Discovering Patterns in Online Reviews of Beijing and Lisbon Hostels

ABSTRACT

This study employed a data mining approach to model the quantitative scores given to hostels located in Beijing, China, and Lisbon, Portugal, in guests’ online reviews posted on Booking.com. A neural network was built using a total of nine input features (e.g. age, most and least favorite aspects, travel and traveler types, nationality, hostel, and month and weekday of review) to model the score distributions. Each feature’s contribution to the scores was then extracted through data-based sensitivity analysis. The most favorite aspect and continent of origin were the two most significant features for hostels in both cities. Lisbon guests were also highly influenced by the hostel itself and traveler type as compared with Beijing travelers. Notably, facilities are the most favorite aspect valued by guests staying in Lisbon, while those that stay in Beijing hostels give more importance to value for money. These findings denote different guest behaviors are associated with each city’s particular offerings.

摘要

本文采用数据挖掘的方法，研究缤客 (Booking.com) 网站上住客对位于中国北京和葡萄牙里斯本的青年旅舍的在线点评中的定量打分进行建模。研究共采用九个输入特征（如年龄、最喜欢的和最不喜欢的方面、出行类型和住客类型、国籍、所住旅舍以及给出点评的月份和工作日），来建立神经网络以对得分分布进行建模。之后通过基于数据的敏感性分析提取出每个特征对于得分的贡献程度。无论是位于北京还是里斯本的青年旅舍，其两大最显著的特征都是住客最喜欢的方面和住客所来自的大陆。与北京的住客相比，里斯本的住客还受到锁住旅舍和住客类型这两个输入特征的重大影响。值得注意的是，旅舍设施是到访里斯本的住客最喜欢的方面，而到访北京的住客更看中性价比。这些发现表明不同住客行为与每个城市的具体情况相关。

KEYWORDS:

• Service quality
• hostels
• online reviews
• data mining
• Beijing
• Lisbon

关键词:

• 服务质量
• 青年旅舍
• 在线点评
• 数据挖掘
• 北京
• 里斯本

Disclosure statement

No potential conflict of interest was reported by the authors.