. [Exploratory analysis](#eda)\n",
"4. [Prepare data for LDA analysis](#data_preparation)\n",
"5. [LDA model training](#train_model)\n",
"6. [Analyzing LDA model results](#results)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0L9UYNuLefDQ"
},
"source": [
"** **\n",
"For this tutorial, we’ll use the dataset of papers published in NeurIPS (NIPS) conference which is one of the most prestigious yearly events in the machine learning community. The CSV data file contains information on the different NeurIPS papers that were published from 1987 until 2016 (29 years!). These papers discuss a wide variety of topics in machine learning, from neural networks to optimization methods, and many more.\n",
"\n",
"<img src=\"https://s3.amazonaws.com/assets.datacamp.com/production/project_158/img/nips_logo.png\" alt=\"The logo of NIPS (Neural Information Processing Systems)\">\n",
"\n",
"Let’s start by looking at the content of the file"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bWnB6V70efDQ"
},
"source": [
"** **\n",
"#### Step 1: Loading Data <a class=\"anchor\\\" id=\"load_data\"></a>\n",
"** **"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IKSjysOPefDR",
"outputId": "50cda08b-0e59-435e-f6ee-0ceacc4f0b4a"
},
"source": [
"# Importing modules\n",
"import pandas as pd\n",
"import os\n",
"\n",
"os.chdir('..')\n",
"\n",
"# Read data into papers\n",
"papers = pd.read_csv('./data/NIPS Papers/papers.csv')\n",
"\n",
"# Print head\n",
"papers.head()"
],
"execution_count": null,
"outputs": [
{
"data": {
"text/html": [
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"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>year</th>\n",
" <th>title</th>\n",
" <th>event_type</th>\n",
" <th>pdf_name</th>\n",
" <th>abstract</th>\n",
" <th>paper_text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1987</td>\n",
" <td>Self-Organization of Associative Database and ...</td>\n",
" <td>NaN</td>\n",
" <td>1-self-organization-of-associative-database-an...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10</td>\n",
" <td>1987</td>\n",
" <td>A Mean Field Theory of Layer IV of Visual Cort...</td>\n",
" <td>NaN</td>\n",
" <td>10-a-mean-field-theory-of-layer-iv-of-visual-c...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>100</td>\n",
" <td>1988</td>\n",
" <td>Storing Covariance by the Associative Long-Ter...</td>\n",
" <td>NaN</td>\n",
" <td>100-storing-covariance-by-the-associative-long...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1000</td>\n",
" <td>1994</td>\n",
" <td>Bayesian Query Construction for Neural Network...</td>\n",
" <td>NaN</td>\n",
" <td>1000-bayesian-query-construction-for-neural-ne...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>Bayesian Query Construction for Neural\\nNetwor...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1001</td>\n",
" <td>1994</td>\n",
" <td>Neural Network Ensembles, Cross Validation, an...</td>\n",
" <td>NaN</td>\n",
" <td>1001-neural-network-ensembles-cross-validation...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>Neural Network Ensembles, Cross\\nValidation, a...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id year title event_type \\\n",
"0 1 1987 Self-Organization of Associative Database and ... NaN \n",
"1 10 1987 A Mean Field Theory of Layer IV of Visual Cort... NaN \n",
"2 100 1988 Storing Covariance by the Associative Long-Ter... NaN \n",
"3 1000 1994 Bayesian Query Construction for Neural Network... NaN \n",
"4 1001 1994 Neural Network Ensembles, Cross Validation, an... NaN \n",
"\n",
" pdf_name abstract \\\n",
"0 1-self-organization-of-associative-database-an... Abstract Missing \n",
"1 10-a-mean-field-theory-of-layer-iv-of-visual-c... Abstract Missing \n",
"2 100-storing-covariance-by-the-associative-long... Abstract Missing \n",
"3 1000-bayesian-query-construction-for-neural-ne... Abstract Missing \n",
"4 1001-neural-network-ensembles-cross-validation... Abstract Missing \n",
"\n",
" paper_text \n",
"0 767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA... \n",
"1 683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU... \n",
"2 394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n... \n",
"3 Bayesian Query Construction for Neural\\nNetwor... \n",
"4 Neural Network Ensembles, Cross\\nValidation, a... "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ETqjuAAIefDT"
},
"source": [
"** **\n",
"#### Step 2: Data Cleaning <a class=\"anchor\\\" id=\"clean_data\"></a>\n",
"** **\n",
"\n",
