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text2vec: Modern Text Mining Framework for R

Fast and memory-friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities. This package provides a source-agnostic streaming API, which allows researchers to perform analysis of collections of documents which are larger than available RAM. All core functions are parallelized to benefit from multicore machines.

Version: 0.6.4
Depends: R (≥ 3.6.0), methods
Imports: Matrix (≥ 1.5-2), Rcpp (≥ 1.0.3), R6 (≥ 2.3.0), data.table (≥ 1.9.6), rsparse (≥ 0.3.3.4), stringi (≥ 1.1.5), mlapi (≥ 0.1.0), lgr (≥ 0.2), digest (≥ 0.6.8)
LinkingTo: Rcpp, digest (≥ 0.6.8)
Suggests: magrittr, udpipe (≥ 0.6), glmnet, testthat, covr, knitr, rmarkdown, proxy
Published: 2023-11-09
DOI: 10.32614/CRAN.package.text2vec
Author: Dmitriy Selivanov [aut, cre, cph], Manuel Bickel [aut, cph] (Coherence measures for topic models), Qing Wang [aut, cph] (Author of the WaprLDA C++ code)
Maintainer: Dmitriy Selivanov <selivanov.dmitriy at gmail.com>
BugReports: https://github.com/dselivanov/text2vec/issues
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL: http://text2vec.org
NeedsCompilation: yes
Materials: README NEWS
In views: NaturalLanguageProcessing
CRAN checks: text2vec results

Documentation:

Reference manual: text2vec.pdf
Vignettes: GloVe Word Embeddings
Analyzing Texts with the text2vec Package

Downloads:

Package source: text2vec_0.6.4.tar.gz
Windows binaries: r-devel: text2vec_0.6.4.zip, r-release: text2vec_0.6.4.zip, r-oldrel: text2vec_0.6.4.zip
macOS binaries: r-release (arm64): text2vec_0.6.4.tgz, r-oldrel (arm64): text2vec_0.6.4.tgz, r-release (x86_64): text2vec_0.6.4.tgz, r-oldrel (x86_64): text2vec_0.6.4.tgz
Old sources: text2vec archive

Reverse dependencies:

Reverse imports: conText, kgraph, NUSS, occupationMeasurement, PsychWordVec, regtools, text2map, textmineR, ttgsea, wactor, wordsalad
Reverse suggests: fdm2id, lime, oolong, opticskxi, polyglotr, sentiment.ai, textrecipes
Reverse enhances: quanteda

Linking:

Please use the canonical form https://CRAN.R-project.org/package=text2vec to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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