Abstract
People use social media to express their opinions. Often linguistic devices such as irony are used. From the sentiment analysis perspective such utterances represent a challenge being a polarity reversor (usually from positive to negative). This paper presents an approach to address irony detection from a machine learning perspective. Our model considers structural features as well as, for the first time, sentiment analysis features such as the overall sentiment of a tweet and a score of its polarity. The approach has been evaluated over a set classifiers such as: Naïve Bayes, Decision Tree, Maximum Entropy, Support Vector Machine, and for the first time in irony detection task: Multilayer Perceptron. The results obtained showed the ability of our model to distinguish between potentially ironic and non-ironic sentences.
