CASCADE: Contextual sarcasm detection in online discussion forums

Hazarika, D and Poria, S and Gorantla, S and Cambria, E and Zimmermann, R and Mihalcea, R (2018) CASCADE: Contextual sarcasm detection in online discussion forums. In: 27th International Conference on Computational Linguistics, COLING 2018, 20 - 26 August 2018, Santa Fe, pp. 1837-1848.

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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. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

Item Type:	Conference Paper
Publication:	COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings
Publisher:	Association for Computational Linguistics (ACL)
Additional Information:	The copyright for this article belongs to Association for Computational Linguistics (ACL)
Keywords:	Computational linguistics; Neural networks; Semantics; User profile, Background knowledge; Commonsense knowledge; Content-based features; Contextual information; Embeddings; Hybrid approach; Online discussion forums; Online social medias; Semantic levels; Stylometrics, Social networking (online)
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	14 Aug 2022 05:42
Last Modified:	14 Aug 2022 05:42
URI:	https://eprints.iisc.ac.in/id/eprint/75699

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