TY - JOUR
T1 - Insights from sentiment analysis to leverage local tourism business in restaurants
AU - Yu, Ting
AU - Rita, Paulo
AU - Moro, Sérgio
AU - Oliveira, Cristina
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
Yu, T., Rita, P., Moro, S., & Oliveira, C. (2021). Insights from sentiment analysis to leverage local tourism business in restaurants. International Journal of Culture, Tourism, and Hospitality Research. https://doi.org/10.1108/IJCTHR-02-2021-0037 ---%ABS1%--- Funding Information: The work by S. Moro and C.Oliveira was partially funded by national funds through FCT ‐ Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04466/2020. The work by P. Rita was partially funded by national funds through FCT ‐ Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04152/2020 ‐ Centro de Investigação em Gestão de Informação (MagIC).
Publisher Copyright:
© 2021, Emerald Publishing Limited.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
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.
KW - Giethoorn
KW - Lexalytics
KW - Online reviews
KW - Restaurant business
KW - Sentiment classification
KW - Social media
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85119583291&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000721980300001
U2 - 10.1108/IJCTHR-02-2021-0037
DO - 10.1108/IJCTHR-02-2021-0037
M3 - Article
AN - SCOPUS:85119583291
SN - 1750-6182
VL - 16
SP - 321
EP - 336
JO - International Journal of Culture, Tourism, and Hospitality Research
JF - International Journal of Culture, Tourism, and Hospitality Research
IS - 1
ER -