CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, Rada Mihalcea
Abstract
The literature in automated sarcasm detection has mainly focused on lexical-, syntactic- and semantic-level analysis of text. However, a sarcastic sentence can be expressed with contextual presumptions, background and commonsense knowledge. In this paper, we propose a ContextuAl SarCasm DEtector (CASCADE), which adopts a hybrid approach of both content- and context-driven modeling for sarcasm detection in online social media discussions. For the latter, CASCADE aims at extracting contextual information from the discourse of a discussion thread. Also, since the sarcastic nature and form of expression can vary from person to person, CASCADE utilizes user embeddings that encode stylometric and personality features of users. When used along with content-based feature extractors such as convolutional neural networks, we see a significant boost in the classification performance on a large Reddit corpus.- Anthology ID:
- C18-1156
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1837–1848
- Language:
- URL:
- https://aclanthology.org/C18-1156
- DOI:
- Cite (ACL):
- Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, and Rada Mihalcea. 2018. CASCADE: Contextual Sarcasm Detection in Online Discussion Forums. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1837–1848, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- CASCADE: Contextual Sarcasm Detection in Online Discussion Forums (Hazarika et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/C18-1156.pdf
- Data
- SARC