Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 1035–1044, Beijing, China, July 26-31, 2015. c⃝2015 Association for Computational Linguistics Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment Byron C. Wallace University of Texas at Austin byron.wallace@utexas.edu Do Kook Choe and Eugene Charniak Brown University {dc65, ec}@cs.brown.edu Abstract Automatically detecting verbal irony (roughly, sarcasm) in online content is important for many practical applications (e.g., sentiment detection), but it is dif- ficult. Previous approaches have relied predominantly on signal gleaned from word counts and grammatical cues. But such approaches fail to exploit the context in which comments are embedded. We thus propose a novel strategy for verbal irony classification that exploits contex- tual features, specifically by combining noun phrases and sentiment extracted from comments with the forum type (e.g., conservative or liberal) to which they were posted. We show that this approach improves verbal irony classifica- tion performance. Furthermore, because this method generates a very large feature space (and we expect predictive contextual features to be strong but few), we propose a mixed regularization strategy that places a sparsity-inducing ℓ1 penalty on the contextual feature weights on top of the ℓ2 penalty applied to all model coefficients. This increases model sparsity and reduces the variance of model performance. 1 Introduction and Motivation Automated verbal irony detection is a challenging problem.1 But recognizing when an author has in- tended a statement ironically is practically impor- tant for many text classification tasks (e.g., senti- ment detection). Previous models for irony detection (Tsur et al., 2010; Lukin and Walker, 2013; Riloff et al., 1In this paper we will be a bit cavalier in using the terms ‘verbal irony’ and ‘sarcasm’ interchangeably. We recognize that the latter is a special type of the former, the definition of which is difficult to pin down precisely. Guys who the fuck cares?! Leave him alone, there are real problems like bridge-gate scandal with Chris Cristie Figure 1: A reddit comment illustrating contextualizing fea- tures that we propose leveraging to improve classification. Here the highlighted entities (external the comment text it- self) provide contextual signals indicating that the shown comment was intended ironically. As we shall see, Oba- macare is in general a strong indicator of irony when present in posts to the conservative subreddit, but less so in posts to the progressive subreddit. 2013) have relied predominantly on features in- trinsic to the texts to be classified. By contrast, here we propose exploiting contextualizing infor- mation, which is often available for web-based classification tasks. More specifically, we exploit signal gleaned from the conversational threads to which comments belong. Our approach capital- izes on the intuition that members of different user communities are likely to be sarcastic about dif- ferent things. As a proxy for user community, we leverage knowledge of the specific forums to which comments were posted. For example, one may surmise that the statement ‘I really am …

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