CRAN/E | sentiment.ai

sentiment.ai

Simple Sentiment Analysis Using Deep Learning

Installation

About

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 ), 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.

benwiseman.github.io/sentiment.ai/
github.com/BenWiseman/sentiment.ai

Key Metrics

Version 0.1.1
R ≥ 4.0.0
Published 2022-03-19 772 days ago
Needs compilation? no
License MIT
License File
CRAN checks sentiment.ai results

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Maintainer

Maintainer

Ben Wiseman

benjamin.h.wiseman@gmail.com

Authors

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

Material

README
NEWS
Reference manual
Package source

Vignettes

sentiment.ai

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

sentiment.ai archive

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