Research on User Satisfaction of Museum Service Based on Fine-grained Comment Mining of Large Language Model
[Objective/Significance]Through fine-grained emotion analysis of domestic and foreign tourists'com-ments 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 re-views,respectively.Then it employed the optimal large language model to conduct fine-grained sentiment analysis of at-tribute-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 do-mestic 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.
fine-grained sentiment analysismuseumuser satisfactionlarge language model
NETL
NSTL
万方数据
