住宿業訂房平台線上評論之比較性分析－以Agoda與Airbnb為例

A Comparative Analysis of the Online Reviews from Online Booking Platforms in Hospitality Industry - Evidence of Agoda and Airbnb

倪家珍(Jia-Jen Ni) ； 王笠言(Li-Yen Wang) ； 林孟彥(Tom M.Y. Lin)

《觀光休閒學報》 26卷3期 (2020/12) Pp. 259-282

https://doi.org/10.6267/JTLS.202012_26(3).0002

摘要

線上訂房平台是多數人旅遊訂房的管道，主要的線上訂房平台可以分為OTA（Online Travel Agency）型與P2P（Peer-To-Peer）型，本研究在兩平台各挑選一個訂房網站做為研究對象，並使用文字探勘來比較住宿後留下的評論內容，討論住房旅客最介意的項目，來了解兩個平台的競爭優勢。本研究運用Python進行資料爬取，於OTA平台擷取26,287個評論，P2P平台擷取4,369個評論。再運用R語言進行文字探勘，進行平台與住宿點的關鍵字比較。研究結果顯示，無論在何種訂房平台，房間都是最重要的主題。但在OTA平台，旅客更在意服務與人員；在P2P平台，旅客更在意房東與地點。同時本研究也比較了臺北與花蓮的留言，發現不同地點的旅客，在意的重點也有所不同。最後根據研究結果進行理論的討論與提出管理意涵。

關鍵字

電子口碑 ； 線上評論 ； 文字探勘 ； 住宿體驗

並列摘要

Accommodation rentals have a variety of internet distribution channels to help them sell rooms, including sites that have come to be called online travel agents (OTA), peer-to-peer (P2P) accommodation rentals websites. This study comparatively examines two major online booking platforms to explore the competitive edge of P2P accommodation in comparison to OTA by analyzing the content of online reviews. This research used Python to crawl data, getting 26,287 reviews from OTA platform and 4,369 reviews from P2P platform. Then we used R language to do the text mining, comparing the keywords between these two different online booking platforms' reviews. The results from analytics using text mining and word co-occurrence networks show that room always is the most important theme to discuss. Also, there are some discrepancies between these two platforms. Particularly, the services hotel supplying and room attendant are crucial points for the guest who's renting on OTA, and host and location are the essential points on P2P. We also compared the reviews from different cities-Taipei and Hualien, getting the different points those customers would emphasis when they choose a different city to accommodate. According to the results, we discuss the contribution from theory and management view.

並列關鍵字

electronic word-of-mouth ； online reviews ； text mining ； lodging experience

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