Cited By
View all- Li SGuan ZTang LChen Z(2012)Exploiting Consumer Reviews for Product Feature RankingJournal of Computer Science and Technology10.1007/s11390-012-1250-z27:3(635-649)Online publication date: 19-May-2012
Exploiting web reviews for generating customer service surveys
Authors: 
Suke Li, Zhong ChenAuthors Info & Claims
Suke Li, Zhong ChenAuthors Info & ClaimsAbstract
Traditional customer satisfaction analysis relies on the work of designing, distributing, collecting and analyzing surveys. Surveys that are designed by humans may be subjective, and it is hard to know what service aspects are the most important for customers. To address this issue, this paper proposes a method of automatically generating service surveys through mining Web reviews. Candidate service aspects are extracted using simple extraction rules. Then we rank candidate service aspects in terms of their weights generated by combining co-occurrence method and linear regression method together. Experimental results demonstrate the effectiveness of the proposed method.
References
[1]
R. Agrawal, T. Imieliński, and A. Swami. Mining association rules between sets of items in large databases. In SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pages 207--216, 1993.
[2]
M. Gamon, A. Aue, S. Corston-Oliver, and E. K. Ringger. Pulse: Mining customer opinions from free text. In IDA, volume 3646 of Lecture Notes in Computer Science, pages 121--132, 2005.
[3]
S. Godbole and S. Roy. Text classification, business intelligence, and interactivity: automating c-sat analysis for services industry. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 911--919, 2008.
[4]
H. Guo, H. Zhu, Z. Guo, X. Zhang, and Z. Su. Product feature categorization with multilevel latent semantic association. In CIKM '09: Proceeding of the 18th ACM conference on Information and knowledge management, pages 1087--1096, 2009.
[5]
J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pages 1--12, 2000.
[6]
M. Hu and B. Liu. Mining and summarizing customer reviews. In KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168--177, 2004.
[7]
R. Jurca, F. Garcin, A. Talwar, and B. Faltings. Reporting incentives and biases in online review forums. ACM Trans. Web, 4(2):1--27, 2010.
[8]
H. Landrum, V. Prybutok, X. Zhang, and D. Peak. Measuring is system service quality with servqual: Usersaŕ, a perceptions of relative importance of the five servperf dimensions. Informing Science: the International Journal of an Emerging Transdiscipline, 12:17--36, 1985.
[9]
B. Liu, M. Hu, and J. Cheng. Opinion observer: analyzing and comparing opinions on the web. In WWW '05: Proceedings of the 14th international conference on World Wide Web, pages 342--351, 2005.
[10]
Y. Lu, C. Zhai, and N. Sundaresan. Rated aspect summarization of short comments. In WWW '09: Proceedings of the 18th international conference on World wide web, pages 131--140, 2009.
[11]
S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fukushima. Mining product reputations on the web. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 341--349, 2002.
[12]
A. Parasuraman, V. Zeithaml, and L. Berry. Servqual: A multiple-item scale for measuring consumer perceptions. Journal of Retailing, 64, 1988.
[13]
I. Titov and R. McDonald. Modeling online reviews with multi-grain topic models. In WWW '08: Proceeding of the 17th international conference on World Wide Web, pages 111--120, 2008.
[14]
J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In ICDM '03: Proceedings of the Third IEEE International Conference on Data Mining, pages 427--434, 2003.
Index Terms
- Exploiting web reviews for generating customer service surveys
Recommendations
Extracting service aspects from web reviews
WISM'10: Proceedings of the 2010 international conference on Web information systems and miningWeb users have published huge amounts of opinions about services in blogs, Web forums and other review friendly social websites. Consumers form their judgements to service quality according to a variety of service aspects which may be mentioned in ...
Generating tags for service reviews
ADMA'10: Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part IIThis paper proposes an approach to generating tags for service reviews. We extract candidate service aspects from reviews, score candidate opinion words and weight extracted candidate service aspects. Tags are automatically generated for reviews by ...
A Review of the Automatic Web Service Composition Surveys
ICSC '14: Proceedings of the 2014 IEEE International Conference on Semantic ComputingIn recent years, developing needed software applications via the technique Web Service Composition (WSC) has been more and more popular. Moreover, benefit from the Semantic Web Services (SWSs) technology, it is possible to even automatically conduct WSC,...
Comments
Information & Contributors
Information
Published In
October 2010
136 pages
ISBN:9781450303866
DOI:10.1145/1871985
Copyright © 2010 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 October 2010
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
CIKM '10: International Conference on Information and Knowledge Management
October 30, 2010
ON, Toronto, Canada
Acceptance Rates
SMUC '10 Paper Acceptance Rate 15 of 25 submissions, 60%;
Overall Acceptance Rate 15 of 25 submissions, 60%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 244Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
Cited By
View all- Li SGuan ZTang LChen Z(2012)Exploiting Consumer Reviews for Product Feature RankingJournal of Computer Science and Technology10.1007/s11390-012-1250-z27:3(635-649)Online publication date: 19-May-2012
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in