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Title: Discovering the patterns of online reviews of hostels in Beijing and Lisbon
Authors: Brochado, A.
Rita, P.
Moro, S.
Keywords: Service quality
Online reviews
Data mining
Issue Date: 2019
Publisher: Routledge/Taylor and Francis
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 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.
Peer reviewed: yes
DOI: 10.1080/19388160.2018.1543065
ISSN: 1938-8160
Appears in Collections:CIS-RI - Artigos em revistas científicas internacionais com arbitragem científica
DINÂMIA'CET-RI - Artigo em revista científica internacional com arbitragem científica
ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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