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: | 1.5.1 |
| 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 (≥ 0.5.0),irlba,rsvd,rsparse |
| Published: | 2025-12-09 |
| 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 |