"Since the goal of this analysis is to perform topic modeling, let's focus only on the text data from each paper, and drop other metadata columns. Also, for the demonstration, we'll only look at 100 papers"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SxUphCSfefDT",
"outputId": "e77576f8-bf39-4833-96aa-5b2fee056e6f"
},
"source": [
"# Remove the columns\n",
"papers = papers.drop(columns=['id', 'event_type', 'pdf_name'], axis=1).sample(100)\n",
"\n",
"# Print out the first rows of papers\n",
"papers.head()"
],
"execution_count": null,
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>title</th>\n",
" <th>abstract</th>\n",
" <th>paper_text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2370</th>\n",
" <td>2006</td>\n",
" <td>Map-Reduce for Machine Learning on Multicore</td>\n",
" <td>Abstract Missing</td>\n",
" <td>Map-Reduce for Machine Learning on Multicore\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>537</th>\n",
" <td>1998</td>\n",
" <td>Kernel PCA and De-Noising in Feature Spaces</td>\n",
" <td>Abstract Missing</td>\n",
" <td>Kernel peA and De-Noising in Feature Spaces\\n\\...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5061</th>\n",
" <td>2014</td>\n",
" <td>Approximating Hierarchical MV-sets for Hierarc...</td>\n",
" <td>The goal of hierarchical clustering is to cons...</td>\n",
" <td>Approximating Hierarchical MV-sets for Hierarc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2053</th>\n",
" <td>2005</td>\n",
" <td>Fast Krylov Methods for N-Body Learning</td>\n",
" <td>Abstract Missing</td>\n",
" <td>Fast Krylov Methods for N-Body Learning\\n\\nYan...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165</th>\n",
" <td>1988</td>\n",
" <td>Heterogeneous Neural Networks for Adaptive Beh...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>577\\n\\nHETEROGENEOUS NEURAL NETWORKS FOR\\nADAP...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year title \\\n",
"2370 2006 Map-Reduce for Machine Learning on Multicore \n",
"537 1998 Kernel PCA and De-Noising in Feature Spaces \n",
"5061 2014 Approximating Hierarchical MV-sets for Hierarc... \n",
"2053 2005 Fast Krylov Methods for N-Body Learning \n",
"165 1988 Heterogeneous Neural Networks for Adaptive Beh... \n",
"\n",
" abstract \\\n",
"2370 Abstract Missing \n",
"537 Abstract Missing \n",
"5061 The goal of hierarchical clustering is to cons... \n",
"2053 Abstract Missing \n",
"165 Abstract Missing \n",
"\n",
" paper_text \n",
"2370 Map-Reduce for Machine Learning on Multicore\\n... \n",
"537 Kernel peA and De-Noising in Feature Spaces\\n\\... \n",
"5061 Approximating Hierarchical MV-sets for Hierarc... \n",
"2053 Fast Krylov Methods for N-Body Learning\\n\\nYan... \n",
"165 577\\n\\nHETEROGENEOUS NEURAL NETWORKS FOR\\nADAP... "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GSVDbSTwefDU"
},
"source": [
"##### Remove punctuation/lower casing\n",
"\n",
"Next, let’s perform a simple preprocessing on the content of `paper_text` column to make them more amenable for analysis, and reliable results. To do that, we’ll use a regular expression to remove any punctuation, and then lowercase the text"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0mWFY0uTefDV",
"outputId": "750831f9-8700-4244-ff08-2cf2db705b24"
},
"source": [
"# Load the regular expression library\n",
"import re\n",
"\n",
"# Remove punctuation\n",
"papers['paper_text_processed'] = \\\n",
"papers['paper_text'].map(lambda x: re.sub('[,\\.!?]', '', x))\n",
"\n",
"# Convert the titles to lowercase\n",
"papers['paper_text_processed'] = \\\n",
"papers['paper_text_processed'].map(lambda x: x.lower())\n",
"\n",
"# Print out the first rows of papers\n",
"papers['paper_text_processed'].head()"
],
"execution_count": null,
"outputs": [
{
"data": {
"text/plain": [
"2370 map-reduce for machine learning on multicore\\n...\n",
"537 kernel pea and de-noising in feature spaces\\n\\...\n",
"5061 approximating hierarchical mv-sets for hierarc...\n",
"2053 fast krylov methods for n-body learning\\n\\nyan...\n",
"165 577\\n\\nheterogeneous neural networks for\\nadap...\n",
"Name: paper_text_processed, dtype: object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MfWffIAoefDW"
},
"source": [
"** **\n",
"#### Step 3: Exploratory Analysis <a class=\"anchor\\\" id=\"eda\"></a>\n",
"** **\n",
"\n",
"To verify whether the preprocessing, we’ll make a simple word cloud using the `wordcloud` package to get a visual representation of most common words. It is key to understanding the data and ensuring we are on the right track, and if any more preprocessing is necessary before training the model."
