一個受觀迎的網站可能會從網路會員處收到千百封的留言，最重要的訊息是屬於顧客關於技術需求及不滿意的怨言。本研究提出一個自動機制(WebQC)基於文化探勘和支援向量機(SVM)技術來分類顧客的留言，此機制可以自動過濾抱怨的留言並且正確地增加客服部的生產力以及顧客滿意度。本研究在抱怨率上使用了不良率管制圖(p-control chart)來檢查服務品質是否低於網站運行的期望水準，並以一社群網站案例作為實驗案例，根據實驗結果顯示，其分類正確率或檢定力(如果留言為抱怨，SVM可以辨識為抱怨)的正確率超過83%(平均值為89%)，而不良率管制圖可適時地反應出非隨機的狀況。

A popular Web site may receive hundreds for thousands of messages each day from site users. The most useful customer feedback consists of technical requests and complaints. In this research, we propose the WebQC software to classify a user’s feedback messages by using text-mining techniques and a support vector machine (SVM). Our software can filter the messages as complaints or other kinds of messages automatically and thus effectively increase the productivity of the customer service department and as well as customer satisfaction. In this research, we employ the –control chart on the complaints scale to check the quality of the Web site as our test case. The empirical results show that our ability to classify a message accurately (i.e., if a message is a complaint, the SVM recognizes it as a complaint) is over 83% (on average 89%). Also, the p-control chart has the ability to reflect a normal situation in real-time.