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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


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

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    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. 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.

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In the following tracks

Machine Learning Scientist


Richie CottonHadrien Lacroix
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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