Computational models for irony detection in three Spanish variants

The lack of understanding of figurative language online, such as ironic messages, is a common cause of error for systems that analyze automatically the users’ opinions online detecting sentiment, emotions or stance. In order to deal with this problem of automatic processing of natural language, IroSvA shared task at IberLef 2019 asks participants to detect, for the first time, irony in short texts written in Spanish language, considering the three linguistic variants from Spain, Mexico and Cuba. Another novelty of this task is the presence of labels specifying the context of the utterance, such as current political or social issues discussed online. In the context of this shared task, we approached irony detection in Spanish short texts trying to exploit the provided topic information. In addition, we investigated the usefulness of stylistic, lexical and affective features during the development of the irony detection models for the three Spanish variants. Experimental results and further analyses allow to shed some light on the analogies and differences in the expression of irony in the three variants, and suggest new research directions, in a perspective of comparison with other languages.