Research on Multi-Label Propagation Clustering Method for Microblog Hot Topic Detection
CHEN Yu-Zhong, FANG Ming-Yue, GUO Wen-Zhong
Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350108 College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108
Abstract：With the rapid growth of microblog data, extracting hot topics from vast amounts of microblog posts has become a research hotspot. The traditional methods for hot term extraction can hardly apply to microblog data, thus a life value calculation model based on aging theory is established to extract hot terms. Then, a hot term co-occurrence network is built based on the correlations between hot terms. Aiming at the problem that traditional clustering methods can hardly handle the hot term overlap between different topics and can not deal with vast amounts of data efficiently, a term clustering method based on multi-label propagation algorithm (TCMLPA), which has a nearly linear time complexity, is proposed to detect hot topics in hot term co-occurrence network.The experimental results show that life value calculation model can filter noise and extract hot terms effectively. Meanwhile, TCMLPA ensures the stability of clustering result and improves the accuracy and efficiency of hot topic detection.
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2015, Vol. 28 
