A word embeddings-based semi-supervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>. LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove). It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.
Version: 1.4.5 Depends: R (≥ 3.5.0) Imports: methods, quanteda (≥ 2.0), quanteda.textstats, stringi, digest, Matrix, RSpectra, proxyC, stats, ggplot2, ggrepel, reshape2, locfit Suggests: testthat, spelling, knitr, rmarkdown, wordvector, irlba, rsvd, rsparse Published: 2025-06-19 DOI: 10.32614/CRAN.package.LSX Author: Kohei Watanabe [aut, cre, cph] Maintainer: Kohei Watanabe <watanabe.kohei at gmail.com> BugReports: https://github.com/koheiw/LSX/issues License: GPL-3 URL: https://koheiw.github.io/LSX/ NeedsCompilation: no Language: en-US Citation: LSX citation info Materials: NEWS CRAN checks: LSX results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=LSX to link to this page.
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