Identification of Microblog Opinion Leader Based on User Feature and Interaction Network

Opinion leaders are core users in online communities, who can guide the direction of the public opinion. With the rapid development of microblog, identification of the microblog opinion leaders has become a significant task. In this paper, we propose a hybrid data mining approach based on user feature and interaction network, which includes three parts: a way to analyze users´ authority, activity and influence, a way to consider the orientation of sentiment in interaction network and a combined method based on HITS algorithm for identifying micro blog opinion leaders. Comparative results show that this mechanism can provide an effective mining of the user feature and a better rate of recognition.