Abstract
Topic models are important tools for mining the potential topics of text. However, the existing topic model is mostly derived from latent Dirichlet allocation (LDA), which requires the number of topics to be specified in advance. In order to mine the topic of Chines micro-blogs automatically, we propose a nonparametric Bayesian model, named HDP-TUB model, which is derived from hierarchical Dirichlet Process (HDP). In this model, we assume non-exchangeability of data, and use temporal information, user information and theme tags (TUB) to solve the sparsity problem caused by the short text. In order to construct the HDP-TUB model, the CRF (Chinese Restaurant Franchise) method is extended to integrate the temporal information, user information and topic tag information. Experiments show that the HDP-TUB model outperforms the LDA model and the HDP model in the perplexity and the difference between topics.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
