Mining and summarizing customer reviews
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Linear prediction models with graph regularization for web-page categorization
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Utility scoring of product reviews
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Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
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Finding high-quality content in social media
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Topic modeling with network regularization
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Web spam identification through content and hyperlinks
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Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
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How opinions are received by online communities: a case study on amazon.com helpfulness votes
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Mining opinion features in customer reviews
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Automatically assessing review helpfulness
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Trust network inference for online rating data using generative models
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Recommender systems with social regularization
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Toponym resolution in social media
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ETF: extended tensor factorization model for personalizing prediction of review helpfulness
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Identifying helpful reviews based on customer's mentions about experiences
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Context-aware review helpfulness rating prediction
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Review rating prediction based on the content and weighting strong social relation of reviewers
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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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Online reviews in which users publish detailed commentary about their experiences and opinions with products, services, or events are extremely valuable to users who rely on them to make informed decisions. However, reviews vary greatly in quality and are constantly increasing in number, therefore, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the problem by treating a review as a stand-alone document, extracting features from the review text, and learning a function based on these features for predicting the review quality. In this work, we exploit contextual information about authors' identities and social networks for improving review quality prediction. We propose a generic framework for incorporating social context information by adding regularization constraints to the text-based predictor. Our approach can effectively use the social context information available for large quantities of unlabeled reviews. It also has the advantage that the resulting predictor is usable even when social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social context information can help improve the accuracy of review quality prediction especially when the available training data is sparse.