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
Depends: R (≥ 3.6.0), methods
Imports: Matrix (≥ 1.1), 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: 2020-02-18
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
SystemRequirements: C++11
Materials: README NEWS
In views: NaturalLanguageProcessing
CRAN checks: text2vec results

Downloads:

Reference manual: text2vec.pdf
Vignettes: Advanced topics
GloVe Word Embeddings
Analyzing Texts with the text2vec Package
Package source: text2vec_0.6.tar.gz
Windows binaries: r-prerelease: text2vec_0.6.zip, r-release: text2vec_0.6.zip, r-oldrel: text2vec_0.5.1.zip
macOS binaries: r-prerelease: text2vec_0.6.tgz, r-release: text2vec_0.6.tgz, r-oldrel: text2vec_0.5.1.tgz
Old sources: text2vec archive

Reverse dependencies:

Reverse imports: fdm2id, oolong, textfeatures, textmineR, wactor
Reverse suggests: lime, quanteda, textrecipes

Linking:

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