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
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