Package: seededlda 1.4.1
Kohei Watanabe
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_1.4.1.tar.gz
seededlda_1.4.1.tar.gz(r-4.5-noble)seededlda_1.4.1.tar.gz(r-4.4-noble)
seededlda_1.4.1.tgz(r-4.4-emscripten)seededlda_1.4.1.tgz(r-4.3-emscripten)
seededlda.pdf |seededlda.html✨
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 site:https://koheiw.github.io
- data_corpus_moviereviews - Movie reviews from Pang and Lee
Last updated 4 months agofrom:957ad9b9b8. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 07 2024 |
| R-4.5-linux-x86_64 | OK | Dec 07 2024 |
Exports:divergenceinfo_tbbperplexitysizestermstextmodel_ldatextmodel_seededldatextmodel_seqldatopics
Dependencies:briocallrclicrayondescdiffobjdigestevaluatefastmatchfsglueISOcodesjsonlitelatticelifecyclemagrittrMatrixpkgbuildpkgloadpraiseprocessxproxyCpsquantedaR6RcppRcppArmadillorlangrprojrootSnowballCstopwordsstringitestthatwaldowithrxml2yaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| 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 |
