Automated classification of book blurbs according to the Emotional Tags of the social network Zazie

Sentiment Analysis and Opinion Mining are receiving increasing attention in many sectors because knowing and predicting opinions of people is considered a strategic added value. In the last years an increasing attention has also been devoted to Emotion Recognition, often by developing automated systems that can associate user’s emotions to texts, music or artworks. Zazie is an Italian social network for readers that introduces a new dimension on book characterization, the emotional icon tagging. Each book, besides user’s comments and reviews, can be tagged with special icons, the moods, that are emotional tags chosen by the users. The aim of this work is to study the feasibility of an automated classification of books in Zazie according to the emotional tags, by means of the lexical analysis of book blurbs. A supervised learning approach is used to determine if a correlation between the characteristics of a book blurb and the emotional icons associated to the book by the users exists.