Exportar Publicação

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) 
Calheiros, A. C., Moro, S. & Rita, P. (2017). Sentiment classification of consumer generated online reviews using topic modeling. Journal of Hospitality Marketing and Management. 26 (7), 675-693
Exportar Referência (IEEE) 
A. C. Calheiros et al.,  "Sentiment classification of consumer generated online reviews using topic modeling", in Journal of Hospitality Marketing and Management, vol. 26, no. 7, pp. 675-693, 2017
Exportar BibTeX 
@article{calheiros2017_1775859553512,
	author = "Calheiros, A. C. and Moro, S. and Rita, P.",
	title = "Sentiment classification of consumer generated online reviews using topic modeling",
	journal = "Journal of Hospitality Marketing and Management",
	year = "2017",
	volume = "26",
	number = "7",
	doi = "10.1080/19368623.2017.1310075",
	pages = "675-693",
	url = "http://www.tandfonline.com/doi/abs/10.1080/19368623.2017.1310075"
}
Exportar RIS 
TY  - JOUR
TI  - Sentiment classification of consumer generated online reviews using topic modeling
T2  - Journal of Hospitality Marketing and Management
VL  - 26
IS  - 7
AU  - Calheiros, A. C.
AU  - Moro, S.
AU  - Rita, P.
PY  - 2017
SP  - 675-693
SN  - 1936-8623
DO  - 10.1080/19368623.2017.1310075
UR  - http://www.tandfonline.com/doi/abs/10.1080/19368623.2017.1310075
AB  - The development of the Internet and mobile devices enabled the emergence of travel and hospitality review sites, leading to a large number of customer opinion posts. While such comments may influence future demand of the targeted hotels, they can also be used by hotel managers to improve customer experience. In this article, sentiment classification of an eco-hotel is assessed through a text mining approach using several different sources of customer reviews. The latent Dirichlet allocation modeling algorithm is applied to gather relevant topics that characterize a given hospitality issue by a sentiment. Several findings were unveiled including that hotel food generates ordinary positive sentiments, while hospitality generates both ordinary and strong positive feelings. Such results are valuable for hospitality management, validating the proposed approach.
ER  -