Modelling Sarcasm in Twitter, a Novel Approach

A novel computational model capable to detect sarcasm in the social network Twitter (a popular microblogging service which allows users to post short messages), which is easy to implement and, unlike previous systems, does not include patterns of words as features.

Abstract

Automatic detection of figurative language is a challenging task in computational lin-guistics. Recognising both literal and fig-urative meaning is not trivial for a ma-chine and in some cases it is hard even for humans. For this reason novel and accurate systems able to recognise figura-tive languages are necessary. We present in this paper a novel computational model capable to detect sarcasm in the social network Twitter (a popular microblogging service which allows users to post short messages). Our model is easy to imple-ment and, unlike previous systems, it does not include patterns of words as features. Our seven sets of lexical features aim to detect sarcasm by its inner structure (for example unexpectedness, intensity of the terms or imbalance between registers), ab-stracting from the use of specific terms. 1