]
},
{
"cell_type": "code",
"metadata": {
"id": "VzJPg2S1efDW",
"outputId": "25a51d4f-2ce6-43d7-8895-e60267563fa4"
},
"source": [
"# Import the wordcloud library\n",
"from wordcloud import WordCloud\n",
"\n",
"# Join the different processed titles together.\n",
"long_string = ','.join(list(papers['paper_text_processed'].values))\n",
"\n",
"# Create a WordCloud object\n",
"wordcloud = WordCloud(background_color=\"white\", max_words=1000, contour_width=3, contour_color='steelblue')\n",
"\n",
"# Generate a word cloud\n",
"wordcloud.generate(long_string)\n",
"\n",
"# Visualize the word cloud\n",
"wordcloud.to_image()"
],
"execution_count": null,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=400x200 at 0x7F96264648E0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rmx5InbhefDX"
},
"source": [
"** **\n",
"#### Step 4: Prepare text for LDA analysis <a class=\"anchor\\\" id=\"data_preparation\"></a>\n",
"** **\n",
"\n",
"Next, let’s work to transform the textual data in a format that will serve as an input for training LDA model. We start by tokenizing the text and removing stopwords. Next, we convert the tokenized object into a corpus and dictionary."
]
},
{
"cell_type": "code",
"metadata": {
"id": "xub1FlswefDX",
"outputId": "ec1baa74-7c47-48c0-ef3a-919474156329"
},
"source": [
"import gensim\n",
"from gensim.utils import simple_preprocess\n",
"import nltk\n",
"nltk.download('stopwords')\n",
"from nltk.corpus import stopwords\n",
"\n",
"stop_words = stopwords.words('english')\n",
"stop_words.extend(['from', 'subject', 're', 'edu', 'use'])\n",
"\n",
"def sent_to_words(sentences):\n",
" for sentence in sentences:\n",
" # deacc=True removes punctuations\n",
" yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))\n",
"\n",
"def remove_stopwords(texts):\n",
" return [[word for word in simple_preprocess(str(doc)) \n",
" if word not in stop_words] for doc in texts]\n",
"\n",
"\n",
"data = papers.paper_text_processed.values.tolist()\n",
"data_words = list(sent_to_words(data))\n",
"\n",
"# remove stop words\n",
"data_words = remove_stopwords(data_words)\n",
"\n",
"print(data_words[:1][0][:30])"
],
"execution_count": null,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] /Users/shashank/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['map', 'reduce', 'machine', 'learning', 'multicore', 'cheng', 'tao', 'chu', 'chengtao', 'stanfordedu', 'sang', 'kyun', 'kim', 'skkim', 'stanfordedu', 'yuanyuan', 'yu', 'yuanyuan', 'stanfordedu', 'gary', 'bradski', 'garybradski', 'gmail', 'yi', 'lin', 'ianl', 'stanfordedu', 'andrew', 'ng', 'ang']\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "WR7LI-nfefDX",
"outputId": "2e41b1fd-b5a4-4f14-e28a-55e591cb1cfe"
},
"source": [
"import gensim.corpora as corpora\n",
"\n",
"# Create Dictionary\n",
"id2word = corpora.Dictionary(data_words)\n",
"\n",
"# Create Corpus\n",
"texts = data_words\n",
"\n",
"# Term Document Frequency\n",
"corpus = [id2word.doc2bow(text) for text in texts]\n",
"\n",
"# View\n",
"print(corpus[:1][0][:30])"
],
"execution_count": null,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 5), (8, 3), (9, 1), (10, 2), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 2), (24, 1), (25, 4), (26, 1), (27, 1), (28, 3), (29, 2)]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dgK18e_uefDY"
},
"source": [
"** **\n",
"#### Step 5: LDA model tranining <a class=\"anchor\\\" id=\"train_model\"></a>\n",
"** **\n",
"\n",
"To keep things simple, we'll keep all the parameters to default except for inputting the number of topics. For this tutorial, we will build a model with 10 topics where each topic is a combination of keywords, and each keyword contributes a certain weightage to the topic."
