隨著競爭全球化以及經濟環境的快速變遷，導致消費者需求也越來越難以掌握，企業如何因應這樣的趨勢，已成為是否存續的關鍵。本研究旨在探討，在大數據被提出後，企業是否能透過資料科學思維的養成，從大量的資料中分析出新的商業策略，以找到新的商業契機。包含資料科學三大組成元素，即：領域知識(消費者的感性需求)、統計分析，以及程式設計。期望透過對企業電子商務網站的流量分析，掌握能真正帶來消費人次的網路文章。再藉由感性工學手法與文字探勘技術對口碑文章進行解析，萃取真正影響消費意願的感性詞彙。最後再透過傳統統計分析與資訊領域的類神經網路技術，建構足以輔助行銷策略擬定的參考模型，此一模型將可協助企業增加營收與獲利空間。

This study proposes a hybrid model in order to connect customers’ emotional factors and business goals, faciliate enterprises gain specialized knowledge and achivements. The advocate of electronic word-of-mouth (e-WOM) has received considerable attention in business communitiesand far-reaching consequences in emotional for consumers. In addition, more and more companies regconized that the payoff would be huge if they connect with customers’ emotions. Kansei words are the key to describe customers’ demands.
This study uses use blog article as the source to extract customer’s emotional factors. Text mining technique, Kansei engineering and Google Analytics solutions are applied to extract Kansei words from blog articles and filter out those articles that could effectively bring network traffic to e-commerce websites. The results of this study are desired to build up a novel application that could help enterprises in their marketing activities.

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