Harnessing Cognitive Features for Sarcasm Detection

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1095–1104, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Harnessing Cognitive Features for Sarcasm Detection Abhijit Mishra†, Diptesh Kanojia†, Seema Nagar ⋆, Kuntal Dey⋆, Pushpak Bhattacharyya† †Indian Institute of Technology Bombay, India ⋆IBM Research, India †{abhijitmishra, diptesh, pb}@cse.iitb.ac.in ⋆{senagar3, kuntadey}@in.ibm.com Abstract In this paper, we propose a novel mecha- nism for enriching the feature vector, for the task of sarcasm detection, with cogni- tive features extracted from eye-movement patterns of human readers. Sarcasm detec- tion has been a challenging research prob- lem, and its importance for NLP applica- tions such as review summarization, dia- log systems and sentiment analysis is well recognized. Sarcasm can often be traced to incongruity that becomes apparent as the full sentence unfolds. This presence of incongruity- implicit or explicit- affects the way readers eyes move through the text. We observe the difference in the be- haviour of the eye, while reading sarcastic and non sarcastic sentences. Motivated by this observation, we augment traditional linguistic and stylistic features for sarcasm detection with the cognitive features ob- tained from readers eye movement data. We perform statistical classification using the enhanced feature set so obtained. The augmented cognitive features improve sar- casm detection by 3.7% (in terms of F- score), over the performance of the best reported system. 1 Introduction Sarcasm is an intensive, indirect and complex con- struct that is often intended to express contempt or ridicule 1. Sarcasm, in speech, is multi-modal, involving tone, body-language and gestures along with linguistic artifacts used in speech. Sarcasm in text, on the other hand, is more restrictive when it comes to such non-linguistic modalities. This makes recognizing textual sarcasm more challeng- ing for both humans and machines. 1The Free Dictionary Sarcasm detection plays an indispensable role in applications like online review summarizers, di- alog systems, recommendation systems and senti- ment analyzers. This makes automatic detection of sarcasm an important problem. However, it has been quite difficult to solve such a problem with traditional NLP tools and techniques. This is apparent from the results reported by the survey from Joshi et al. (2016). The following discussion brings more insights into this. Consider a scenario where an online reviewer gives a negative opinion about a movie through sarcasm: “This is the kind of movie you see be- cause the theater has air conditioning”. It is dif- ficult for an automatic sentiment analyzer to as- sign a rating to the movie and, in the absence of any other information, such a system may not be able to comprehend that prioritizing the air-conditioning facilities of the theater over the movie experience indicates a negative sentiment towards the movie. This gives an intuition to why, for sarcasm detection, it is necessary to go beyond textual analysis. We aim to address this problem by exploiting the psycholinguistic side of sarcasm detection, us- ing cognitive features extracted with the help of eye-tracking. A motivation to consider cogni- tive features comes from …

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