Package: seededlda 1.4.4
seededlda: Seeded Sequential LDA for Topic Modeling
Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>. Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.
Authors:
seededlda_1.4.4.tar.gz
seededlda_1.4.4.tar.gz(r-4.7-arm64)seededlda_1.4.4.tar.gz(r-4.7-x86_64)seededlda_1.4.4.tar.gz(r-4.6-arm64)seededlda_1.4.4.tar.gz(r-4.6-x86_64)
seededlda_1.4.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
seededlda/json (API)
NEWS
| # Install 'seededlda' in R: |
| install.packages('seededlda', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/koheiw/seededlda/issues
Pkgdown/docs site:https://koheiw.github.io
- data_corpus_moviereviews - Movie reviews from Pang and Lee
Last updated from:7be987c9f4. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 353 | ||
| linux-devel-x86_64 | OK | 296 | ||
| source / vignettes | OK | 218 | ||
| linux-release-arm64 | OK | 357 | ||
| linux-release-x86_64 | OK | 364 | ||
| wasm-release | OK | 150 |
Exports:divergenceinfo_tbbperplexitysizestermstextmodel_ldatextmodel_seededldatextmodel_seqldatopics
Dependencies:briocallrclicrayondescdiffobjevaluatefastmatchfsglueISOcodesjsonlitelatticelifecyclemagrittrMatrixpkgbuildpkgloadpraiseprocessxproxyCpsquantedaR6RcppRcppArmadillorlangrprojrootSnowballCstopwordsstringitestthatwaldowithrxml2yaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create a dictionary from topic terms | as.dictionary.textmodel_lda |
| Movie reviews from Pang and Lee (2004) | data_corpus_moviereviews |
| Optimize the number of topics for LDA | divergence |
| Optimize the hyper-parameters for LDA | perplexity |
| Compute the sizes of topics | sizes |
| Extract most likely terms | terms |
| Unsupervised latent Dirichlet allocation | textmodel_lda |
| Semisupervised latent Dirichlet allocation | textmodel_seededlda |
| Sequential latent Dirichlet allocation | textmodel_seqlda |
| Extract most likely topics | topics |
