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Hot topic detection in news blogs from the perspective of W2T

By Erzhong Zhou, Ning Zhong, Y. Li and Jiajin Huang

Abstract

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective

Topics: 080109 Pattern Recognition and Data Mining, topic detection, opinion mining
Publisher: Springer
Year: 2012
DOI identifier: 10.1007/978-3-642-35236-2_3
OAI identifier: oai:eprints.qut.edu.au:58393

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