Reactive Supervision: A New Method for Collecting Sarcasm Data

Boaz Shmueli, Lun-Wei Ku, Soumya Ray


Abstract
Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection research. Our method can be adapted to other affective computing domains, thus opening up new research opportunities.
Anthology ID:
2020.emnlp-main.201
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2553–2559
Language:
URL:
https://aclanthology.org/2020.emnlp-main.201
DOI:
10.18653/v1/2020.emnlp-main.201
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.201.pdf
Video:
 https://slideslive.com/38938693