Exploiting Consumer Reviews for Product Feature Ranking

Web 2.0 technology leads Web users to publish a large number of consumer reviews about products and services on various websites. Major product features extracted from consumer reviews may let product providers find what features are mostly cared by consumers, and also may help potential consumers t...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 27; no. 3; pp. 635 - 649
Main Author 李素科 关志 唐礼勇 陈钟
Format Journal Article
LanguageEnglish
Published Boston Springer US 2012
Springer Nature B.V
School of Software and Microelectronics,Peking University,Beijing 100871,China%School of Electronics Engineering and Computer Science,Peking University,Beijing 100871,China
Subjects
Algorithms
Alliances
Artificial Intelligence
Computer Science
Connectivity
Consumer surveys
Consumers
Data Structures and Information Theory
Decisions
Decomposition
Empirical analysis
Experiments
Information Systems Applications (incl.Internet)
Product reviews
Product specifications
Purchasing
Ranking
Ratings
Ratings & rankings
Regression
Regression analysis
Regular Paper
Software Engineering
Theory of Computation
Web 2.0
产品功能
应用程序
消费者
程序分解
线性回归
网络用户
自动生成
评论
opinion mining
product review
product feature ranking
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Summary:Web 2.0 technology leads Web users to publish a large number of consumer reviews about products and services on various websites. Major product features extracted from consumer reviews may let product providers find what features are mostly cared by consumers, and also may help potential consumers to make purchasing decisions. In this work, we propose a linear regression with rules-based approach to ranking product features according to their importance. Empirical experiments show our approach is effective and promising. We also demonstrate two applications using our proposed approach. The first application decomposes overall ratings of products into product feature ratings. And the second application seeks to generate consumer surveys automatically.
Bibliography:product feature ranking, product review, opinion mining
11-2296/TP
Web 2.0 technology leads Web users to publish a large number of consumer reviews about products and services on various websites. Major product features extracted from consumer reviews may let product providers find what features are mostly cared by consumers, and also may help potential consumers to make purchasing decisions. In this work, we propose a linear regression with rules-based approach to ranking product features according to their importance. Empirical experiments show our approach is effective and promising. We also demonstrate two applications using our proposed approach. The first application decomposes overall ratings of products into product feature ratings. And the second application seeks to generate consumer surveys automatically.
Su-Ke LiI , Zhi Guan, Li-Yong Tang, and Zhong Chen, Member, CCF, IEEE（1School of Software and Microelectronics, Peking University, Beijing 100871, China 2School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China）
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-012-1250-z