关键词: 意见领袖, 主题社团, 社交网络, 情感特征, 支持率

Abstract:

The opinion leader’s influence on public opinion is different under different theme communities. In order to find out the opinion leader quickly and accurately in the social network, an opinion leader mining method for the theme community is proposed. First of all, according to the proposed Interest Latent Dirichlet Allocation（I-LDA） subject model, the theme distribution with stronger ability of topic expression is obtained, and the topic similarity of the adjacent users is calculated on this basis. Then, the theme communities are divided by a multi-label equilibrium association based on the similarity of the topic, so that a user of the similar degree is divided into the same subject community, thereby further improving the accuracy and the rationality of the division of communities. With regard to the mining of the opinion leader, it proposes a Quickly-Mining Opinion Leader Algorithm（QMOLA）, which is to filter out the candidate of the opinion leader in the theme community through the structural features, and then to tap the opinion leader in the theme community by combining the characteristics of the communication and the emotional characteristics. The experimental results show that QMOLA has an obvious advantage in the mining efficiency with respect to the traditional opinion leader’s mining method, and the excavated opinion leader has higher coverage and approval rating.

Key words: opinion leader, theme community, social network, emotional characteristics, approval rating