隨著大數據概念的興起，行銷領域對於大數據研究的興趣也日益增長，不僅改變了行銷領域的研究方法，也提供了新的決策工具，如何將大數據的優勢與行銷概念結合為研究人員重要的任務。而大數據行銷研究相較於傳統的行銷研究在研究方法及成果有許多差異，亦提供了管理者更豐富的決策資訊。然而，即便學術上不乏分別使用兩種方法所做的研究，仍無研究針對其特性、方法與成果進行全面性的比較。
本研究將以餐飲業顧客意見為議題，利用大數據的「6Vs」特性為研究面向，針對傳統行銷研究與大數據行銷研究之差異進行比較，探討傳統行銷研究所能帶給管理者的貢獻與尚存的缺失，而後使用Yelp評論集，結合大數據分析常用的內容分析、情緒分析及互動式視覺化技巧，驗證於餐飲業顧客意見研究上，大數據行銷研究方法是否及如何補足傳統行銷研究方法的缺失。
由研究成果可知，兩者皆有其優缺點。而大數據行銷研究得以處理更為大量、多元、真實的數據，且可快速更新與連接多個數據源，更提供管理者更為全面、具備外部資訊的決策參考，展現大數據行銷研究的優勢及其為管理者於未來決策上所能帶來的貢獻。

With the rise of the concept of big data, the interest in big data research in marketing field has also increased. It has not only changed the research methods, but also provided new decision-making tools. How to combine the advantages of big data with marketing concepts is important for researchers. Compared with traditional marketing research, big data marketing research has many differences in research methods and results, and it provides managers with richer decision-making information. However, even if there is academic research using these two methods, there is still no research to make a comprehensive comparison of their characteristics, methods and results.
This research focuses on customer opinions in the restaurant industry and uses the "6Vs" characteristics of big data as the research aspect. It compares the differences between big data analysis and traditional methodology for marketing Research. Then, it explores the contributions and remaining deficiencies that traditional marketing research brings to managers. After that, it uses the Yelp review data with the content analysis, sentiment analysis and interactive visualization techniques which are commonly used in big data analysis to verify whether and how big data marketing research methods complement the deficiencies in the traditional research of customer opinions in the restaurant industry.
The results show that both methods have their pros and cons. And big data analysis can process larger, more diverse, and more realistic data. And the data can be updated quickly or connected with multiple data sources. This provides managers with a more comprehensive decision-making reference that also contains external information, and shows the advantages of big data marketing research as well as the contribution it can bring to managers in future decision-makings.

論文審定書.......................................................................i
誌謝...................................................................................ii
摘要..................................................................................iii
Abstract............................................................................iv
圖次..................................................................................vi
表次.................................................................................vii
第一章 緒論......................................................................1
第一節 研究背景與動機..................................................1
第二節 研究目的..............................................................2
第三節 研究問題..............................................................3
第四節 研究架構..............................................................3
第二章 文獻探討..............................................................4
第一節 大數據定義與特性..............................................4
第二節 傳統的餐飲業顧客意見研究..............................6
第三節 大數據時代下的餐飲業顧客意見研究.............9
第三章 研究方法............................................................12
第一節 研究方法............................................................12
第二節 研究流程............................................................18
第四章 研究結果............................................................19
第一節 傳統研究與大數據研究的比較.......................19
第二節 傳統研究於餐飲業管理意涵上之缺失...........28
第三節 大數據行銷研究對於傳統研究缺失之補足...31
第五章 結論與建議.......................................................56
第一節 研究結論...........................................................56
第二節 研究貢獻...........................................................57
第三節 研究限制與未來研究方向...............................57
參考文獻........................................................................59

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(二) 網路資料
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