Abstract：Microblog is a novel individual publication model over Internet, making significantly more information open and interactive. Utilizing topic detection techniques to classify and organize microblog texts by topics can enable users access to the information interested to them under the dynamic environment. To deal with the short, semi-structured, context dependent microblog texts, we propose a thread-based two-stage clustering method. In the first phase, the temporal-author-topic (TAT) model is applied to clean the thread, namely to filter out the noisy microblog texts. In the second phrase, microblog texts with each thread are merged to form the thread texts for global topic detection. Experimental results show the approach achieves a good performance with a F-measure of 31.2%. Key wordsmicroblog texts; topic detection; TAT model; thread information; LDA feature selection
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2012, Vol. 26 
