transforEmotion: Sentiment Analysis for Text, Image and Video using Transformer Models

Implements sentiment analysis using huggingface <> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <> and default image/video pipeline is Open AI's CLIP <>. All other zero-shot classification model pipelines can be implemented using their model name from <>.

Version: 0.1.4
Depends: R (≥ 3.5.0)
Imports: reticulate, pbapply, googledrive, LSAfun, dplyr, remotes, Matrix
Suggests: markdown, knitr, rmarkdown, rstudioapi, testthat (≥ 3.0.0)
Published: 2024-01-09
Author: Alexander Christensen ORCID iD [aut], Hudson Golino ORCID iD [aut], Aleksandar Tomašević ORCID iD [aut, cre]
Maintainer: Aleksandar Tomašević <atomashevic at>
License: GPL (≥ 3.0)
NeedsCompilation: no
Citation: transforEmotion citation info
Materials: README NEWS
CRAN checks: transforEmotion results


Reference manual: transforEmotion.pdf
Vignettes: Setup and Tutorial


Package source: transforEmotion_0.1.4.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): transforEmotion_0.1.4.tgz, r-release (arm64): transforEmotion_0.1.4.tgz, r-oldrel (arm64): transforEmotion_0.1.4.tgz, r-prerel (x86_64): transforEmotion_0.1.4.tgz, r-release (x86_64): transforEmotion_0.1.4.tgz
Old sources: transforEmotion archive


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