Fine-grained Emotion Role Detection Based on Retweet Information

User behaviors in online social networks convey not only literal information but also one’s emotional attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this article, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model (MERM) to determine a user’s emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor, and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%.