Package: LSX 1.4.1
LSX: Semi-Supervised Algorithm for Document Scaling
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.
Authors:
LSX_1.4.1.tar.gz
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LSX.pdf |LSX.html✨
LSX/json (API)
NEWS
| # Install 'LSX' in R: |
| install.packages('LSX', repos = c('https://koheiw.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/koheiw/lsx/issues
- data_dictionary_ideology - Seed words for analysis of left-right political ideology
- data_dictionary_sentiment - Seed words for analysis of positive-negative sentiment
- data_textmodel_lss_russianprotests - A fitted LSS model on street protest in Russia
lsaquantedasentiment-analysistext-analysis
Last updated 4 months agofrom:1a06b33dd8. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 20 2024 |
| R-4.5-win | NOTE | Nov 20 2024 |
| R-4.5-linux | NOTE | Nov 20 2024 |
| R-4.4-win | NOTE | Nov 20 2024 |
| R-4.4-mac | NOTE | Nov 20 2024 |
| R-4.3-win | NOTE | Nov 20 2024 |
| R-4.3-mac | NOTE | Nov 20 2024 |
Exports:as.coefficients_textmodelas.seedwordsas.statistics_textmodelas.summary.textmodelas.textmodel_lssbootstrap_lsschar_contextcoefficients.textmodel_lsscohesiondiagnosysoptimize_lssseedwordssmooth_lsstextmodel_lsstextplot_componentstextplot_similtextplot_termstextstat_context
Dependencies:clicolorspacedata.tabledigestfansifarverfastmatchfloatggplot2ggrepelgluegtableirlbaisobandISOcodesjsonlitelabelinglatticelgrlifecyclelocfitmagrittrMASSMatrixMatrixExtramgcvmunsellnlmensyllablepillarpkgconfigplyrproxyCquantedaquanteda.textstatsR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2RhpcBLASctlrlangrsparseRSpectrarsvdscalesSnowballCstopwordsstringistringrtibbleutf8vctrsviridisLitewithrxml2yaml
