BTM: Biterm Topic Models for Short Text

Biterm Topic Models find topics in collections of short texts. It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms. This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis which are word-document co-occurrence topic models. A biterm consists of two words co-occurring in the same short text window. This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.

Version:	0.3.7
Imports:	Rcpp, utils
LinkingTo:	Rcpp
Suggests:	udpipe, data.table
Published:	2023-02-11
DOI:	10.32614/CRAN.package.BTM
Author:	Jan Wijffels [aut, cre, cph] (R wrapper), BNOSAC [cph] (R wrapper), Xiaohui Yan [ctb, cph] (BTM C++ library)
Maintainer:	Jan Wijffels <jwijffels at bnosac.be>
License:	Apache License 2.0
URL:	https://github.com/bnosac/BTM
NeedsCompilation:	yes
Materials:	README NEWS
In views:	NaturalLanguageProcessing
CRAN checks:	BTM results

Documentation:

Reference manual:

BTM.pdf

Downloads:

Package source:	BTM_0.3.7.tar.gz
Windows binaries:	r-devel: BTM_0.3.7.zip, r-release: BTM_0.3.7.zip, r-oldrel: BTM_0.3.7.zip
macOS binaries:	r-release (arm64): BTM_0.3.7.tgz, r-oldrel (arm64): BTM_0.3.7.tgz, r-release (x86_64): BTM_0.3.7.tgz, r-oldrel (x86_64): BTM_0.3.7.tgz
Old sources:	BTM archive

Reverse dependencies:

Reverse suggests:

oolong, textplot

Linking:

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