关键词: 热点话题发现, 微博, 突发词对主题模型（BBTM）, 主题模型

Abstract: In order to overcome the problems of current hot topic discovery methods based on topic model, such as the sparsity of features, the high dimension, and the requirement for pre-specifying the number of topics, a hot topic discovery method based on an improved bursty biterm topic model (BBTM) which is called hot topic-hot biterm topic model (H-HBTM) is proposed. First, the word burst probability is used to select features and to filter the non-burst words. Second, the hot burst probability of micro-blog word pairs can be expressed by integrating the burst characteristic and the propagation characteristic of micro-blog texts. The hot burst probability is used as the prior probability of the BBTM model. Finally, a density based method is used to select the optimal number of topics for the BBTM model so that the optimal BBTM model is determined to detect hot topics. The experiments conducted on the real micro-blog datasets demonstrate that the H-HBTM can automatically find the optimal model without pre-specifying the number of topics, and the quality of the hot topics found is superior to the other methods, such as the BBTM, the biterm topic model and the latent Dirichlet allocation.

Key words: hot topic detection, microblog, bursty biterm topic model (BBTM), topic model