keyATM: Keyword Assisted Topic Model

Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2020) <arXiv:2004.05964>.

Package details

AuthorShusei Eshima [aut, cre] (<>), Tomoya Sasaki [aut], William Lowe [ctb], Kosuke Imai [aut], Chung-hong Chan [ctb] (<>), Romain Fran├žois [ctb] (<>)
MaintainerShusei Eshima <>
Package repositoryView on CRAN
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keyATM documentation built on Feb. 15, 2021, 1:07 a.m.