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基于网络文本挖掘的国家公园旅游满意度评价研究——基于武夷山国家公园的数据分析

Study of Tourist Satisfaction Evaluation in Nation Parks based on Web Text Mining--Data Analysis of Wuyishan National Park
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摘要 通过分析在线评论,评估中国国家公园的旅游满意度,帮助园区确定游客关注点,进而有的放矢地制定改善方案。应用LDA主题分析方法,从2021年至今的武夷山国家公园游客评论中提取15个游客关注主题,测量每个主题的关注度。随后,使用SnowNLP情感分析工具从关注主题中获取游客情感倾向,进而对关注主体的观点进行客观分析。最后,应用IPA模型研究每个主题权重与游客满意度间的关系。研究结果显示,娱乐项目和生态价值两个主题在游客关注度和满意度方面表现出色,凸显了它们在国家公园旅游中的重要性;而酒店住宿方面的满意度较低,需要改进和提升服务质量。研究结论:国家公园管理者应更好地了解游客需求和关注点,以改进服务,提升游客满意度,并推动国家公园的可持续发展。 This research evaluates the satisfaction of tourists in Chinese national parks by analyzing online reviews to help parks identify areas of visitor interest and formulate targeted improvement plans.The LDA topic analysis method is applied to extract 15 visitor interest topics from online reviews of Wuyishan National Park from 2021 to the present,measuring the level of interest in each topic.Subsequently,the SnowNLP sentiment analysis tool is used to assess the emotional tendencies of visitors from the identified topics,facilitating an objective analysis of visitor perspectives on these subjects.Finally,the IPA model is employed to explore the relationship between the weight of each topic and visitor satisfaction.The research findings highlight that entertainment and ecological value are two topics that exhibit strong visitor interest and satisfaction,emphasizing their importance in national park tourism.However,satisfaction with hotel accommodations is relatively low,indicating the need for improvement and enhanced service quality.The study conclusions provide national park managers with a better understanding of visitor demands and priorities,enabling them to enhance services,improve visitor satisfaction,and promote the sustainable development of national parks.
作者 杨博雷 YANG Bo-lei(Anqing Normal University,Anqing 246133,China)
出处 《黑龙江生态工程职业学院学报》 2024年第2期56-62,共7页 Journal of Heilongjiang Vocational Institute of Ecological Engineering
基金 安庆师范大学传媒学院研究生学术创新项目“皖江非物质文化遗产数字化传播的路径创新研究”(CMQM2022D16)。
关键词 国家公园 LDA主题分析 网络文本挖掘 IPA模型 National Park LDA Topic Analysis Web Text Mining IPA Model
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