A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the 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
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Do online reviews affect product sales? The role of reviewer characteristics and temporal effects
Information Technology and Management
International Journal of Electronic Commerce
Automatically assessing the post quality in online discussions on software
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
ACM Transactions on Management Information Systems (TMIS)
Mining millions of reviews: a technique to rank products based on importance of reviews
Proceedings of the 13th International Conference on Electronic Commerce
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E-commerce web sites, such as Amazon.com, provide platforms for consumers to review products and share their opinions. However, it is impossible for consumers to read throughout the huge amount of available reviews. In addition, the quality and helpfulness of reviews are unavailable unless consumers have to read through them.This paper proposes an entropy-based model to predict the helpfulness of reviews. Reviews can be ranked by our entropy-based scoring model and reviews that may help consumers better than others will be found. We also compare our model with several machine learning algorithms. Our experimental results show that our approach is effective in ranking and classifying online reviews. With the predicted helpfulness of reviews, consumers can make purchase decisions more easily.