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
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- 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
- Cite (ACL):
- Boaz Shmueli, Lun-Wei Ku, and Soumya Ray. 2020. Reactive Supervision: A New Method for Collecting Sarcasm Data. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2553–2559, Online. Association for Computational Linguistics.
- Cite (Informal):
- Reactive Supervision: A New Method for Collecting Sarcasm Data (Shmueli et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2020.emnlp-main.201.pdf
- Code
- bshmueli/SPIRS
- Data
- SPIRS