Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Machine Learning
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
A multi-agent system for web-based risk management in small and medium business
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Popular websites can see hundreds of messages posted per day. The key messages for customer service department are customer complaints, including technical problems and non-satisfactory reports. An auto mechanism to classify customer messages based on the techniques of text mining and support vector machine (SVM) is proposed in this study. The proposed mechanism can filter the messages into the complaints automatically and appropriately to enhance service department productivity and customer satisfaction. This study employs the p-control chart to control the complaining rate under the expected service quality level for the website execution. This study adopts a community website as an example. The experimental results demonstrated that namely the ability of the SVM to correctly recognize defective messages exceeded 83% with an average of 89% for the classifying mechanism, and the p-control chart was capable of reflecting unusual changes of service quality timely.