Insights from sentiment analysis to leverage local tourism business in restaurants
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/23614 |
Resumo: | 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. |
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Insights from sentiment analysis to leverage local tourism business in restaurantsGiethoornLexalyticsOnline reviewsRestaurant businessSentiment classificationSocial mediaText miningPurpose: 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.Emerald2021-12-02T13:41:21Z2022-01-01T00:00:00Z20222023-03-28T12:47:30Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/23614eng1750-618210.1108/IJCTHR-02-2021-0037Ting, Y.Moro, S.Rita, P.Oliveira, C.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:42:43Zoai:repositorio.iscte-iul.pt:10071/23614Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:20:02.035337Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Insights from sentiment analysis to leverage local tourism business in restaurants |
title |
Insights from sentiment analysis to leverage local tourism business in restaurants |
spellingShingle |
Insights from sentiment analysis to leverage local tourism business in restaurants Ting, Y. Giethoorn Lexalytics Online reviews Restaurant business Sentiment classification Social media Text mining |
title_short |
Insights from sentiment analysis to leverage local tourism business in restaurants |
title_full |
Insights from sentiment analysis to leverage local tourism business in restaurants |
title_fullStr |
Insights from sentiment analysis to leverage local tourism business in restaurants |
title_full_unstemmed |
Insights from sentiment analysis to leverage local tourism business in restaurants |
title_sort |
Insights from sentiment analysis to leverage local tourism business in restaurants |
author |
Ting, Y. |
author_facet |
Ting, Y. Moro, S. Rita, P. Oliveira, C. |
author_role |
author |
author2 |
Moro, S. Rita, P. Oliveira, C. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Ting, Y. Moro, S. Rita, P. Oliveira, C. |
dc.subject.por.fl_str_mv |
Giethoorn Lexalytics Online reviews Restaurant business Sentiment classification Social media Text mining |
topic |
Giethoorn Lexalytics Online reviews Restaurant business Sentiment classification Social media Text mining |
description |
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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-02T13:41:21Z 2022-01-01T00:00:00Z 2022 2023-03-28T12:47:30Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/23614 |
url |
http://hdl.handle.net/10071/23614 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1750-6182 10.1108/IJCTHR-02-2021-0037 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Emerald |
publisher.none.fl_str_mv |
Emerald |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134760441741312 |