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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: |
0.1.4 |
| 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
 [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:
Downloads:
Linking:
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https://CRAN.R-project.org/package=transforEmotion
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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