Predicting User-Topic Opinions in Twitter with Social and Topical Context

With popular microblogging services like Twitter, users are able to online share their real-time feelings in a more convenient way. The user generated data in Twitter is thus regarded as a resource providing individuals' spontaneous emotional information, and has attracted much attention of researchers. Prior work has measured the emotional expressions in users' tweets and then performed various analysis and learning. However, how to utilize those learned knowledge from the observed tweets and the context information to predict users' opinions toward specific topics they had not directly given yet, is a novel problem presenting both challenges and opportunities. In this paper, we mainly focus on solving this problem with a Social context and Topical context incorporated Matrix Factorization (ScTcMF) framework. The experimental results on a real-world Twitter data set show that this framework outperforms the state-of-the-art collaborative filtering methods, and demonstrate that both social context and topical context are effective in improving the user-topic opinion prediction performance.