LSX: Semi-Supervised Algorithm for Document Scaling (original) (raw)
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: | 1.4.0 |
|---|---|
| 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: | knitr, rmarkdown, testthat |
| Published: | 2024-03-05 |
| 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 |
| Materials: | NEWS |
| CRAN checks: | LSX results |
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