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论文类型：期刊论文

第一作者：Yang L.

通讯作者：Yang, L.; Faculty of Electronic Information and Electrical Engineering, Dalian University of TechnologyChina

合写作者：Lin H.,Lin Y.,Shi C.

发表时间：2015-07-15

发表刊物：Journal of Computational Information Systems

收录刊物：EI、Scopus

卷号：11

期号：14

页面范围：5031-5038

ISSN号：15539105

摘要：Due to the real-time response of microblog, individuals like to use microblog sharing the topics, which happened around them, especially in news headlines. As the rapid growth of users and topic numbers, tracking the progress of topics has become a must. However, topic drift and an ocean of noise are common seen in microblog stream. In order to address the problems of topic tracking, we propose an algorithm LMT based on language model and inference network, and adopt the microblog entropy to weigh the importance of each microblog. 12 million microblogs posted by more than 170 thousand users are collected as out experiment dataset, and the experiment results show that our algorithm is more efficient and less noisy compared with traditional Dynamic Topic Model. ?, 2015, Journal of Computational Information Systems. All right reserved.