CASCADE: Contextual Sarcasm Detection in Online Discussion Forums

Proceedings of the 27th International Conference on Computational Linguistics, pages 1837–1848 Santa Fe, New Mexico, USA, August 20-26, 2018. 1837 CASCADE: Contextual Sarcasm Detection in Online Discussion Forums Devamanyu Hazarika School of Computing, National University of Singapore hazarika@comp.nus.edu.sg Soujanya Poria Artificial Intelligence Initiative, A*STAR, Singapore sporia@ihpc.a-star.edu.sg Sruthi Gorantla Computer Science & Automation, Indian Institute of Science, Bangalore gorantlas@iisc.ac.in Erik Cambria School of Computer Science and Engineering, NTU, Singapore cambria@ntu.edu.sg Roger Zimmermann School of Computing, National University of Singapore rogerz@comp.nus.edu.sg Rada Mihalcea Computer Science & Engineering, University of Michigan, Ann Arbor mihalcea@umich.edu 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 Contex- tuAl 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. 1 Introduction Sarcasm is a linguistic tool that uses irony to express contempt. Its figurative nature poses a great challenge for affective systems performing sentiment analysis (Cambria et al., 2017). Previous research in automated sarcasm detection has primarily focused on lexical and pragmatic cues found in sentences (Kreuz and Caucci, 2007). In the literature, interjections, punctuations, and sentimental shifts have been considered as major indicators of sarcasm (Joshi et al., 2017). When such lexical cues are present in sentences, sarcasm detection can achieve high accuracy. However, sarcasm is also expressed implicitly, i.e., without the presence of such lexical cues. This use of sarcasm also relies on context, which involves the presumption of commonsense and background knowledge of an event. When it comes to detecting sarcasm in a discussion forum, it may not only be required to understand the context of previous comments but also the necessary background knowledge about the topic of discussion. The usage of slangs and informal language also diminishes the reliance on lexical cues (Satapathy et al., 2017). This particular type of sarcasm is tough to detect (Poria et al., 2016). Contextual dependencies for sarcasm can take many forms. As an example, a sarcastic post from Reddit1, “I’m sure Hillary would’ve done that, lmao.” requires background knowledge about the event, i.e., Hillary Clinton’s action at the time the post was made. Similarly, sarcastic posts like “But atheism, yeah *that’s* a religion!” requires the knowledge that topics like atheism often contain argumentative discussions and, hence, they are more prone towards sarcasm. The main aim of this work is sarcasm detection in online discussion forums. In particular, we propose a hybrid network, named CASCADE, that leverages both …

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