Package: seededlda 1.4.3
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_1.4.3.tar.gz
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seededlda_1.4.3.tgz(r-4.4-x86_64)seededlda_1.4.3.tgz(r-4.4-arm64)seededlda_1.4.3.tgz(r-4.3-x86_64)seededlda_1.4.3.tgz(r-4.3-arm64)
seededlda_1.4.3.tar.gz(r-4.5-noble)seededlda_1.4.3.tar.gz(r-4.4-noble)
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seededlda.pdf |seededlda.html✨
seededlda/json (API)
NEWS
| # Install 'seededlda' in R: |
| install.packages('seededlda', repos = c('https://koheiw.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
semi-supervised-learningtext-classificationonetbbcpp
Last updated 9 days agofrom:2b47a1b8f0. Checks:3 OK, 6 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 22 2025 |
| R-4.5-win-x86_64 | OK | Jan 22 2025 |
| R-4.5-linux-x86_64 | OK | Jan 22 2025 |
| R-4.4-win-x86_64 | NOTE | Jan 22 2025 |
| R-4.4-mac-x86_64 | NOTE | Jan 22 2025 |
| R-4.4-mac-aarch64 | NOTE | Jan 22 2025 |
| R-4.3-win-x86_64 | NOTE | Jan 22 2025 |
| R-4.3-mac-x86_64 | NOTE | Jan 22 2025 |
| R-4.3-mac-aarch64 | NOTE | Jan 22 2025 |
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 |
