Computational rule-based model for Irony Detection in Italian Tweets

In the domain of Natural Language Processing (NLP), the interest in figurative language is enhanced, especially in the last few years, thanks to the amount of linguistic data provided by web and social networks. Figurative language provides a non-literary sense to the words, thus the utterances require several interpretations disclosing the play of signification. In order to individuate different meaning levels in case of ironic texts detection, it is necessary a computational model appropriated to the complexity of rhetorical artifice. In this paper we describe our rule-based system of irony detection as it has been presented to the SENTIPOLC task of EVALITA 2016, where we ranked third on twelve participants.