ldagibbs: A command for topic modeling in Stata using latent Dirichlet allocation

Carlo Schwarz

Stata Journal, 2018, vol. 18, issue 1, 101-117

Abstract: 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.

Keywords: ldagibbs; machine learning; latent Dirichlet allocation; Gibbs sampling; topic model; text analysis (search for similar items in EconPapers)
Date: 2018
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