Abstract
Micro-blog has become a symbol of the novel social media, and because of its rapid development in such a short time, many research researchers are full of enthusiasm about it. We take use of Latent Dirichlet Allocation (LDA) Model which has excellent dimension reduction capability and can excavate latent semantic from texts to discover popular topics. We improve the original LDA model to FSC-LDA model by combining the text clustering methods and feature selection methods, which can identify the number of topics adaptively. FSC-LDA model can keep short micro-blog texts features better, and make the result more stable. The result of the experiments on real Chinese microblog text dataset shows that FSC-LDA model can perform well on the custom evaluation and find more accurate popular topics.
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