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A novel ChatGPT-based multimodel framework for tourism review mining: a case study on China’s five sacred mountains

A novel ChatGPT-based multimodel framework for tourism review mining: a case study on China’s... This study aims to introduce an innovative framework for mining tourism reviews that not only excels in sentiment analysis accuracy but also prioritizes user-friendly design for enhanced usability.Design/methodology/approachOnline reviews of China’s Five Sacred Mountains were analyzed using an integrated methodology. Sentiment analysis was performed using ChatGPT, bidirectional encoder representations from transformers (BERT) and convolutional neural networks, with ChatGPT demonstrating superior performance. Latent Dirichlet allocation extracted key attributes. Models including importance–performance analysis (IPA), asymmetric impact-performance analysis (AIPA) and importance–performance competitor analysis (IPCA) then synthesized findings.FindingsThe results demonstrate that ChatGPT outperforms both machine learning and lexicon-based models in sentiment recognition, exhibiting performance comparable to that of the BERT model. In the case study, integrating sentiment analysis outcomes with IPA reveals deficiencies in both topics and attributes. Moreover, the synergistic combination of IPA, AIPA and IPCA furnishes actionable recommendations for resource management and enables nuanced monitoring of sustainability attributes.Practical implicationsLeveraging this framework in conjunction with the ChatGPT platform for application development can bring practical convenience to the tourism industry. It supports sentiment analysis, topic categorization and opinion mining. Equipped with monitoring capabilities, it provides valuable insights for sustainable improvement, aiding managers in formulating effective marketing strategies.Originality/valueThis research develops a novel multimodel framework integrating various ML/DL techniques and business models in a synergistic way. It provides an innovative and highly accurate yet simple approach to tourism review mining and enhances accessibility of advanced artificial intelligence for sustainable tourism monitoring, addressing limitations of prior methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hospitality and Tourism Technology Emerald Publishing

A novel ChatGPT-based multimodel framework for tourism review mining: a case study on China’s five sacred mountains

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References (32)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1757-9880
eISSN
1757-9880
DOI
10.1108/jhtt-06-2023-0170
Publisher site
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Abstract

This study aims to introduce an innovative framework for mining tourism reviews that not only excels in sentiment analysis accuracy but also prioritizes user-friendly design for enhanced usability.Design/methodology/approachOnline reviews of China’s Five Sacred Mountains were analyzed using an integrated methodology. Sentiment analysis was performed using ChatGPT, bidirectional encoder representations from transformers (BERT) and convolutional neural networks, with ChatGPT demonstrating superior performance. Latent Dirichlet allocation extracted key attributes. Models including importance–performance analysis (IPA), asymmetric impact-performance analysis (AIPA) and importance–performance competitor analysis (IPCA) then synthesized findings.FindingsThe results demonstrate that ChatGPT outperforms both machine learning and lexicon-based models in sentiment recognition, exhibiting performance comparable to that of the BERT model. In the case study, integrating sentiment analysis outcomes with IPA reveals deficiencies in both topics and attributes. Moreover, the synergistic combination of IPA, AIPA and IPCA furnishes actionable recommendations for resource management and enables nuanced monitoring of sustainability attributes.Practical implicationsLeveraging this framework in conjunction with the ChatGPT platform for application development can bring practical convenience to the tourism industry. It supports sentiment analysis, topic categorization and opinion mining. Equipped with monitoring capabilities, it provides valuable insights for sustainable improvement, aiding managers in formulating effective marketing strategies.Originality/valueThis research develops a novel multimodel framework integrating various ML/DL techniques and business models in a synergistic way. It provides an innovative and highly accurate yet simple approach to tourism review mining and enhances accessibility of advanced artificial intelligence for sustainable tourism monitoring, addressing limitations of prior methods.

Journal

Journal of Hospitality and Tourism Technology – Emerald Publishing

Published: Aug 5, 2024

Keywords: Tourism reviews; Sentiment analysis; ChatGPT; Deep learning models; Opinion mining; Sustainable tourism; 关键词： 旅游评论、情感分析、ChatGPT、深度学习模型、意见挖掘、可持续旅游

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