Microblog Topic Mining Based on FR-DATM

查看全文 作　　者：LIU [1]Bingyu;WANG [1]Cuirong;WANG [2]Yiran;ZHANG [1]Kun;WANG [1]Cong

机构地区：[1]School of information science and engineering, Northeastern University, Shenyang 110819, China;[2]School of management, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China

出　　处：《Chinese Journal of Electronics》索引2018年第27卷第2期,共8页

基　　金：supported by National Natural Science Foundation of China(No.61300195);Natural Science Foundation of Hebei Province(No.F2014501078);Technology Planning Project of Hebei Province(No.15210146);the General Project of Liaoning Province Department of Education Science Research(No.L2013099) 摘　　要：Microblog has become a ma jor platform for people to release or obtain information. Texts on Microblog are shorter and have scarce co-occurrence information of terms. It is more complicated to discover topics from Microblog. To solve the problems, this paper proposes a dynamic author topic model FR-DATM and uses Gibbs sampling implementation for inference of this model.The FR-DATM model analyzes the relationships between blogs, and connects the related blogs to solve the sparseness of data. It allows blogs to be related to multiple topics,and each author of the blogs is also related to the topics of the blogs. The FR-DATM can also mine the topic evolution of the blogs and the authors. Experiments on Twitter dataset show that FR-DATM outperforms Latent dirichlet allocation(LDA) model and Microblog latent Dirichlet Allocation(MB-LDA) from three different perspectives:The quality of generated latent topics, the model perplexity and FR-DATM can mine the topic that the author are concerned dynamically. 关键词：DIRICHLET DIRICHLET 采矿模型作者吉布斯数据集信息

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Microblog Topic Mining Based on FR-DATM

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