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: |
1.5.0 |
| 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, irlba, rsvd, rsparse |
| Published: |
2025-09-12 |
| 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 |