Using readability tests to predict helpful product reviews
|Title:||Using readability tests to predict helpful product reviews||Authors:||O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/2463||Date:||28-Apr-2010||Abstract:||User-generated content provides online consumers with a wealth of information. Given the ever-increasing quantity of available content and the lack of quality control applied to this content, there is a clear need to enhance the user experience when it comes to effectively leveraging this vast information source. In this paper, we address these issues in the context of user-generated product reviews. We expand on recent work to consider the performance of structural and readability feature sets on the classification of helpful product reviews. Our findings, based on a large-scale evaluation of TripAdvisor and Amazon reviews, indicate that structural and readability features are useful predictors for Amazon product reviews but less so for TripAdvisor hotel reviews.||Funding Details:||Not applicable||Type of material:||Conference Publication||Copyright (published version):||2010 Centre De Hautes Etudes Internationales D'informatique Documentaire (CID)||Keywords:||User-generated product reviews;Classification;Helpful;TripAdvisor;Amazon||Subject LCSH:||User-generated content--Evaluation
Readability (Literary style)
Recommender systems (Information filtering)
|Language:||en||Status of Item:||Peer reviewed||Conference Details:||Paper presented at RIAO 2010 the 9th international conference on Adaptivity, Personalization and Fusion of Heterogeneous Information, Paris, France, April 28-30, 2010|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
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