Sentiment Analysis for Text, Image and Video using Transformer Models R package transforEmotion version 0.1.7 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>. Software Depends: R (>= 3.5.0) Imports: dplyr, googledrive, LSAfun, Matrix, methods, pbapply, remotes, reticulate, textdata, jsonlite, ggplot2, reshape2, httr Suggests: knitr, markdown, rmarkdown, rstudioapi, testthat (>= 3.0.0) Aleksandar Tomašević <atomashevic@gmail.com> Comprehensive R Archive Network (CRAN) Alexander Christensen [aut] (ORCID: <https://orcid.org/0000-0002-9798-7037>), Hudson Golino [aut] (ORCID: <https://orcid.org/0000-0002-1601-1447>), Aleksandar Tomašević [aut, cre] (ORCID: <https://orcid.org/0000-0003-4863-6051>) GPL (>= 3.0) 2026-01-08 application/tgz https://CRAN.R-project.org/package=transforEmotion doi:10.32614/CRAN.package.transforEmotion