Journal
STATA JOURNAL
Volume 18, Issue 1, Pages 101-117Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1536867X1801800107
Keywords
st0515; ldagibbs; machine learning; latent Dirichlet allocation; Gibbs sampling; topic model; text analysis
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In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications.
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