关键词: 词共现矩阵, 对称非负矩阵分解, 概率潜在语义分析, 微博热点话题发现

Abstract: In order to face the challenges of feature sparsely of short text messages for microblog hot topic detection, this paper proposes a hot topic detection method based on the combination of term mutual information and probabilistic topic model. Symmetric Nonnegative Matrix Factorization（sNMF） is performed on word co-occurrence with word mutual information and the matrix of term-topic matrix is thereafter inferred. Probabilistic Latent Semantic Analysis（pLSA） model is then adopted to model the topic-microblog. The hotness of topic is analyzed and sorted. Experiments show that this method can effectively cluster and detect the hot topics.

Key words: term co-occurrence matrix, symmetrical nonnegative matrix factorization, probabilistic latent semantic analysis, micro-blog hot topic detection