Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
NLTK: the natural language toolkit
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Towards Automated Reputation and Brand Monitoring on the Web
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Designing novel review ranking systems: predicting the usefulness and impact of reviews
Proceedings of the ninth international conference on Electronic commerce
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
User experience over time: an initial framework
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Discovering Association Rules on Experiences from Large-Scale Blog Entries
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Entity discovery and assignment for opinion mining applications
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering clues for review quality from author's behaviors on e-commerce sites
Proceedings of the 11th International Conference on Electronic Commerce
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
Detecting experiences from weblogs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Automatic organization of human task goals for web-scale problem solving knowledge
Proceedings of the seventh international conference on Knowledge capture
Predicting the helpfulness of online reviews using multilayer perceptron neural networks
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
As numerous on-line product reviews that vary in quality are published every day, much attention is being paid to quality assessment of such reviews. The current metric of using the number of votes by other customers such as 'helpful vote', despite its dominance, does not yield a fully effective outcome. In this article, we propose a novel metric to rank product reviews by 'mentions about experiences', accounting for customer's personal experiences, as a way of identifying high quality reviews. The proposed metric has two parameters that capture time expressions related to the use of products and product entities over different purchasing time periods by linguistic clues. The empirical results show that this metric is not only as helpful as the best existing metrics, 'helpful vote' or 'reviewer rank', but is also free from undesirable biases that either penalize recency or are driven solely by popularity. Our usability study also shows that ordering reviews by our metric is considered helpful on the accounts of both usefulness and satisfaction.