基于网络文本分析的国家博物馆旅游体验研究  

A Study on Tourist Experience at the National Museum of China Based on Online Text Analysis

作　　者：吴雨农 范文静 

机构地区：[1]北京印刷学院经济管理学院,北京

出　　处：《可持续发展》2024年第9期2269-2278,共10页Sustainable Development

摘　　要：以中国国家博物馆为案例地,以去哪儿网站抓取的网络评论文本为研究素材,利用ROST CM6软件,采取内容分析法和社会网络分析法,从高频特征词、语义网络、情感评价三方面研究影响游客体验的要素结构。结果表明:1) “国家”“预约”“历史”词频次数靠前,在整个博物馆旅游体验中极其重要。2) 评论文本呈现出以“展览、文物、文化、预约、门票”多个核心高频词为中心,表现出整体分散、局部集中的特点。3) 由于存在预约难、电子讲解不智能、游览盲目性导致时间浪费无法参观更多文物等问题,从而导致积极情绪程度中的高度情绪不高。This study took the National Museum of China as a case and utilized online review texts collected from the Qunar website as research material. Using ROST CM6 software, content analysis and social network analysis methods were employed to investigate the structural factors influencing tourist experiences from three aspects: high-frequency characteristic words, semantic networks, and sentiment evaluation. The results indicate that: 1) The words “national”, “reservation”, and “history” appear with high frequency, underscoring their critical importance in the overall museum tourism experience;2) The review texts demonstrate a pattern characterized by multiple core high-frequency words such as “exhibition”, “relics”, “culture”, “reservation” and “tickets”, which exhibit a general dispersal with localized concentration;3) Issues such as difficulties in making reservations, the lack of intelligence in electronic guides, and the aimlessness of tours leading to wasted time and fewer opportunities to view more relics contribute to a low level of high-intensity positive emotions.

关 键 词：国家博物馆 旅游体验 网络文本 语义网络 情感评价 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]