The nature of statistical learning theory
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Introduction to Modern Information Retrieval
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Optimizing search engines using clickthrough data
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Mining and summarizing customer reviews
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Thumbs up?: sentiment classification using machine learning techniques
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Learning subjective nouns using extraction pattern bootstrapping
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Learning extraction patterns for subjective expressions
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Determining the sentiment of opinions
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Feature generation for text categorization using world knowledge
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Support vector machines for spam categorization
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The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data
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Foundations and Trends in Information Retrieval
A Unified Framework for Opinion Retrieval
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An Entropy-Based Model for Discovering the Usefulness of Online Product Reviews
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HelpMeter: A Nonlinear Model for Predicting the Helpfulness of Online Reviews
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How opinions are received by online communities: a case study on amazon.com helpfulness votes
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Exploiting Surface Features for the Prediction of Podcast Preference
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Automatically assessing the post quality in online discussions on software
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Discovering clues for review quality from author's behaviors on e-commerce sites
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Learning to recommend helpful hotel reviews
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Using an Information Quality Framework to Evaluate the Quality of Product Reviews
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A model for evaluating the quality of user-created documents
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Voice of the customers: mining online customer reviews for product feature-based ranking
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Detecting product review spammers using rating behaviors
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Efficient confident search in large review corpora
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Quality evaluation of product reviews using an information quality framework
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Understanding differences in perceived peer-review helpfulness using natural language processing
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Was this review helpful to you?: it depends! context and voting patterns in online content
Proceedings of the 23rd international conference on World wide web
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User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating.