Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
Product feature categorization with multilevel latent semantic association
Proceedings of the 18th ACM conference on Information and knowledge management
Reporting incentives and biases in online review forums
ACM Transactions on the Web (TWEB)
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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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.