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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Brochado, A., Rita, P. & Moro, S. (2019). Discovering the patterns of online reviews of hostels in Beijing and Lisbon. Journal of China Tourism Research. 15 (2), 172-191
Exportar Referência (IEEE)
A. M. Brochado et al.,  "Discovering the patterns of online reviews of hostels in Beijing and Lisbon", in Journal of China Tourism Research, vol. 15, no. 2, pp. 172-191, 2019
Exportar BibTeX
@article{brochado2019_1603338645838,
	author = "Brochado, A. and Rita, P. and Moro, S.",
	title = "Discovering the patterns of online reviews of hostels in Beijing and Lisbon",
	journal = "Journal of China Tourism Research",
	year = "2019",
	volume = "15",
	number = "2",
	doi = "10.1080/19388160.2018.1543065",
	pages = "172-191",
	url = "https://www.tandfonline.com/doi/full/10.1080/19388160.2018.1543065"
}
Exportar RIS
TY  - JOUR
TI  - Discovering the patterns of online reviews of hostels in Beijing and Lisbon
T2  - Journal of China Tourism Research
VL  - 15
IS  - 2
AU  - Brochado, A.
AU  - Rita, P.
AU  - Moro, S.
PY  - 2019
SP  - 172-191
SN  - 1938-8160
DO  - 10.1080/19388160.2018.1543065
UR  - https://www.tandfonline.com/doi/full/10.1080/19388160.2018.1543065
AB  - 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.
ER  -