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António, N., de Almeida, A. & Nunes, L. (2018). Predictive models for hotel booking cancellation: a semiautomated analysis of the literature. In José António C. Santos, Margarida C. Santos, Marisol B. Correia, Célia Ramos (Ed.), Tourism and Management Studies International Conference, TMS Algarve 2018. Olhão: Escola Superior de gestão, Hotelaria e Turismo, Universidade do Algarve.

N. M. António et al.,  "Predictive models for hotel booking cancellation: a semiautomated analysis of the literature", in Tourism and Management Studies Int. Conf., TMS Algarve 2018, José António C. Santos, Margarida C. Santos, Marisol B. Correia, Célia Ramos, Ed., Olhão, Escola Superior de gestão, Hotelaria e Turismo, Universidade do Algarve, 2018

@inproceedings{antónio2018_1714590153116,
	author = "António, N. and de Almeida, A. and Nunes, L.",
	title = "Predictive models for hotel booking cancellation: a semiautomated analysis of the literature",
	booktitle = "Tourism and Management Studies International Conference, TMS Algarve 2018",
	year = "2018",
	editor = "José António C. Santos, Margarida C. Santos, Marisol B. Correia, Célia Ramos",
	volume = "",
	number = "",
	series = "",
	publisher = "Escola Superior de gestão, Hotelaria e Turismo, Universidade do Algarve",
	address = "Olhão",
	organization = "Escola Superior de gestão, Hotelaria e Turismo, Universidade do Algarve"
}

TY  - CPAPER
TI  - Predictive models for hotel booking cancellation: a semiautomated analysis of the literature
T2  - Tourism and Management Studies International Conference, TMS Algarve 2018
AU  - António, N.
AU  - de Almeida, A.
AU  - Nunes, L.
PY  - 2018
CY  - Olhão
AB  - In reservation-based industries, accurate booking cancellation forecast is of foremost importance to estimate demand. By combining data science tools and capabilities with human judgement and interpretation it is possible to demonstrate how the semiautomatic analysis of the literature can contribute to synthetize research findings and identify research topics on the subject of booking cancellation forecasting. The data used was obtained through keyword search in Scopus and Web of Science databases. The methodology presented not only diminishes human bias, but also enhances the fact that data visualization and text mining techniques facilitate abstraction, expedite analysis and contribute to the improvement of reviews. Results show that albeit the importance of
bookings’ cancellation forecast, further research on the subject is still needed. By detailing the full experimental procedure of the analysis, this work aims to encourage other authors to conduct automated literature analysis as a means to understand current research in their working fields.
ER  -