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 DOI: 10.32614/CRAN.package.sentiment.ai Author: Ben Wiseman [cre, aut, ccp], Steven Nydick [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 In views: NaturalLanguageProcessing CRAN checks: sentiment.ai resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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