LSX: Semisupervised Document Scaling by Word-Embedding Models
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: |
1.1.1 |
| 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: |
testthat |
| Published: |
2022-02-26 |
| 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 |
Documentation:
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