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: 0.2
Imports: Rcpp, utils
LinkingTo: Rcpp
Suggests: udpipe
Published: 2018-12-27
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
NeedsCompilation: yes
SystemRequirements: C++11
Materials: README NEWS
CRAN checks: BTM results

Downloads:

Reference manual: BTM.pdf
Package source: BTM_0.2.tar.gz
Windows binaries: r-devel: BTM_0.2.zip, r-release: BTM_0.2.zip, r-oldrel: BTM_0.2.zip
OS X binaries: r-release: BTM_0.2.tgz, r-oldrel: not available

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