Popular Topic Detection in Chinese Micro-Blog Based on the Modified LDA Model

摘要：

　　Micro-blog has become a symbol of the novel social media, and because of its rapid development in such a short time,many research researchers are full of enthusiasm about it.We take use of Latent Dirichlet Allocation (LDA) Model which has excellent dimension reduction capability and can excavate latent semantic from texts to discover popular topics.We improve the original LDA model to FSC-LDA model by combining the text clustering methods and feature selection methods, which can identify the number of topics adaptively.FSC-LDA model can keep short micro-biog texts features better, and make the result more stable.The result of the experiments on real Chinese microblog text dataset shows that FSC-LDA model can perform well on the custom evaluation and find more accurate popular topics.

关键词： popular topics detection text clustering latent dirichlet allocation model FSC-LDA

作者: Yuzhong Chen Wanhua Li Wenzhong Guo Kun Guo

作者单位: College of Mathematics and Computer Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University FuZhou, China

会议类型: 国际会议

会议名称: The 12th Web Information System and Application Conference第十二届全国Web信息系统及其应用学术会议(WISA2015)、全国第十次语义Web 与本体论学术研讨会(SWON2015)、全国第九次电子政务技术及应用学术研讨会(EGTA2015)

会议地点: 济南

会议语种:英文

页码: 37-42

在线出版日期: 2015-09-11（万方平台首次上网日期，不代表论文的发表时间）