摘要
【目的】基于在线评论构建游客满意度分析框架,为乡村旅游的可持续发展提供新的研究视角。【方法】围绕游客对景区的评论数据,构建基于IPA模型的游客满意度分析框架,采用无监督方法抽取游客对于景区的细粒度属性观点,并基于SnowNLP和XGBoost分别评估游客对不同属性的感知情感及感知重要性,进而采用IPA模型对景区属性的满意度进行分析。【结果】实证分析表明,所构建的满意度分析框架可以识别出用户观点,并分析不同属性的满意度。其中,案例地宏村景区的优势属性包括自然风光和娱乐,可作为景区重点宣传内容,而消费感知、商业化以及旅游服务属于重点改进属性,此外,客流量、餐饮、基础设施以及景区管理作为低优先级发展选项,在资源充足的情况下可进行有序改进。【局限】实验数据集在评分上存在数据不均衡问题。【结论】根据案例地游客满意度分析结果,探讨了促进景区可持续发展的管理和营销策略,为旅游领域相关问题提供了新思路。
[Objective]Based on online reviews,this paper constructs a framework for analyzing tourist satisfaction and provides a new research perspective for the sustainable development of rural tourism.[Methods]With the visitor comments about scenic areas,we built a tourist satisfaction analysis framework using the IPA model.Then,we used the unsupervised method to extract the visitors’fine-grained attribute opinions about the scenic attractions.Third,we evaluated the visitors’perceived emotions and the importance of different attributes with SnowNLP and XGBoost.Finally,we analyzed the satisfaction of attraction attributes with the IPA model.[Results]Empirical analysis demonstrates that the constructed framework can identify user opinions and analyze satisfaction levels for different attributes.The advantages of Hongcun scenic area include natural scenery and entertainment,which can be emphasized in promoting the area.On the other hand,consumer perception,commercialization,and tourism services need improvement.Furthermore,visitor flow,dining options,infrastructure,and scenic area management are low-priority development options that can be sequentially improved when sufficient resources are available.[Limitations]The experimental dataset has data imbalance issues in the ratings.[Conclusions]According to the analysis results of tourist satisfaction in the case study,this paper explores management and marketing strategies to promote the sustainable development of scenic areas,providing new insights into related issues in tourism.
作者
吴江
李秋贝
胡忠义
刘洋
Wu Jiang;Li Qiubei;Hu Zhongyi;Liu Yang(Center for E-commerce Research and Development,Wuhan University,Wuhan 430072,China;School of Information Management,Wuhan University,Wuhan 430072,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2023年第7期89-99,共11页
Data Analysis and Knowledge Discovery
基金
国家重点研发计划项目(项目编号:2019YFB1405600)的研究成果之一。
