Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/23614
Author(s): | Ting, Y. Moro, S. Rita, P. Oliveira, C. |
Date: | 2022 |
Title: | Insights from sentiment analysis to leverage local tourism business in restaurants |
Journal title: | International Journal of Culture, Tourism, and Hospitality Research |
Volume: | 16 |
Number: | 1 |
Pages: | 321 - 336 |
Reference: | 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. http://dx.doi.org/10.1108/IJCTHR-02-2021-0037 |
ISSN: | 1750-6182 |
DOI (Digital Object Identifier): | 10.1108/IJCTHR-02-2021-0037 |
Keywords: | Giethoorn Lexalytics Online reviews Restaurant business Sentiment classification Social media Text mining |
Abstract: | 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. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Files in This Item:
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article_83671.pdf | 675,38 kB | Adobe PDF | View/Open |
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