task_categories:text-classification task_ids:sentiment-classification annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:unknown arxiv:2105.09967 region:us
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: reactiongif
---


## ReactionGIF

> From https://github.com/bshmueli/ReactionGIF

![gif](https://huggingface.co/datasets/julien-c/reactiongif/resolve/main/hug.gif)


___

## Excerpt from original repo readme

ReactionGIF is a unique, first-of-its-kind dataset of 30K sarcastic tweets and their GIF reactions. 

To find out more about ReactionGIF, 
check out our ACL 2021 paper:

* Shmueli, Ray and Ku, [Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter](https://arxiv.org/abs/2105.09967)


## Citation

If you use our dataset, kindly cite the paper using the following BibTex entry:

```bibtex
@misc{shmueli2021happy,
      title={Happy Dance, Slow Clap: Using Reaction {GIFs} to Predict Induced Affect on {Twitter}}, 
      author={Boaz Shmueli and Soumya Ray and Lun-Wei Ku},
      year={2021},
      eprint={2105.09967},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