]
},
{
"cell_type": "code",
"metadata": {
"id": "TceXDAZ8efDZ",
"outputId": "6880d245-0c01-4409-8b2e-ac9787b2fc3d"
},
"source": [
"from pprint import pprint\n",
"\n",
"# number of topics\n",
"num_topics = 10\n",
"\n",
"# Build LDA model\n",
"lda_model = gensim.models.LdaMulticore(corpus=corpus,\n",
" id2word=id2word,\n",
" num_topics=num_topics)\n",
"\n",
"# Print the Keyword in the 10 topics\n",
"pprint(lda_model.print_topics())\n",
"doc_lda = lda_model[corpus]"
],
"execution_count": null,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0,\n",
" '0.007*\"learning\" + 0.006*\"data\" + 0.006*\"time\" + 0.006*\"model\" + '\n",
" '0.006*\"set\" + 0.005*\"one\" + 0.004*\"using\" + 0.003*\"information\" + '\n",
" '0.003*\"function\" + 0.003*\"algorithm\"'),\n",
" (1,\n",
" '0.005*\"set\" + 0.005*\"one\" + 0.005*\"data\" + 0.005*\"model\" + 0.004*\"function\" '\n",
" '+ 0.004*\"time\" + 0.004*\"algorithm\" + 0.004*\"figure\" + 0.003*\"learning\" + '\n",
" '0.003*\"used\"'),\n",
" (2,\n",
" '0.008*\"data\" + 0.006*\"model\" + 0.005*\"time\" + 0.004*\"learning\" + '\n",
" '0.004*\"one\" + 0.004*\"algorithm\" + 0.004*\"two\" + 0.004*\"using\" + '\n",
" '0.004*\"figure\" + 0.003*\"given\"'),\n",
" (3,\n",
" '0.006*\"data\" + 0.005*\"model\" + 0.004*\"matrix\" + 0.004*\"learning\" + '\n",
" '0.004*\"set\" + 0.004*\"figure\" + 0.003*\"time\" + 0.003*\"two\" + 0.003*\"used\" + '\n",
" '0.003*\"given\"'),\n",
" (4,\n",
" '0.006*\"learning\" + 0.004*\"data\" + 0.004*\"model\" + 0.004*\"set\" + '\n",
" '0.004*\"function\" + 0.004*\"algorithm\" + 0.003*\"training\" + 0.003*\"one\" + '\n",
" '0.003*\"time\" + 0.003*\"using\"'),\n",
" (5,\n",
" '0.006*\"data\" + 0.006*\"learning\" + 0.005*\"time\" + 0.005*\"using\" + '\n",
" '0.005*\"number\" + 0.005*\"algorithm\" + 0.005*\"set\" + 0.004*\"one\" + '\n",
" '0.004*\"model\" + 0.004*\"two\"'),\n",
" (6,\n",
" '0.005*\"data\" + 0.005*\"learning\" + 0.005*\"time\" + 0.004*\"model\" + '\n",
" '0.004*\"using\" + 0.004*\"number\" + 0.004*\"function\" + 0.004*\"one\" + '\n",
" '0.003*\"neural\" + 0.003*\"algorithm\"'),\n",
" (7,\n",
" '0.005*\"data\" + 0.004*\"set\" + 0.004*\"noise\" + 0.004*\"one\" + 0.003*\"input\" + '\n",
" '0.003*\"model\" + 0.003*\"function\" + 0.003*\"using\" + 0.003*\"figure\" + '\n",
" '0.003*\"used\"'),\n",
" (8,\n",
" '0.006*\"data\" + 0.005*\"model\" + 0.005*\"learning\" + 0.004*\"time\" + '\n",
" '0.004*\"set\" + 0.004*\"algorithm\" + 0.004*\"number\" + 0.004*\"using\" + '\n",
" '0.003*\"one\" + 0.003*\"figure\"'),\n",
" (9,\n",
" '0.006*\"learning\" + 0.005*\"model\" + 0.005*\"time\" + 0.005*\"set\" + '\n",
" '0.004*\"network\" + 0.004*\"algorithm\" + 0.004*\"data\" + 0.004*\"two\" + '\n",
" '0.004*\"using\" + 0.003*\"number\"')]\n"
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{
"cell_type": "markdown",
"metadata": {
"id": "CRjmN07pefDZ"
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"source": [
"** **\n",
"#### Step 6: Analyzing our LDA model <a class=\"anchor\\\" id=\"results\"></a>\n",
