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 + fileLICENSE |
| 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 results |