Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Communications of the ACM - Supporting community and building social capital
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Utility scoring of product reviews
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Understanding user behavior in online feedback reporting
Proceedings of the 8th ACM conference on Electronic commerce
Show me the money!: deriving the pricing power of product features by mining consumer reviews
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Needs-based analysis of online customer reviews
Proceedings of the ninth international conference on Electronic commerce
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
Fast collapsed gibbs sampling for latent dirichlet allocation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The effect of negative online consumer reviews on product attitude: An information processing view
Electronic Commerce Research and Applications
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Identification of influencers - Measuring influence in customer networks
Decision Support Systems
Semi-supervised graph clustering: a kernel approach
Machine Learning
Overcoming the J-shaped distribution of product reviews
Communications of the ACM - A View of Parallel Computing
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
A quality-aware model for sales prediction using reviews
Proceedings of the 19th international conference on World wide web
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A ν-twin support vector machine (ν-TSVM) classifier and its geometric algorithms
Information Sciences: an International Journal
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding unusual review patterns using unexpected rules
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
SWITCHBOARD: telephone speech corpus for research and development
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Fraud detection in online consumer reviews
Decision Support Systems
Quality evaluation of product reviews using an information quality framework
Decision Support Systems
Rough set and ensemble learning based semi-supervised algorithm for text classification
Expert Systems with Applications: An International Journal
Strength of social influence in trust networks in product review sites
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Predictive client-side profiles for personalized advertising
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
ETF: extended tensor factorization model for personalizing prediction of review helpfulness
Proceedings of the fifth ACM international conference on Web search and data mining
Estimation of mixture models using Co-EM
ECML'05 Proceedings of the 16th European conference on Machine Learning
Graph-Based Semi-Supervised Learning and Spectral Kernel Design
IEEE Transactions on Information Theory
Identifying helpful reviews based on customer's mentions about experiences
Expert Systems with Applications: An International Journal
Identifying helpful online reviews: A product designer's perspective
Computer-Aided Design
Networked individuals predict a community wide outcome from their local information
Decision Support Systems
Editorial: data mining in electronic commerce - support vs. confidence
Journal of Theoretical and Applied Electronic Commerce Research
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In e-commerce, online product reviews significantly influence the purchase decisions of buyers and the marketing strategies employed by vendors. However, the abundance of reviews and their uneven quality make distinguishing between useful and useless reviews difficult for potential customers, thereby diminishing the benefits of online review systems. To address this problem, we develop a semi-supervised system called Online Review Quality Mining (ORQM). Embedded with independent component analysis and semi-supervised ensemble learning, ORQM exploits two opportunities: the improvement of classification performance through the use of a few labeled instances and numerous unlabeled instances, and the effectiveness of the social characteristics of e-commerce communities as identifiers of influential reviewers who write high-quality reviews. Three complementary experiments on datasets from Amazon.com show that ORQM exhibits remarkably higher performance in classifying reviews of different quality levels than do other well-accepted state-of-the-art text mining methods. The high performance of ORQM is also consistent and stable even under limited availability of labeled instances, thereby outperforming other baseline methods. The experiments also reveal that (1) the social features of reviewers are important in deriving better classification results; (2) classification results are affected by product type given the different purchase habits of consumers; and (3) reviews are contingent on the inherent nature of products, such as whether they are search goods or experience goods, and digital products or physical products, through which purchase decisions are influenced.