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.
Documentation: Downloads: Reverse dependencies: Reverse depends: AurieLSHGaussian, LSAfun Reverse imports: ccmap, CellScore, conversim, CoreGx, DTWBI, DTWUMI, GeneNMF, IBCF.MTME, MD2sample, OmicsQC, OutSeekR, RESOLVE, WordListsAnalytics Reverse suggests: quanteda, quanteda.textmodels, Signac, SpatialDDLS Linking:Please use the canonical form https://CRAN.R-project.org/package=lsa to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4