关键词: 微博, 主题挖掘, AT模型, 吉布斯采样

Abstract: As micro-blog grows more popular and widely used, micro-blogging site such as Sina has become a huge source of information, although the traditional method of texts, topic mining has been extensively applied research. For this special kind of text of micro-blogging, traditional text mining algorithm can not be well studied. In order to compensate the deficiencies of current topic mining for micro-blogging platform and considering the sparsity and multidimensional characteristics of micro-blogging, this paper proposes targeted pretreatment method and combines the users’ micro-blogging data with AT model, then mining the micro-blog topics by gibbs sampling, getting users’ interest through extracting the topics of authors. Through the experiments on a real data sets, as well as comparison with LDA models prove that the model can get micro-blog topics effectively.

Key words: micro-blog, topic mining, author-topic model, Gibbs sampling