Sentiment analysis as a text categorization task: A study on feature and algorithm selection for Italian language

The availability on the Internet of huge amounts of blog posts, messages and comments allows to study the attitude of people on various topics. Sentiment Analysis, Opinion Mining and Emotion Analysis denote the area of research in Computer Science aimed at studying, analyzing and classifying text documents based on the underlying opinions expressed by their authors on various topics. While this is a tough task, because it is related to psychological aspects that are not always immediately evident in the lexical and syntactical aspects of the sentences, its importance may be paramount for several applications such as market analysis, political polls, etc. Fundamental pre-processing techniques for this task come from the area of Natural Language Processing, which may pose additional problems when the language of interest is different than English, and thus less (or less reliable) resources are available to extract the needed data from the text. This paper studies the performance of Sentiment Analysis, seen as a Text Categorization task, depending on the use of different classifiers and different features. While the approach is general, we focus on texts in Italian. The outcomes suggest which experimental settings can be most profitably used in this landscape, and show that significantly good results can be obtained.