Collective Classification for Sentiment Analysis in Social Networks

The emergence of online social networks has generated an enormous amount of data containing users' opinions about the most varied subjects. Aiming to identify opinion orientation, Sentiment Analysis techniques have been proposed, mainly based on text classification methods. We propose a different perspective to treat this problem, based on a user centric approach. We adopt a graph representation in which nodes represent users and connections represent relationships in a social network. Then, we apply collective classification techniques which use link information to infer opinions of users who have not posted their opinion about the subject under analysis. Preliminary experiments on a Twitter corpus of political preferences have shown promising results.