HE Min1; LIU Wei1;2; LIU Yue1; WANG Lihong2; BAI Shuo1; CHENG Xueqi1
1. CAS Key Laboratory of Newtwork Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2. National Computer network Emergency Response technical Team/Coordination Center of China, Beijing 100029, China
Abstract：With the microblog properties of sparse data and difficult decision on relation of content, a feature-driven microblog topic detection method is proposed. The meaningful strings are extracted as dynamic microblog features. The author-influence and document-influence of features are defined according to the structure relation of microblogs, which form the attribute sets together with the statistics on content. The logic regression model is used to classify features into key features and noise features. The nearest neighbor clustering method is modified to derive the topics from clustering the key feartures, in which the mutual information of key features is applied as the distance measure. The microblog data experiment shows that the accuracy and recall are remarkably improved by the proposed method.
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2017, Vol. 31 
