Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Large-scale information retrieval with latent semantic indexing
Information Sciences: an International Journal
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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Improving customer experience on company web sites is an important aspect of maintaining a competitive edge in the technology industry. To better understand customer behavior, e-commerce sites provide online surveys for individual web site visitors to record their feedback with site performance. This paper describes some areas where text mining appears to have useful applications. For comments from web site visitors, we implemented automated analysis to discover emerging problems on the web site using clustering methods and furthermore devised procedures to assign comments to pre-defined categories using statistical classification. Statistical clustering was based on a Gaussian mixture model and hierarchical clustering to uncover new issues related to customer care-abouts. Statistical classification of comments was studied extensively by applying a variety of popular algorithms. We benchmarked their performance and make some recommendations based on our evaluations.