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论文作者：	Weijiang Li, Zhengtao Yu
论文名称：	Micro-Blog Topic Detection Method Based on BTM Topic Model and K-Means Clustering Algorithm
论文发表刊物：	Automatic Control and Computer Sciences
论文发表时间：	2016
卷号页码：	2016,50:271-277.
论文描述：
收录情况：	EI Indexed EI Accession number:EI:20163802830439
论文摘要：
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.
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