大语言模型细粒度评论挖掘下的博物馆服务用户满意度研究

[Objective/Significance] Through fine-grained emotion analysis of domestic and foreign tourists’ comments on museum services, this paper explores tourists’ needs and preferences, and compares the differences in domestic and foreign tourists’ image perception and satisfaction towards domestic history museums so as to provide references for museum managers to formulate more targeted service strategies. [Method/Process] Firstly, it collected online reviews of museums from both domestic and foreign tourists, and extracted attribute words of museum services and attribute-level statements. Then, by fine-tuning multiple large language models and comparing the effectiveness in extracting museum users’ fine-grained reviews, it identified GPT-3 and Llama2 as the optimal classification effect for Chinese and foreign reviews, respectively. Then it employed the optimal large language model to conduct fine-grained sentiment analysis of attribute-level statements. Finally, based on the results, it analyzed the satisfaction and differences between the two groups. [Results/Conclusion] The categories of museum services reviewed by domestic tourists include cultural creation, service facilities, guided explanation, museum staff, ticket security, online service. Foreign tourists, on the other hand, focus on guided explanation, in-museum service, ticket security, online service, and shopping. The analysis reveals that both domestic and foreign tourists show the highest levels of attention and satisfaction with guided explanation. Domestic tourists have the lowest satisfaction on the staff service, while foreign tourists express the least satisfaction with the ticket security. It summarizes the key factors influencing tourist satisfaction in each service category.