lsa: Latent Semantic Analysis

The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.

Version: 0.73.3
Depends: SnowballC
Suggests: tm
Published: 2022-05-09
DOI: 10.32614/CRAN.package.lsa
Author: Fridolin Wild
Maintainer: Fridolin Wild <wild at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: ChangeLog
In views: NaturalLanguageProcessing
CRAN checks: lsa results


Reference manual: lsa.pdf


Package source: lsa_0.73.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): lsa_0.73.3.tgz, r-oldrel (arm64): lsa_0.73.3.tgz, r-release (x86_64): lsa_0.73.3.tgz, r-oldrel (x86_64): lsa_0.73.3.tgz
Old sources: lsa archive

Reverse dependencies:

Reverse depends: AurieLSHGaussian, LSAfun
Reverse imports: aPEAR, ccmap, CellScore, CoreGx, DTWBI, DTWUMI, IBCF.MTME, OmicsQC, RESOLVE
Reverse suggests: quanteda, quanteda.textmodels, Signac, SpatialDDLS


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