ACL 2021short4 citations
Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter
Boaz Shmueli, Soumya Ray, Lun-Wei Ku
Abstract
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.
BibTeX
@inproceedings{shmueli-etal-2021-happy,
title = "Happy Dance, Slow Clap: {Using} Reaction {GIFs} to Predict Induced Affect on {Twitter}",
author = "Shmueli, Boaz and
Ray, Soumya and
Ku, Lun-Wei",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.50/",
doi = "10.18653/v1/2021.acl-short.50",
pages = "395--401"
}