Predicting helpful product reviews
|Title:||Predicting helpful product reviews||Authors:||O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/2514||Date:||30-Aug-2010||Abstract:||Millions of users are today posting user-generated content online, expressing their opinions on all manner of goods and services, topics and social affairs. While undoubtedly useful,user-generated content presents consumers with significant challenges in terms of information overload and quality considerations. In this paper, we address these issues in the context of product reviews and present a brief survey of our work to date on predicting review helpfulness. In particular, the performance of a variety of different machine learning approaches is evaluated on four large-scale review datasets drawn from the TripAdvisor and Amazon domains. Our findings highlight some interesting properties of this task from a machine learning perspective and demonstrate that author reputation, the sentiment expressed in reviews and review length are among the most effective predictors of review helpfulness.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Keywords:||User-generated content;Product reviews;Review helpfulness;Classification;Amazon;TripAdvisor||Subject LCSH:||User-generated content--Evaluation
Recommender systems (Information filtering)
|Language:||en||Status of Item:||Peer reviewed||Conference Details:||Paper presented at the 21st Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2010), Galway, Ireland, 30 August - 1 September, 2010|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.