Using readability tests to predict helpful product reviews

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Title: Using readability tests to predict helpful product reviews
Authors: O'Mahony, Michael P.
Smyth, Barry
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 reviewsClassificationHelpfulTripAdvisorAmazon
Subject LCSH: User-generated content--Evaluation
User-generated content--Classification
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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