# Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter

arXiv: https://arxiv.org/abs/2105.09967 · HTML: https://mlsanity.com/paper/2105.09967/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 5 of 5)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [TGIF: A New Dataset and Benchmark on Animated GIF Description](https://mlsanity.com/paper/1604.02748/) (2016)
- [Emotion Detection in Text: a Review](https://mlsanity.com/paper/1806.00674/) (2018)
- [Distant supervision for emotion detection using Facebook reactions](https://mlsanity.com/paper/1611.02988/) (2016)

## Built on by (most cited first, 2 of 2)

- [An Effective Deployment of Contrastive Learning in Multi-label Text Classification](https://mlsanity.com/paper/2212.00552/) (2022)
- [Multimodal Sentiment Analysis: Perceived vs Induced Sentiments](https://mlsanity.com/paper/2312.07627/) (2023)

## Abstract (arXiv)

Datasets with induced emotion labels are scarce but of utmost importance for many NLP tasks. We present a new, automated method for collecting texts along with their induced reaction labels. The method exploits the online use of reaction GIFs, which capture complex affective states. We show how to augment the data with induced emotion and induced sentiment labels. We use our method to create and publish ReactionGIF, a first-of-its-kind affective dataset of 30K tweets. We provide baselines for three new tasks, including induced sentiment prediction and multilabel classification of induced emotions. Our method and dataset open new research opportunities in emotion detection and affective computing.