کنترل کیفیت سرویس وب بر اساس متن استخراج معدن با استفاده از ماشین بردار پشتیبان

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.

Community websites generally have multiple functions, which combine contents, members and commerce to attract web users and achieve the beneficial purpose of web execution. Community websites face severe challenges because too many similar community websites share a limited market. To increase member numbers and income, the managers of community websites regularly update the web contents and functions to enhance survival conditions in this highly competitive environment. Most websites provide a message board function in customer service department to gather complaints and requests from customers. Popular community websites experience hundreds of thousands of messages entering their databases every day, leading to the customer department facing a “data explosion”. Websites require an auto mechanism to filter the useful messages and even transfer them into customer knowledge. This study proposes an auto mechanism known as Web-complaint Quality Control (WebQC), which can recognize the complaint message and issue a warning signal when the number of complaints exceeds the usual level. In the WebQC, this study first uses text mining to extract the keywords based on the weight calculation of TFIDF (Term-Frequency Inverse-Document-Frequency) (Salton & McGill, 1983) and uses SVM (Support Vector Machine) (Cortes & Vapnik, 1995) to classify the messages into four categories, including Non-Chinese message or disorder code, technical problem, report of dissatisfaction, and others. This study regards the messages involving technical problems and expressing dissatisfaction as complaints. This study uses the p-control chart to control the complaint rate. If the complaint rate exceeds the upper control limit, then WebQC issues a warning signal to demonstrate the decline in service quality. The rest of this paper is organized as follows. In Section 2, we review some related techniques of text mining. Section 3 describes the auto mechanism structure of WebQC and the methods used in this research, SVM and p-control chart. The experimental results employing WebQC on a community website for eight months are presented in Section 4. Finally, the paper is concluded in Section 5.

Currently, website management is so technical that managers cannot fully understand website management if they have inadequate IT skills. This study proposed an easy mechanism, WebQC, to facilitate website management based only on common knowledge, that is, if the complaining rate is higher than the upper control limit, then it will give the warning signal to show the decline in the service quality. This study uses text mining to automatically classify customer messages. In our experimental data, we built a 224-keyword database, and the correctness of SVM classification exceeded 80%, compared to 76% for other Chinese classification. Text mining reduces the need for human effort in message recognition and accelerates message handling by customer service departments. This mechanism can promote the level of service in website management. Following message classification, the p control chart was used to control web service quality. The p control chart has been shown to be capable of specifying quality information and detecting unusual quality problems in time. Without this control chart, it is difficult for managers to understand website quality due to the quality information being hidden under the data level. To let online company managers visualize quality information via text mining and control chart is the main aim and contribution of this study.