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.
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