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

Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.

Version:	0.1.5
Depends:	R (≥ 3.5.0)
Imports:	dplyr, googledrive, LSAfun, Matrix, methods, pbapply, remotes, reticulate
Suggests:	knitr, markdown, rmarkdown, rstudioapi, testthat (≥ 3.0.0)
Published:	2024-08-20
DOI:	10.32614/CRAN.package.transforEmotion
Author:	Alexander Christensen [aut], Hudson Golino [aut], Aleksandar Tomašević [aut, cre]
Maintainer:	Aleksandar Tomašević <atomashevic at gmail.com>
License:	GPL (≥ 3.0)
NeedsCompilation:	no
Citation:	transforEmotion citation info
Materials:	README NEWS
CRAN checks:	transforEmotion results

Documentation:

Reference manual:	transforEmotion.pdf
Vignettes:	Setup and Tutorial (source, R code)

Downloads:

Package source:	transforEmotion_0.1.5.tar.gz
Windows binaries:	r-devel: transforEmotion_0.1.5.zip, r-release: transforEmotion_0.1.5.zip, r-oldrel: transforEmotion_0.1.5.zip
macOS binaries:	r-release (arm64): transforEmotion_0.1.5.tgz, r-oldrel (arm64): transforEmotion_0.1.5.tgz, r-release (x86_64): transforEmotion_0.1.5.tgz, r-oldrel (x86_64): transforEmotion_0.1.5.tgz
Old sources:	transforEmotion archive

Linking:

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