LSX: Model for Semisupervised Text Analysis Based on Word Embeddings

A word embeddings-based semisupervised models for document scaling Watanabe (2017) <doi:10.1177/0267323117695735>. LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).

Version: 0.9.2
Depends: quanteda (≥ 2.0), quanteda.textmodels, methods, R (≥ 3.5.0)
Imports: digest, Matrix, RSpectra, irlba, rsvd, rsparse, proxyC, grDevices, stats, ggplot2, ggrepel, reshape2, e1071
Suggests: testthat
Published: 2020-09-22
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: LSX results

Downloads:

Reference manual: LSX.pdf
Package source: LSX_0.9.2.tar.gz
Windows binaries: r-devel: LSX_0.9.2.zip, r-release: LSX_0.9.2.zip, r-oldrel: LSX_0.9.2.zip
macOS binaries: r-release: LSX_0.9.2.tgz, r-oldrel: LSX_0.9.2.tgz
Old sources: LSX archive

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