User-Level Sentiment Analysis Incorporating Social Networks

Chenhao Tan, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, Ping Li
In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'2011) (poster, aggregate oral+poster acceptance rate: 17.5%)

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 sentimentclassification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.

[Poster] [PDF] [Slides]

     author = {Chenhao Tan and Lillian Lee and Jie Tang and Long Jiang and Ming Zhou and Ping Li},
     title = {User-Level Sentiment Analysis Incorporating Social Networks},
     year = {2011},
     booktitle = {Proceedings of KDD}