Topic Modeling in R
Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.
- 4 hours
- 14 Videos
- 49 Exercises
- 2,921 Participants
- 3,950 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.
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1
Quick introduction to the workflow
FreeThis 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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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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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.
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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.
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1
Quick introduction to the workflow
FreeThis chapter introduces the workflow used in topic modeling: preparation of a document-term matrix, model fitting, and visualization of results with ggplot2.
-
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.
-
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.
-
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.
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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.
See MoreCollaborators
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Hadrien Lacroix
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Richie Cotton