Annotating Irony in a Novel Italian Corpus for Sentiment Analysis

In this paper we describe our current work on Senti–TUT, a novel Italian corpus for sentiment analysis. This resource includes annotations concerning both sentiment and morpho-syntax, in order to make available several possibilities of further exploitation related to sentiment analysis. For what concerns the annotation at sentiment level, we focus on irony and we selected therefore texts on politics from a social media, namely Twitter, where irony is usually applied by humans. Our aim is to add a new sentiment dimension, which explicitly accounts for irony, to a sentiment analysis classification framework based on polarity annotation. The paper describes the data set, the features of the annotation both at sentiment and morpho-syntactic level, the procedures and tools applied in the annotation process. Finally, it shows the preliminary experiments we are carrying on in order to validate the annotation work.