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Topic Modeling in R

Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.

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4 Hours14 Videos49 Exercises4,534 Learners
3950 XP

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Course Description

This course introduces students to the areas involved in topic modeling: preparation of corpus, fitting of topic models using Latent Dirichlet Allocation algorithm (in package topicmodels), and visualizing the results using ggplot2 and wordclouds.

  1. 1

    Quick introduction to the workflow

    Free

    This chapter introduces the workflow used in topic modeling: preparation of a document-term matrix, model fitting, and visualization of results with ggplot2.

    Play Chapter Now
    Why learn topic modeling
    50 xp
    Topics as word contexts
    50 xp
    Topic prevalence
    50 xp
    Probabilities of words belonging to topics
    100 xp
    Counting words
    50 xp
    Removal of punctuation marks
    50 xp
    Word frequencies
    100 xp
    Our first LDA model
    100 xp
    Displaying frequencies with ggplot
    50 xp
    Simple LDA model
    100 xp
  2. 2

    Wordclouds, stopwords, and control arguments

    This chapter explains how to use join functions to remove or keep words in the document-term matrix, how to make wordcloud charts, and how to use some of the many control arguments.

    Play Chapter Now
    Random nature of LDA algorithm
    50 xp
    Probabilities of words in topics
    100 xp
    Effect of argument alpha
    100 xp
    Manipulating the vocabulary
    50 xp
    Making a dtm - refresher
    100 xp
    Removing stopwords
    100 xp
    Keeping the needed words
    100 xp
    Word clouds
    50 xp
    Wordcloud of term frequency
    100 xp
    History of the Byzantine Empire
    50 xp
    LDA model fitting - first iteration
    100 xp
    Capturing the actions - dtm with verbs
    100 xp
    Making a chart
    100 xp
    Use wordclouds
    100 xp
  3. 3

    Named entity recognition as unsupervised classification

    This chapter goes into detail on how LDA topic models can be used as classifiers. It covers the importance of the Dirichlet shape parameter alpha, construction of word contexts for named entities using regex, and technical issues like corpus alignment and held-out data.

    Play Chapter Now
    Using topic models as classifiers
    50 xp
    Same k, different alpha
    100 xp
    Probabilities of words in topics
    100 xp
    From word windows to dtm
    50 xp
    Regex patterns for entity matching
    100 xp
    Making a corpus
    100 xp
    From dtm to topic model
    100 xp
    Corpus alignment and classification
    50 xp
    Train a topic model
    100 xp
    Align corpus
    100 xp
    Classify test data
    100 xp
    Explore the results
    50 xp
  4. 4

    How many topics is enough?

    This chapter explains the basic methods used in the search for the optimal number of topics. It also covers how to use a single document as a source of data, and how topic numbering can be controlled using seed words.

    Play Chapter Now
    Finding the best number of topics
    50 xp
    Preparing the dtm
    100 xp
    Filtering by word frequency
    100 xp
    Fitting one model
    100 xp
    Using perplexity to find the best k
    100 xp
    Topic models fitted to novels
    50 xp
    Generating chunk numbers
    100 xp
    Inner join and cast dtm
    100 xp
    Finding the best value for k
    50 xp
    Locking topics by using seed words
    50 xp
    Topics without seedwords
    100 xp
    Topics with seedwords
    100 xp
    Final words (and more things to learn)
    50 xp

In the following tracks

Machine Learning Scientist

Datasets

History DataDocument Corpus

Collaborators

Richie CottonHadrien Lacroix

Prerequisites

Text Mining with Bag-of-Words in RIntroduction to Natural Language Processing in R
Pavel Oleinikov Headshot

Pavel Oleinikov

Associate Director, Quantitative Analysis Center, Wesleyan University

Pavel Oleinikov uses his background in social and natural sciences to advance the application of quantitative methods to data from the social world. He teaches courses on basics of Big Data, network analysis, text mining, and skills-focused courses. A large part of his work lies in assisting Wesleyan faculty with their diverse projects.
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