Simple Sentiment Analysis Using Deep Learning R package sentiment.ai version 0.1.1 CRAN Task View: NaturalLanguageProcessing (https://CRAN.R-project.org/view=NaturalLanguageProcessing) 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. Software 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) Ben Wiseman <benjamin.h.wiseman@gmail.com> Comprehensive R Archive Network (CRAN) Ben Wiseman [cre, aut, ccp], Steven Nydick [aut] (ORCID: <https://orcid.org/0000-0002-2908-1188>), Tristan Wisner [aut], Fiona Lodge [ctb], Yu-Ann Wang [ctb], Veronica Ge [art], Korn Ferry Institute [fnd] MIT + file LICENSE (https://CRAN.R-project.org/package=sentiment.ai/LICENSE) 2022-03-19 application/tgz https://CRAN.R-project.org/package=sentiment.ai doi:10.32614/CRAN.package.sentiment.ai