sentiment.ai: Simple Sentiment Analysis Using Deep Learning

Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>), it also allows the user to create embedding vectors for text which can be used in other analyses. GPU acceleration is supported on Windows and Linux.

Version: 0.1.1
Depends: R (≥ 4.0.0)
Imports: data.table (≥ 1.12.8), jsonlite, reticulate (≥ 1.16), roperators (≥ 1.2.0), stats, tensorflow (≥ 2.2.0), tfhub (≥ 0.8.0), utils, xgboost
Suggests: rmarkdown, knitr, magrittr, microbenchmark, prettydoc, rappdirs, rstudioapi, text2vec (≥ 0.6)
Published: 2022-03-19
Author: Ben Wiseman [cre, aut, ccp], Steven Nydick ORCID iD [aut], Tristan Wisner [aut], Fiona Lodge [ctb], Yu-Ann Wang [ctb], Veronica Ge [art], Korn Ferry Institute [fnd]
Maintainer: Ben Wiseman <benjamin.h.wiseman at gmail.com>
License: MIT + file LICENSE
URL: https://benwiseman.github.io/sentiment.ai/, https://github.com/BenWiseman/sentiment.ai
NeedsCompilation: no
Materials: README NEWS
CRAN checks: sentiment.ai results

Documentation:

Reference manual: sentiment.ai.pdf
Vignettes: sentiment.ai

Downloads:

Package source: sentiment.ai_0.1.1.tar.gz
Windows binaries: r-devel: sentiment.ai_0.1.1.zip, r-release: sentiment.ai_0.1.1.zip, r-oldrel: sentiment.ai_0.1.1.zip
macOS binaries: r-release (arm64): sentiment.ai_0.1.1.tgz, r-release (x86_64): sentiment.ai_0.1.1.tgz, r-oldrel: sentiment.ai_0.1.1.tgz
Old sources: sentiment.ai archive

Linking:

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