Emotions are playing significant roles in daily life, making emotion prediction important. To date, most of state-of-theart methods make emotion prediction for the masses which are invalid for individuals. In this paper, we propose a novel emotion prediction method for individuals based on user interest and social influence. To balance user interest and social influence, we further propose a simple yet efficient weight learning method in which the weights are obtained from users' behaviors. We perform experiments in real social media network, with 4,257 users and 2,152,037 microblogs. The experimental results demonstrate that our method outperforms traditional methods with significant performance gains.
REF combines the textual, visual and the social information of users and propose a user-level emotion prediction method.
User interest and social influence based emotion prediction for individuals
MM '13
Computer Science