在現今網站及臉部識別應用相當廣泛的時代下，透過臉部表情預測使用者對於網站的滿意度作為使用者經驗（UX）之數據，不僅可以透過客觀生理數據做為網站滿意度的評估依據，也可降低蒐集主觀數據之時間成本，來提升分析的準確度。
本研究建置CNN模型來預測受測者的臉部表情，並與SUS量表問卷進行網站滿意度分析，共招募15位受測者參與實驗，受測者須操作3種類型網站並完成所交付之任務後填寫SUS量表，整個實驗過程將蒐集受測者臉部表情資料進行分析，探討臉部表情與網站滿意度之間的關係。
研究結果顯示臉部表情與網站滿意度有顯著的正相關，且透過皮爾森相關分析與單因子變異分析後得知，若使用者在操作網站時，出現開心（happy）或生氣（angry）之臉部表情特徵，表示使用者對該網站的滿意度為佳，相反之，若使用者在操作網站時，出現傷心（sad）之臉部表情特徵，則無法判別使用者對該網站的滿意度。期望本研究之結果能對於臉部表情預測網站滿意度帶來貢獻。

Nowadays, the application of face recognition is very extensive. The user's satisfaction with the website can possibly be predicted by facial expressions as the feedback of user experience (UX). Not only can objective physiological data be used as a basis for evaluating website satisfaction, but also the time and cost of collecting respondent data can be reduced. This study builds a CNN model to predict the participants' facial expressions, and conducts website satisfaction analysis with the System Usability Scale. A total of 15 respondents were recruited to participate in the website evaluation task. Respondents were required to fill in the System Usability Scale after interacting with three types of website and complete the assigned tasks. The facial expression data of the respondents were collected for analysis to explore the relationship between facial expression and website satisfaction. The results show that some of the facial expressions are significantly positively correlated with website satisfaction. Pearson's correlation analysis and one-way variance analysis further shows that if the user was happy or angry when interacting with the website, the facial expression features predicted that the user had a satisfied experience. If the user was sad, the facial expression features could not determine the user's satisfaction with the website. Further research is needed to develop a better prediction model based on the initial results revealed in the present facial expression prediction study.

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