Microblog hot topic detection based on topic model using term correlation matrix

In order to face the challenges of feature sparsity of short text messages for microblog hot topic detection, in this paper, we first explore the relation between terms, and then build term correlation matrix which is much denser than term-document matrix. Symmetric non-negative matrix factorization (SNMF) on term correlation matrix is applied to obtain term-topic matrix. Finally, we formulated the topic learning problem as probabilistic Latent semantic analysis (pLSA) on term-topic matrix. Besides, this paper also presents the definition of heat and mechanism of sorting the topics. Experiments show that our method can effectively cluster topics and be applied to microblog hot topic detection.