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.

Install the latest version of this package by entering the following in R:
AuthorFridolin Wild
Date of publication2015-05-08 19:58:09
MaintainerFridolin Wild <>
LicenseGPL (>= 2)

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alnumx Man page
associate Man page
as.textmatrix Man page
corpus_essays Man page
corpus_scores Man page
corpus_training Man page
cosine Man page
delTriple Man page
dimcalc Man page
dimcalc_fraction Man page
dimcalc_kaiser Man page
dimcalc_ndocs Man page
dimcalc_raw Man page
dimcalc_share Man page
entropy Man page
fold_in Man page
getSubjectId Man page
getTriple Man page
gw_entropy Man page
gw_gfidf Man page
gw_idf Man page
gw_normalisation Man page
lsa Man page
lw_bintf Man page
lw_logtf Man page
lw_tf Man page
print.textmatrix Man page
query Man page
sample.textmatrix Man page
setTriple Man page
specialchars Man page
stopwords_ar Man page
stopwords_de Man page
stopwords_en Man page
stopwords_fr Man page
stopwords_nl Man page
stopwords_pl Man page
summary.textmatrix Man page
textmatrix Man page
textvector Man page

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