#NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets

Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 649–654 New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics #NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets Endang Wahyu Pamungkas, Viviana Patti Dipartimento di Informatica, University of Turin {pamungka,patti}@di.unito.it Abstract This paper describes the participation of the #NonDicevoSulSerio team at SemEval2018- Task3, which focused on Irony Detection in English Tweets and was articulated in two tasks addressing the identification of irony at different levels of granularity. We participated in both tasks proposed: Task A is a classical binary classification task to determine whether a tweet is ironic or not, while Task B is a multi- class classification task devoted to distinguish different types of irony, where systems have to predict one out of four labels describing verbal irony by clash, other verbal irony, sit- uational irony, and non-irony. We addressed both tasks by proposing a model built upon a well-engineered features set involving both syntactic and lexical features, and a wide range of affective-based features, covering different facets of sentiment and emotions. The use of new features for taking advantage of the affec- tive information conveyed by emojis has been analyzed. On this line, we also tried to ex- ploit the possible incongruity between senti- ment expressed in the text and in the emojis included in a tweet. We used a Support Vec- tor Machine classifier, and obtained promising results. We also carried on experiments in an unconstrained setting. 1 Introduction The use of creative language and figurative lan- guage devices such as irony has been proven to be pervasive in social media (Ghosh et al., 2015). The presence of these devices makes the process of mining social media texts challenging, espe- cially because they can influence and twist the sen- timent polarity of an utterance in different ways. Glossing over differences across different theoret- ical accounts proposed in the context of various disciplines (Gibbs and Colston, 2007; Grice, 1975; Wilson and Sperber, 1992; Attardo, 2007; Giora, 2003), irony can be defined as an incongruity be- tween the literal meaning of an utterance and its intended meaning (Karoui et al., 2017). The term irony covers mainly two phenomena: verbal and situational irony (Attardo, 2006). Situational irony refers to events or situations which fail to meet expectations, such as for instance “warnings the dangerous effect of smoking on the cigarette ad- vertisement”, while verbal irony occurs when the speaker intend to communicate a different mean- ing w.r.t what he/she is literally saying. Most of the time it involves the intention of commu- nicating an opposite meaning, and this kind of opposition can be expressed by polarity contrast. However this is not the only possibility, and so- cial media messages well reflect such variety, in- cluding different expressions of verbal irony and descriptions of situational irony (Van Hee et al., 2016a; Sulis et al., 2016). Automatic irony de- tection is an important task to improve sentiment analysis (Reyes et al., …

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