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 results |