MoodCast: Emotion prediction via dynamic continuous factor graph model

Human emotion is one important underlying force affecting and affected by the dynamics of social networks. An interesting question is "can we predict a person's mood based on his historic emotion log and his social network?". In this paper, we propose a MoodCast method based on a dynamic continuous factor graph model for modeling and predicting users' emotions in a social network. MoodCast incorporates users' dynamic status information (e.g., locations, activities, and attributes) and social influence from users' friends into a unified model. Based on the historical information (e.g., network structure and users' status from time 0 to t-1), MoodCast learns a discriminative model for predicting users' emotion status at time t. To the best of our knowledge, this work takes the first step in designing a principled model for emotion prediction in social networks. Our experimental results on both real social network and virtual web-based network show that we can accurately predict emotion status of more than 62% of users and 8+% improvement than the baseline methods.