topicmodels: AnRPackage for Fitting Topic Models | Zendy

Bettina Grün | Zendy; Kurt Hornik | Zendy

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topicmodels: AnRPackage for Fitting Topic Models

Author(s) -

Bettina Grün,

Kurt Hornik

Publication year - 2011

Publication title -

journal of statistical software

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.636

H-Index - 145

ISSN - 1548-7660

DOI - 10.18637/jss.v040.i13

Subject(s) - gibbs sampling , computer science , similarity (geometry) , expectation–maximization algorithm , set (abstract data type) , topic model , probabilistic logic , data set , r package , algorithm , data mining , maximum likelihood , artificial intelligence , statistics , mathematics , bayesian probability , computational science , programming language , image (mathematics)

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.

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