Italian Irony Detection in Twitter: a First Approach
Authors: Francesco Barbieri, Francesco Ronzano, Horacio Saggion (Universitat Pompeu Fabra, 2014)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Irony is a linguistic device used to say something but meaning something else. The distinctive trait of ironic utterances is the opposition of literal and intended meaning. This characteristic makes the automatic recognition of irony a challenging task for current systems. In this paper we present and evaluate the first automated system targeted to detect irony in Italian Tweets, introducing and exploiting a set of linguistic features useful for this task.

Introduction
Sentiment Analysis is the interpretation of attitudes and opinions of subjects on certain topics. With the growth of social networks, Sentiment Analysis has become fundamental for customer reviews, opinion mining, and natural language user interfaces (Yasavur et al., 2014). During the last decade the number of investigations dealing with sentiment analysis has considerably increased, targeting most of the time English language. Comparatively and to the best of our knowledge there are only few works for the Italian language. In this paper we explore an important sentiment analysis problem: irony detection. Irony is a linguistic device used to say something when meaning something else (Quintilien and Butler, 1953). Dealing with figurative languages is one of the biggest challanges to correctly determine the polarity of a text: analysing phrases where literal and indented meaning are not the same, is hard for humans, hence even harder for machines. Moreover, systems able of detect irony can benefit also other A.I. areas like Human Computer Interaction. Approaches to detect irony have been already proposed for English, Portuguese and Dutch texts (see Section 2). Some of these systems used words, or word-patterns as irony detection features (Davidov et al., 2010; Gonz´ alez-Ib´ a˜ nez et al., 2011; Reyes et al., 2013; Buschmeier et al., 2014). Other approaches, like Barbieri and Saggion (2014a), exploited lexical and semantic features of single words like their frequency in reference corpora or the number of associated synsets. Relying on the latter method, in this paper we present the first system for automatic detection of irony in Italian Tweets. In particular, we investigate the effectiveness of Decision Trees in classifying Tweets as ironic or not ironic, showing that classification performances increase by considering lexical and semantic features of single words instead of pure bag-of-words (BOW) approaches. To train our system, we exploited as ironic examples the Tweets from the account of a famous collective blog named Spinoza and as not ironic examples the Tweets retrieved from the timelines of seven popular Italian newspapers.

Conclusion
In this study we evaluate a novel system to detect irony in Italian, focusing on Tweets. We tackle this problem as binary classification, where the ironic examples are posts of the Twitter account Spinoza and the non-ironic examples are Tweets from seven popular Italian newspapers. We evaluated the effectiveness of Decision Trees with different feature sets to carry out this classification task. Our system only focuses on characteristics on lexical and semantic information that characterises each word, rather than the words themselves as features. The performance of the system is good if compared to our baseline (BOW) considering only word occurrences as features, since we obtain an F1 improvement of 0.11. This result shows the suitability of our approach to detect ironic Italian Tweets. However, there is space to enrich and tune the model as this is only a first approach. It is possible to both improve the model with new features (for example related to punctuation or language models) and evaluate the system on new and extended corpora of Italian Tweets as they become available. Another issue we faced is the lack of accurate evaluations of features performance considering distinct classifiers / algorithms for irony detection.