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:Kohei Watanabe [aut, cre, cph], Phan Xuan-Hieu [aut, cph]

seededlda_1.4.4.tar.gz
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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://koheiw.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/koheiw/seededlda/issues

Pkgdown/docs site:https://koheiw.github.io

Uses libs:

onetbb– Parallelism library for C++
c++– GNU Standard C++ Library v3

Datasets:

data_corpus_moviereviews - Movie reviews from Pang and Lee

On CRAN:

semi-supervised-learning text-classification onetbb cpp

6.88 score 79 stars 1 packages 212 scripts 725 downloads 9 exports 36 dependencies

Last updated from:afb4cd02f2. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	347
linux-devel-x86_64	OK	390
source / vignettes	OK	212
linux-release-arm64	OK	379
linux-release-x86_64	OK	335
macos-release-arm64	OK	198
macos-release-x86_64	OK	704
macos-oldrel-arm64	OK	250
macos-oldrel-x86_64	OK	516
windows-devel	OK	442
windows-release	OK	471
windows-oldrel	OK	533
wasm-release	OK	127

Exports:divergence info_tbb perplexity sizes terms textmodel_lda textmodel_seededlda textmodel_seqlda topics

Dependencies:brio callr cli crayon desc diffobj evaluate fastmatch fs glue ISOcodes jsonlite lattice lifecycle magrittr Matrix pkgbuild pkgload praise processx proxyC ps quanteda R6 Rcpp RcppArmadillo rlang rprojroot SnowballC stopwords stringi testthat waldo withr xml2 yaml

Citation

Development and contributors

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