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

• Autores: Carlo Schwarz
• Localización: The Stata journal, ISSN 1536-867X, Vol. 18, Nº. 1, 2018, págs. 101-117
• Idioma: inglés
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• Resumen

  • 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.