Micro-Blog Topic Detection Method Based on BTM Topic Model and K-Means Clustering Algorithm

The development of micro-blog, generating large-scale short texts, provides people with convenient communication. In the meantime, discovering topics from short texts genuinely becomes an intractable problem. It was hard for traditional topic model-to-model short texts, such as probabilistic latent semantic analysis (PLSA) and Latent Dirichlet Allocation (LDA). They suffered from the severe data sparsity when disposed short texts. Moreover, K-means clustering algorithm can make topics discriminative when datasets is intensive and the difference among topic documents is distinct. In this paper, BTM topic model is employed to process short texts - micro-blog data for alleviating the problem of sparsity. At the same time, we integrating K-means clustering algorithm into BTM (Biterm Topic Model) for topics discovery further. The results of experiments on Sina micro-blog short text collections demonstrate that our method can discover topics effectively.