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 brookes.ac.uk>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Materials:	ChangeLog
In views:	NaturalLanguageProcessing
CRAN checks:	lsa results

Documentation:

Reference manual:

lsa.pdf

Downloads:

Package source:	lsa_0.73.3.tar.gz
Windows binaries:	r-devel: lsa_0.73.3.zip, r-release: lsa_0.73.3.zip, r-oldrel: lsa_0.73.3.zip
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, conversim, CoreGx, DTWBI, DTWUMI, GeneNMF, IBCF.MTME, OmicsQC, RESOLVE, WordListsAnalytics
Reverse suggests:	quanteda, quanteda.textmodels, Signac, SpatialDDLS

Linking:

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