transforEmotion: Sentiment Analysis for Text and Qualitative Data

Implements sentiment analysis using huggingface <> transformer zero-shot classification model pipelines. The default pipeline is Cross-Encoder's DistilRoBERTa <> trained on the Stanford Natural Language Inference <> and Multi-Genre Natural Language Inference <> datasets. Using similar models, zero-shot classification transformers have demonstrated superior performance relative to other natural language processing models <arXiv:1909.00161>. All other zero-shot classification model pipelines can be implemented using their model name from <>}.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: reticulate, pbapply, osfr, LSAfun, dplyr, remotes
Suggests: markdown, knitr, rmarkdown, rstudioapi
Published: 2022-05-11
Author: Alexander Christensen ORCID iD [aut, cre], Hudson Golino ORCID iD [aut]
Maintainer: Alexander Christensen <alexpaulchristensen at>
License: GPL (≥ 3.0)
NeedsCompilation: no
Citation: transforEmotion citation info
Materials: NEWS
CRAN checks: transforEmotion results


Reference manual: transforEmotion.pdf
Vignettes: Python Setup


Package source: transforEmotion_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): transforEmotion_0.1.1.tgz, r-oldrel (arm64): transforEmotion_0.1.1.tgz, r-release (x86_64): transforEmotion_0.1.1.tgz, r-oldrel (x86_64): transforEmotion_0.1.1.tgz
Old sources: transforEmotion archive


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