CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
This work addresses the problem of improving sarcasm detection accuracy for social media analysis, though it is incremental as it builds on existing methods by adding contextual and user-specific features.
The paper tackles sarcasm detection in online discussions by proposing CASCADE, a hybrid model that combines content-based features with contextual information from discussion threads and user embeddings, achieving a significant performance boost on a Reddit corpus.
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.