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  • 學位論文

旅館線上評論之有益性評估:以Tripadvisor為例

Evaluating the Helpfulness of Online Hotel Reviews

指導教授 : 胡雅涵
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摘要


隨著網際網路的蓬勃發展,旅客能在全球性的網路平台上分享個人的旅遊經驗,使用者不在只是被動的接收訊息,而是成為主動的訊息傳播者,建立了”who” says “what” and “how” they say it. 的架構。由於網路評論越來越多,但其品質和有益性(Helpfulness)卻有很大的差異,造成了資訊超載(Information overload)的問題,網路資訊容易取得但卻難以處理和判斷,而一篇有幫助的評論不僅能影響消費者的購買決策,更能進一步的影響企業未來的銷售額和利潤,因此快速且準確地辨識出有幫助的評論變成一項重要的議題。本研究使用Tripadvisor.com旅遊社群網站作為實驗資料,收集美國紐約市、拉斯維加斯、芝加哥、奧蘭多和邁阿密共五個城市的所有旅館評論,針對評論品質、情感分析和評論者特性,三個面向對評論有益性進行全面性的探討。應用WEKA資料探勘軟體的分類技術來建立預測模型,實驗中除了比較不同分類技術和不同城市資料集間的效能差異外,還要瞭解不同分類特徵類別間的相對重要性,另外再透過屬性篩選來瞭解重要的研究變項。研究結果發現,評論者特徵能有效的幫助預測評論有益性,其中以RECENCY最為重要,雖然品質特徵和情感特徵對於預測評論有益性之效能不如預期,但能透過特徵選取的方式過濾掉干擾變數,使用少數幾個最重要的變項進行訓練,建立出良好的預測模式,幫助旅客或是旅館業者找出最有幫助的評論。

並列摘要


With the rapid development of the Internet, passengers are able to share their travel experience on the global Internet platform. The users are not only receive the information passively, but become the active information disseminator. In such a context, a framework includes “who” says “what” and “how” they say it has been created. Despite the increasing of the user reviews, the differences between quality and helpfulness have caused information overload. The information on the Internet is easy to access but hard to handle and determine. However, a helpful review not only can affect the consumers’ decision, but also influence the enterprises’ sales amount and profit. Therefore, it is an important issue to identify helpful review fast and precisely. This study use Tripadvisor.com as a database for the empirical analysis. The data include all the hotel reviews among five cites in the U.S., namely, New York City, Las Vegas, Chicago, Orlando and Miami. The main purpose of the thesis is to analyze review helpfulness through three features, including review quality, sentiment analysis and reviewer characteristic. Using WEKA data mining software to build predictive models, we evaluate the prediction performance of different classification techniques and different area datasets. We further examine the relative importance of the different feature categories, through select attribute module to understand the most important research variables. The empirical evaluation suggests the reviewer features are shown to have the most impact, RECENCY turned out to be the strongest single predictor. Although the quality and sentiment features are not good as we expected, we can filter out noise variables by feature selection, use a few of the most important variables to construct a good prediction model to help travelers or travel industry to find the most helpful reviews.

參考文獻


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