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NON-PARAMETRIC TOPIC MODEL FOR DISCOVERING GEOGRAPHICAL TOPIC VARIATIONS

NON-PARAMETRIC TOPIC MODEL FOR DISCOVERING GEOGRAPHICAL TOPIC VARIATIONS

作     者:Qi Xiang Huang Yu Song Jun Huang Tinglei Wang Hongqi Fu Kun 

作者机构:Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences 

出 版 物:《Journal of Electronics(China)》 (电子科学学刊(英文版))

年 卷 期:2014年第31卷第6期

页      面:576-586页

学科分类:081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

基  金:Supported by National High Technology Research and Development Program of China(No.2012AA011005) 

主  题:Text mining Topic model Geographical topics Bayesian non-parameter 

摘      要:This paper presents a non-parametric topic model that captures not only the latent topics in text collections, but also how the topics change over space. Unlike other recent work that relies on either Gaussian assumptions or discretization of locations, here topics are associated with a distance dependent Chinese Restaurant Process(ddC RP), and for each document, the observed words are influenced by the document s GPS-tag. Our model allows both unbound number and flexible distribution of the geographical variations of the topics content. We develop a Gibbs sampler for the proposal, and compare it with existing models on a real data set basis.

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