"** **\n",
"\n",
"Now that we have a trained model let’s visualize the topics for interpretability. To do so, we’ll use a popular visualization package, pyLDAvis which is designed to help interactively with:\n",
"\n",
"1. Better understanding and interpreting individual topics, and\n",
"2. Better understanding the relationships between the topics.\n",
"\n",
"For (1), you can manually select each topic to view its top most frequent and/or “relevant” terms, using different values of the λ parameter. This can help when you’re trying to assign a human interpretable name or “meaning” to each topic.\n",
"\n",
"For (2), exploring the Intertopic Distance Plot can help you learn about how topics relate to each other, including potential higher-level structure between groups of topics."
]
},
{
"cell_type": "code",
"metadata": {
"id": "8krd7sYxefDZ",
"outputId": "7aefc487-ba18-40f1-d189-0d5c06cedc1b"
},
"source": [
"import pyLDAvis.gensim\n",
"import pickle \n",
"import pyLDAvis\n",
"\n",
"# Visualize the topics\n",
"pyLDAvis.enable_notebook()\n",
"\n",
"LDAvis_data_filepath = os.path.join('./results/ldavis_prepared_'+str(num_topics))\n",
"\n",
"# # this is a bit time consuming - make the if statement True\n",
"# # if you want to execute visualization prep yourself\n",
"if 1 == 1:\n",
" LDAvis_prepared = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
" with open(LDAvis_data_filepath, 'wb') as f:\n",
" pickle.dump(LDAvis_prepared, f)\n",
"\n",
"# load the pre-prepared pyLDAvis data from disk\n",
"with open(LDAvis_data_filepath, 'rb') as f:\n",
" LDAvis_prepared = pickle.load(f)\n",
"\n",
"pyLDAvis.save_html(LDAvis_prepared, './results/ldavis_prepared_'+ str(num_topics) +'.html')\n",
"\n",
"LDAvis_prepared"
],
"execution_count": null,
"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_el5968140282953337088419393507\"></div>\n",
"<script type=\"text/javascript\">\n",
"\n",
"var ldavis_el5968140282953337088419393507_data = {\"mdsDat\": {\"x\": [0.0012094147069519042, -0.005537013430266772, -0.0027211085945675366, -0.006120522574433215, 0.009977048197862796, -0.00926065961289576, 0.0026077525206823315, 0.002343523397477518, 0.0016413736510676824, 0.005860191738121037], \"y\": [-0.006852740860139755, 0.0013596157488496904, 0.002352254424497561, -0.0054765681827625284, -0.0049562387220266685, 0.0031223557552789074, 0.0027857807441994393, 0.002922828480118324, -0.005962204047826034, 0.01070491665981107], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [26.219357989751185, 19.057092327089045, 12.621059600301193, 11.072191002894877, 7.965070509867099, 6.6149971394970475, 6.21541285933621, 4.434373155778177, 4.117367873252515, 1.6830775422326492]}, \"tinfo\": {\"Term\": [\"data\", \"model\", \"set\", \"noise\", \"function\", \"one\", \"figure\", \"learning\", \"time\", \"matrix\", \"used\", \"input\", 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"text/plain": [
