收稿日期: 2013-11-13
修回日期: 2014-01-04
网络出版日期: 2014-01-20
基金资助
本文系教育部人文社会科学青年基金项目“社会网络环境下信息内容主题挖掘与语义分类研究”（项目编号：13YJC870008）和国家自然科学青年基金项目“社会网络环境下基于用户-资源关联的信息推荐研究（项目编号：71303178）”研究成果之一。
Mining and Evolution of Content Topics Based on Dynamic LDA
Received date: 2013-11-13
Revised date: 2014-01-04
Online published: 2014-01-20
胡吉明 , 陈果 . 基于动态LDA主题模型的内容主题挖掘与演化[J]. 图书情报工作, 2014 , 58(02) : 138 -142 . DOI: 10.13266/j.issn.0252-3116.2014.02.023
The study of mining and evolution of text topics is of important significance for text modeling and classification, as well as the recommendation service. Starting from the analysis of theory of text topic modeling based on LDA, aiming at dynamic characters of text contents under social networking environment, this article constructed a dynamic LDA model for mining of text topics. Subsequently, the accuracy degree of topic mining was improved by incremental Gibbs sampling and estimation. Furthermore, the evolution of dynamic topics of text contents was achieved from the aspects of topic similarity and intensity. The experiment demonstrated that methods proposed in this article were feasible and effective, which will be the foundation of further study about semantic modeling and classification text.
Key words： topics mining; topics evolution; dynamic LDA model
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