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| Chinese Short Text Topic Analysis by Latent Dirichlet Allocation Model with Co-word Network Analysis |
| Cai Yongming1, Chang Qing2 |
1. Business School, University of Jinan, Jinan 250002;
2. School of Economics and Management, Inner Mongolia University of Technology, Huhhot 010051 |
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Abstract Given the sparse feature of the short text, the results of the traditional LDA or PLSA topic model is not suitable for analyzing short texts. Based on the traditional LDA model, a Latent Dirichlet Allocation Model with Co-word Network Analysis (CA-LDA) model is proposed considering the words co-occurrence network. According to the automorphic equivalence principle, the latent space model is used to reduce the dimension with minimum information loss. Eigenvector Centrality is used to revise the LDA model to raise the weights of important words by recursive accumulation. During the Gibbs Sampling, the latent position cluster model for social networks is used to raise the probability that the words with similar lexical collocation are divided into the same topic. Experimental results show the excellent performance of the model.
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Received: 24 May 2017
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