LSX: Model for Semisupervised Text Analysis Based on Word Embeddings

A word embeddings-based semisupervised 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: 1.0.2
Depends: methods, R (≥ 3.5.0)
Imports: quanteda (≥ 2.0), quanteda.textstats, stringi, digest, Matrix, RSpectra, irlba, rsvd, rsparse, proxyC, stats, ggplot2, ggrepel, reshape2, locfit
Suggests: testthat
Published: 2021-09-18
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei at gmail.com>
BugReports: https://github.com/koheiw/LSX/issues
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: LSX results

Downloads:

Reference manual: LSX.pdf
Package source: LSX_1.0.2.tar.gz
Windows binaries: r-devel: LSX_1.0.0.zip, r-release: LSX_1.0.2.zip, r-oldrel: LSX_1.0.2.zip
macOS binaries: r-release (arm64): LSX_1.0.0.tgz, r-release (x86_64): LSX_1.0.2.tgz, r-oldrel: LSX_1.0.2.tgz
Old sources: LSX archive

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