Papers › CASCADE: Contextual Sarcasm Detection in Online Discussion Forums

CASCADE: Contextual Sarcasm Detection in Online Discussion Forums

16 May 2018COLING 2018 8arXiv:1805.06413archive 2025-07-28

Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, Rada Mihalcea

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 CASCADE (a ContextuAl SarCasm DEtector) that 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 the users. When used along with content-based feature extractors such as Convolutional Neural Networks (CNNs), we see a significant boost in the classification performance on a large Reddit corpus.

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General ClassificationSarcasm DetectionSentence

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Sarcasm Detection SARC (all-bal) CASCADE Accuracy 77 #1 of 2 Archive leaderboard report
Sarcasm Detection SARC (pol-bal) CASCADE Accuracy 74 #2 of 2 Archive leaderboard report

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