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| -rw-r--r-- | gnu/packages/cran.scm | 31 |
1 files changed, 31 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 66fd2c733d..91cf7c550d 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -28045,3 +28045,34 @@ Application Program Interfaces (API)}.") "Imports plain-text ASC data files from EyeLink eye trackers into (relatively) tidy data frames for analysis and visualization.") (license license:gpl3))) + +(define-public r-btm + (package + (name "r-btm") + (version "0.3.5") + (source + (origin + (method url-fetch) + (uri (cran-uri "BTM" version)) + (sha256 + (base32 + "1x6bncb7r97z8bdyxnn2frdi9kyawfy6c2041mv9f42zdrfzm6jb")))) + (properties `((upstream-name . "BTM"))) + (build-system r-build-system) + (propagated-inputs `(("r-rcpp" ,r-rcpp))) + (home-page "https://github.com/bnosac/BTM") + (synopsis "Biterm Topic Models for Short Text") + (description + "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) +@url{https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/\ +BTM-WWW13.pdf}.") + (license license:asl2.0))) |