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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) 
Moro, S., Rita, P. & Coelho, J. (2017). Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip. Tourism Management Perspectives. 23, 41-52
Exportar Referência (IEEE) 
S. M. Moro et al.,  "Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip", in Tourism Management Perspectives, vol. 23, pp. 41-52, 2017
Exportar BibTeX 
@article{moro2017_1714285700772,
	author = "Moro, S. and Rita, P. and Coelho, J.",
	title = "Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip",
	journal = "Tourism Management Perspectives",
	year = "2017",
	volume = "23",
	number = "",
	doi = "10.1016/j.tmp.2017.04.003",
	pages = "41-52",
	url = "http://www.sciencedirect.com/science/article/pii/S2211973617300387"
}
Exportar RIS 
TY  - JOUR
TI  - Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip
T2  - Tourism Management Perspectives
VL  - 23
AU  - Moro, S.
AU  - Rita, P.
AU  - Coelho, J.
PY  - 2017
SP  - 41-52
SN  - 2211-9736
DO  - 10.1016/j.tmp.2017.04.003
UR  - http://www.sciencedirect.com/science/article/pii/S2211973617300387
AB  - This study presents a data mining approach for modeling TripAdvisor score using 504 reviews published in 2015 for the 21 hotels located in the Strip, Las Vegas. Nineteen quantitative features characterizing the reviews, hotels and the users were prepared and used for feeding a support vector machine for modeling the score. The results achieved reveal the model demonstrated adequate predictive performance. Therefore, a sensitivity analysis was applied over the model for extracting useful knowledge translated into features' relevance for the score. The findings unveiled user features related to TripAdvisor membership experience play a key role in influencing the scores granted, clearly surpassing hotel features. Also, both seasonality and the day of the week were found to influence scores. Such knowledge may be helpful in directing efforts to answer online reviews in alignment with hotel strategies, by profiling the reviews according to the member and review date.
ER  -