Enhance sentiment analysis on social networks with social influence analytics

Sentiment analysis on social networks has attracted increasing research attention. Most previous works rely on text mining and the phenomenon of Homophily reflected by explicit friendship relations, which are a weak assumption for modeling sentiment and opinion similarities. In this paper we show that competitive results can be achieved with consideration of implicit influence relationships. In particular, we use heterogeneous graphs to infer sentiment polarities at user-level. We show that information about social influence processes can be used to improve sentiment analysis. Our transductive learning results reveal that incorporating such information can indeed lead to statistically significant sentiment classification improvements.