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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., Esmerado, J. & Oliveira, C. (2019). Unfolding the drivers for sentiments generated by Airbnb experiences. International Journal of Culture, Tourism, and Hospitality Research. 13 (4), 430-442
Exportar Referência (IEEE)
S. M. Moro et al.,  "Unfolding the drivers for sentiments generated by Airbnb experiences", in Int. Journal of Culture, Tourism, and Hospitality Research, vol. 13, no. 4, pp. 430-442, 2019
Exportar BibTeX
@article{moro2019_1618057457213,
	author = "Moro, S. and Rita, P. and Esmerado, J. and Oliveira, C.",
	title = "Unfolding the drivers for sentiments generated by Airbnb experiences",
	journal = "International Journal of Culture, Tourism, and Hospitality Research",
	year = "2019",
	volume = "13",
	number = "4",
	doi = "10.1108/IJCTHR-06-2018-0085",
	pages = "430-442",
	url = "https://www.emerald.com/insight/content/doi/10.1108/IJCTHR-06-2018-0085/full/html"
}
Exportar RIS
TY  - JOUR
TI  - Unfolding the drivers for sentiments generated by Airbnb experiences
T2  - International Journal of Culture, Tourism, and Hospitality Research
VL  - 13
IS  - 4
AU  - Moro, S.
AU  - Rita, P.
AU  - Esmerado, J.
AU  - Oliveira, C.
PY  - 2019
SP  - 430-442
SN  - 1750-6182
DO  - 10.1108/IJCTHR-06-2018-0085
UR  - https://www.emerald.com/insight/content/doi/10.1108/IJCTHR-06-2018-0085/full/html
AB  - Purpose
Airbnb  Experiences  is  a  new  type  of  service  launched  by  Airbnb  in  November  2016 where users can offer travellers a wide range of activities. This study devotes attention to  analysing  customer  feedback  expressed  in  online  reviews  published  in  Airbnb  to evaluate those experiences.
Design/methodology/approach
A   total   of   1,110   reviews   were   collected   from   twelve   categories,   including   111 experiences,  thus  ten  reviews per  experience.  First,  the  sentiment  score  was  computed based on the text of the reviews. Second, seventeen quantitative features encompassing user,  experience,  and  review  information  were  used  to  model  the  score  through  a support   vector   machine.   Third,   a sensitivity   analysis   was   performed   to   extract knowledge on the most relevant features influencing the sentiment score.
Findings
Touristswriting  online  reviews  are  not  only  influenced  by  their  tourist  experience,  but also  by  their  own  online  experience  with  the  booking  and  online review platform. The number of reviews made by the user accounted for more than 20% of relevance, while users with more reviews tend to grant more positive reviews.
Originality/value
Current literature is enhanced with a conceptual model grounded on existing studies that assess  tourist  satisfaction  with  tour  services.  Both services online  visibility  and  user characteristics  have  shown  significant  importance  to  tourist  satisfaction,  adding  to the existing body of knowledge.
ER  -