User-level sentiment analysis incorporating social networks

TL;DR

This paper demonstrates that incorporating social network information from Twitter, such as follower relationships and mentions, enhances user-level sentiment analysis beyond traditional text-only methods.

Contribution

It introduces semi-supervised models that leverage social network data for sentiment classification, showing significant improvements over text-only approaches.

Findings

Social network data improves sentiment classification accuracy.

Models using follower/followee and mention networks outperform baseline methods.

Incorporating social relationships yields statistically significant results.

Abstract

We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment-classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.