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.