Modelling Irony in Twitter

Computational creativity is one of the central research topics of Artificial Intelligence and Natural Language Processing today. Irony, a creative use of language, has received very little attention from the computational linguistics research point of view. In this study we investigate the automatic detection of irony casting it as a classification problem. We propose a model capable of detecting irony in the social network Twitter. In cross-domain classification experiments our model based on lexical features outperforms a word-based baseline previously used in opinion mining and achieves state-of-the-art performance. Our features are simple to implement making the approach easily replicable.