Hernández Farías, I.; Benedí Ruiz, JM.; Rosso, P. (2015). 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.

People use social networks for different communication purposes, for example to share their opinion on ongoing events. One way to exploit this common knowledge is by using Sentiment Analysis and Natural Language Processing in order to extract useful information. In this paper we present a SA approach applied to a set of tweets related to a recent natural disaster in Italy; our goal is to identify tweets that may provide useful information from a disaster management perspective.