Mining Hot Topics from Free-text Customer Reviews-An LDA-based Approach

摘要：

This study examines how the Latent Di rich let Allocation (LDA) model combined with natural language processing techniques can be used to identify hot topics from free-text customer reviews. To verify the validity of the proposed approach, 21 580 restaurant reviews are collected. Each review is viewed as a probabilistic mixture of latent topics and each topic is treated as a probability distribution over words in a vocabulary. Parameters are estimated with Gibbs sampling, and the hot topics with top words are acquired. The experiments show that this approach could produce satisfactory results.

关键词： Latent Dirichlet Allocatio Hot Topic Detection User Reviews Gibbs Sampling

作者: Chuanming Yu Xiaoqing Zhang Huiting Luo

作者单位: School of Information and Security Engineering, Zhongnan University of Economics and Law, Wuhan, Chi Business School, University of Shanghai for Science and Technology, Shanghai, China

会议类型: 国际会议

会议名称: 2010 Seventh Web Information System and Applications Conference(第七届全国web信息系统及其应用学术会议)

会议地点: 呼和浩特

会议语种:英文

页码: 85-89

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）