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-rw-r--r--gnu/packages/cran.scm31
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)))