A word embeddings-based semisupervised model 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: | 0.9.1 |
| 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: | 2020-09-14 |
| 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 |
| Reference manual: | LSX.pdf |
| Package source: | LSX_0.9.1.tar.gz |
| Windows binaries: | r-devel: LSX_0.9.0.zip, r-release: LSX_0.9.0.zip, r-oldrel: LSX_0.9.0.zip |
| macOS binaries: | r-release: LSX_0.9.1.tgz, r-oldrel: LSX_0.9.1.tgz |
| Old sources: | LSX archive |
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