"PreparedData(topic_coordinates= x y topics cluster Freq\n",
"topic \n",
"5 0.001209 -0.006853 1 1 26.219358\n",
"0 -0.005537 0.001360 2 1 19.057092\n",
"9 -0.002721 0.002352 3 1 12.621060\n",
"2 -0.006121 -0.005477 4 1 11.072191\n",
"4 0.009977 -0.004956 5 1 7.965071\n",
"3 -0.009261 0.003122 6 1 6.614997\n",
"1 0.002608 0.002786 7 1 6.215413\n",
"6 0.002344 0.002923 8 1 4.434373\n",
"8 0.001641 -0.005962 9 1 4.117368\n",
"7 0.005860 0.010705 10 1 1.683078, topic_info= Term Freq Total Category logprob loglift\n",
"226 data 1303.000000 1303.000000 Default 30.0000 30.0000\n",
"608 model 1145.000000 1145.000000 Default 29.0000 29.0000\n",
"839 set 1051.000000 1051.000000 Default 28.0000 28.0000\n",
"644 noise 410.000000 410.000000 Default 27.0000 27.0000\n",
"374 function 754.000000 754.000000 Default 26.0000 26.0000\n",
"... ... ... ... ... ... ...\n",
"2352 models 8.125805 499.677200 Topic10 -6.1682 -0.0344\n",
"351 first 8.134685 506.676646 Topic10 -6.1671 -0.0472\n",
"529 learning 10.739997 1269.997569 Topic10 -5.8893 -0.6882\n",
"33 algorithm 9.152962 869.695250 Topic10 -6.0492 -0.4695\n",
"650 number 8.184121 762.415518 Topic10 -6.1611 -0.4497\n",
"\n",
"[889 rows x 6 columns], token_table= Topic Freq Term\n",
"term \n",
"5774 1 0.288167 activities\n",
"5774 2 0.164667 activities\n",
"5774 3 0.041167 activities\n",
"5774 4 0.082333 activities\n",
"5774 5 0.082333 activities\n",
"... ... ... ...\n",
"14317 5 0.065265 zaj\n",
"14317 6 0.065265 zaj\n",
"14317 7 0.065265 zaj\n",
"14317 8 0.065265 zaj\n",
"14317 9 0.065265 zaj\n",
"\n",
"[3631 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 1, 10, 3, 5, 4, 2, 7, 9, 8])"
]
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"source": [
"** **\n",
"#### Closing Notes\n",
"Machine learning has become increasingly popular over the past decade, and recent advances in computational availability have led to exponential growth to people looking for ways how new methods can be incorporated to advance the field of Natural Language Processing.\n",
"\n",
"Often, we treat topic models as black-box algorithms, but hopefully, this article addressed to shed light on the underlying math, and intuitions behind it, and high-level code to get you started with any textual data.\n",
"\n",
"In the next article, we’ll go one step deeper into understanding how you can evaluate the performance of topic models, tune its hyper-parameters to get more intuitive and reliable results.\n",
"\n",
"** **\n",
"#### References:\n",
"1. Topic model — Wikipedia. https://en.wikipedia.org/wiki/Topic_model\n",
"2. Distributed Strategies for Topic Modeling. https://www.ideals.illinois.edu/bitstream/handle/2142/46405/ParallelTopicModels.pdf?sequence=2&isAllowed=y\n",
"3. Topic Mapping — Software — Resources — Amaral Lab. https://amaral.northwestern.edu/resources/software/topic-mapping\n",
"4. A Survey of Topic Modeling in Text Mining. https://thesai.org/Downloads/Volume6No1/Paper_21-A_Survey_of_Topic_Modeling_in_Text_Mining.pdf\n"
]
}
]
}