Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Utility scoring of product reviews
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
ARSA: a sentiment-aware model for predicting sales performance using blogs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Reputation systems for open collaboration
Communications of the ACM
Multi-facets quality assessment of online opinionated expressions
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
| Hi-index | 0.02 |
With the flourish of the Internet, online review mining has attracted a lot of attention from the research community. However, compared to various well-studied sentiment analysis and opinion summarization problems, less effort has been made to analyze the quality of online reviews. The objective of this paper is to fill in this gap by automatically evaluating the "helpfulness" of reviews and consequently developing novel models to identify the most helpful reviews for a particular product. In particular, based on a thorough analysis of various factors that may affect the review quality, we propose HelpMeter, a nonlinear regression model for helpfulness prediction. Some preliminary experiments were conducted on a movie review data set, and the performance results confirm the superiority of the proposed method.