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A Study on the Moderating Effect of Service Quality on the Relationship between the Positive and Negative Customer Reviews and Customer Satisfaction−A Case Study of TripAdvisor
|關鍵字:||海量資料;情緒分析;正負面評論;期望失驗理論;Big Data;sentiment analysis;positive and negative customer reviews;expectation-disconfirmation theory||引用:||中文部分 孔誠志(1998）。形象公關：實務操演手冊。台北縣：科技圖書。 葉乃嘉（2013）。從社群媒體洞察消費趨勢-談Big Data情感分析。IEK產業情報網。取自http://ieknet.iek.org.tw/BookView.do?rptidno=129080477 西文部分 Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: principles and applications. Journal of Advertising Research, 47(4), 398-411. Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing science, 12(2), 125-143. Anderson, N. H. (1965). Averaging versus adding as a stimulus-combination rule in impression formation. Journal of Experimental Psychology, 70(4), 394. Anderson, R. E. (1973). Consumer dissatisfaction: The effect of disconfirmed expectancy on perceived product performance. Journal of Marketing Research, 38-44. Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. 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In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1528-1531). ACM.||摘要:||
With the development of Big Data, the marketing research and the technology development have new potential opportunities. The growth of Web2.0 is one of the reasons why data volumes keep surging, and contributes to the discussion of online customer research. Customers evaluate the product or service often depends on their satisfaction. Compare to the traditional way of measuring satisfaction, customer reviews are close to the customer real feelings.
Past studies have shown that there are some relationship and influence between positive and negative emotions and satisfaction, but the relative to their influence has no consistent conclusions. However, according to expectation-disconfirmation theory can explain it.
Sentiment analysis was applied to various fields of customer reviews, and had learned about customer's thoughts and emotions. This study used polarity dataset v2.0 and AFINN to build the database and trained the model through Naive Bayes. In this study, we selected customer reviews total of 21 TripAdvisor' hotels. This customer reviews totally contain 20996 sentences wrote by 3054 users. Finally, we used Pivot Analysis to calculate the sentiment scores and regression analysis.to get results.
The results can be verified expectation-disconfirmation theory. When the hotel is high (low) quality, the degree of the negative (positive) reviews' effect on satisfaction is higher than the positive (negative) reviews'. That is the moderating effect of service quality on the relationship between the positive and negative customer reviews and customer satisfaction.
情緒分析被運用於分析各領域的顧客評論，藉此了解顧客想法與情緒，因此研究根據polarity dataset v2.0、AFINN來建置資料庫，並透過Naive Bayes訓練模型，研究對象選取TripAdvisor品質高低飯店共21家評論資料、總共3054位使用者留言、總共留言20996句筆數，後續再進行樞紐分析算出情緒分數、及迴歸統計分析。
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