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Ting, Y., Moro, S., Rita, P. & Oliveira, C. (2022). Insights from sentiment analysis to leverage local tourism business in restaurants. International Journal of Culture, Tourism, and Hospitality Research. 16 (1), 321-336

Y. Ting et al.,  "Insights from sentiment analysis to leverage local tourism business in restaurants", in Int. Journal of Culture, Tourism, and Hospitality Research, vol. 16, no. 1, pp. 321-336, 2022

@article{ting2022_1732212292763,
	author = "Ting, Y. and Moro, S. and Rita, P. and Oliveira, C.",
	title = "Insights from sentiment analysis to leverage local tourism business in restaurants",
	journal = "International Journal of Culture, Tourism, and Hospitality Research",
	year = "2022",
	volume = "16",
	number = "1",
	doi = "10.1108/IJCTHR-02-2021-0037",
	pages = "321-336",
	url = "https://www.emerald.com/insight/publication/issn/1750-6182"
}

TY  - JOUR
TI  - Insights from sentiment analysis to leverage local tourism business in restaurants
T2  - International Journal of Culture, Tourism, and Hospitality Research
VL  - 16
IS  - 1
AU  - Ting, Y.
AU  - Moro, S.
AU  - Rita, P.
AU  - Oliveira, C.
PY  - 2022
SP  - 321-336
SN  - 1750-6182
DO  - 10.1108/IJCTHR-02-2021-0037
UR  - https://www.emerald.com/insight/publication/issn/1750-6182
AB  - Purpose: Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry. Design/methodology/approach: The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value. Findings: The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management. Originality/value: The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.
ER  -