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標題: 認知服務品質高低對於正負面評論與顧客滿意度調節效果之研究−以旅遊網站TripAdvisor為例
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
作者: Yi-Lin Tsai
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
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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 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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