Modeling Emotion Influence Using Attention-based Graph Convolutional Recurrent Network

User emotion modeling is a vital problem of social media analysis. In previous studies, content and topology information of social networks have been considered in emotion modeling tasks, but the inflence of current emotion states of other users was not considered. We define emotion influence as the emotional impact from user’s friends in social networks, which is determined by both network structure and node attributes (the features of friends). In this paper, we try to model the emotion influence to help analyze user’s emotion. The key challenges to this problem are: 1) how to combine content features and network structures together to model emotion influence; 2) how to selectively focus on the major social network information related to emotion influence. To tackle these challenges, we propose an attention-based graph convolutional recurrent network to bring in emotion influence and content data. Firstly, we use an attention-based graph convolutional network to selectively aggregate the features of the user’s friends with specific attention. Then an LSTM model is used to learn user’s own content features and emotion influence. The model we proposed is more capable of quantifying the emotion influence in social networks as well as combining them together to analyze the user emotion status. We conduct emotion classification experiments to evaluate the effectiveness of our model on a real world dataset called Sina Weibo1. Results show that our model outperforms several state-of-the-art methods.