topicmodels: An R Package for Fitting Topic Models

Hornik, Kurt ORCID: and Grün, Bettina (2011) topicmodels: An R Package for Fitting Topic Models. Journal of Statistical Software, 40 (13). pp. 1-30. ISSN 1548-7660


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Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Item Type: Article
Keywords: Gibbs sampling / R / text analysis / topic model / variational EM
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics > Hornik
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 11 Oct 2013 09:35
Last Modified: 24 Oct 2019 13:41


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