Reputation systems for open collaboration
Communications of the ACM
Multi-facets quality assessment of online opinionated expressions
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews
ACM Transactions on Management Information Systems (TMIS)
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
ACM Transactions on Management Information Systems (TMIS)
Discovering business intelligence from online product reviews: A rule-induction framework
Expert Systems with Applications: An International Journal
Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth
ACM Transactions on Management Information Systems (TMIS)
Predicting the helpfulness of online reviews using multilayer perceptron neural networks
Expert Systems with Applications: An International Journal
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The author identifies a new task in the ongoing research in text sentiment analysis: aggregating online product reviews in light of two orthogonal dimensions, namely, polarity/opinion extraction and usefulness scoring. The motivation is to build future review aggregation or ranking services that enable both online shoppers and vendors to better leverage information from multiple sources. Usefulness scoring is viewed as a regression problem. The author builds support-vector-regression models by incorporating a diverse set feature set computed from review text, which achieved promising performance on four Amazon product review collections. Findings also indicate that a product review's perceived usefulness is highly dependent on its linguistic style. Further rank correlation analyses on the Amazon data demonstrates the feasibility and advantage of the proposed review-aggregation framework, in the context of predicting market response to certain products. This article is part of a special issue on Natural Language Processing and the Web.