Topic Analysis for Online Reviews with an Author-Experience-Object-Topic Model

被引：0
|
作者：
Zhang, Yong [1 ,2 ]
Ji, Dong-Hong [1 ]
Su, Ying [3 ]
Hu, Po [1 ,2 ]
机构：
[1] Wuhan Univ, Comp Sch, Wuhan 430072, Peoples R China
[2] Huazhong Normal Univ, Dept Comp Sci, Wuhan 430072, Peoples R China
[3] Huazhong Univ Sci & Technol, Wuchang Branch, Dept Comp Sci, Wuhan 430072, Peoples R China
来源：
INFORMATION RETRIEVAL TECHNOLOGY | 2011年 / 7097卷
关键词：
Latent Dirichlet Allocation; Author-Experience-Object-Topic Model; Social Review Network; Topic Model;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we propose a new probabilistic generative model for topic analysis of online reviews, called Author-Experience-Object-Topic Model (AEOT). This model is to capture the relationship between the authors, objects and reviews in order to improve the performance of topic analysis. The model, as a general one, can be transformed to six simpler models, and can produce topic-word, author-topic and object-topic distributions. Experimental results show that the model is suitable for topic analysis of online reviews, and outperforms other existing methods.
引用
收藏
页码：303 / +
页数：3
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