[Objective] This paper aims to identify the topics of online public opinion. [Methods] We constructed a model to extract public opinion based on the information content of the Weibo posts, the relationship among the users, and user behaviors. [Results] We built a public opinion network, extracted and clustered relevant topics, constructed a two-mode network of “user-topic” and evolution of the opinion topics. The proposed method could identify topics of online public opinion effectively. [Limitations] The influence of users’ attributes on topic identification needed to be investigated. [Conclusions] We could identify the topics of online public opinion based on the social network analysis with the help of LDA model.
Wu W, Zhang B, Ostendorf M.Automatic Generation of Personalized Annotation Tags for Twitter Users[C]//// Proceedings of the 2010 Annual Conference of the North American Chapter of Association for Computational Linguistics, Los Angeles, California, USA. Association for Computational Linguistics, 2010: 689-692.
Narang K, Nagar S, Mehta S, et al.Discovery and Analysis of Evolving Topical Social Discussions on Unstructured Microblogs[A]// Advances in Information Retrieval[M]. Berlin, Heidelberg: Springer, 2013: 545-556.
Kim H G, Lee S, Kyeong S.Discovering Hot Topics Using Twitter Streaming Data Social Topic Detection and Geographic Clustering[C]////Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis & Mining, Niagara, Ontario, Canada. New York, USA: ACM, 2013: 1215-1220.
Nguyen D T, Jung J E.Privacy-preserving Discovery of Topic-based Events from Social Sensor Signals: An Experimental Study on Twitter[J]. Scientific World Journal, 2014, 67(3): 435-444.
Guo J, Zhang P, Tan J L, et al.Mining Hot Topics from Twitter Streams[J]. Procedia Computer Science, 2012, 9(11): 2008-2011.
(Xia Mengnan, Du Yongping, Zuo Benxin.Micro-blog Opinion Analysis Based on Syntactic Dependency and Feature Combination[J]. Journal of Shandong University: Natural Science, 2014, 49(11): 22-30.)
Deng J, Deng K, Li Y, et al.Hot Topic Detection Based on Complex Networks[C]////Proceedings of the 10th International Conference on Fuzzy Systems and Knowledge Discovery. 2013.
Yin Z, Cao L, Gu Q, et al.Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling[J]. ACM Transactions on Intelligent Systems and Technology, 2012, 3(4): 67-83.
(Zhou Jie, Lin Chen, Li Bicheng.Research on the Identification of Opinion Topic Expressed in Web Comments[J]. Journal of the China Society for Scientific and Technical Information, 2010, 29(5): 858-863.)