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: | 0.9.6 |
Depends: | methods, R (≥ 3.5.0) |
Imports: | quanteda (≥ 2.0), quanteda.textmodels, quanteda.textstats, stringi, digest, Matrix, RSpectra, irlba, rsvd, rsparse, proxyC, grDevices, stats, ggplot2, ggrepel, reshape2, e1071, locfit |
Suggests: | testthat |
Published: | 2020-12-17 |
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 |
Reference manual: | LSX.pdf |
Package source: | LSX_0.9.6.tar.gz |
Windows binaries: | r-devel: LSX_0.9.6.zip, r-release: LSX_0.9.6.zip, r-oldrel: LSX_0.9.6.zip |
macOS binaries: | r-release: LSX_0.9.6.tgz, r-oldrel: LSX_0.9.6.tgz |
Old sources: | LSX archive |
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