本文从携程网中提取有关北戴河旅游的网络游记作为研究文本,利用ROST CM6和文本分析方法对网络游记进行分析,主要借助于CM6软件从高频词分析、语义网络分析以及情感分析获取相关数据,研究游客对北戴河旅游的感知状况。研究发现游客对于北戴河的旅游感知主要表现为游客对海边的感知最强,游客对景区、景点、住宿美食的感知较强,对大范围交通感知也较强,对景区内部的交通感知较弱;游客对于北戴河购物感知较弱;语义网络分析表明北戴河旅游与海边、公园、秦皇岛之间关联性较强,其次就是与山海关等有较强的关联性;情感分析表明游客对于北戴河旅游以积极、中性情感为主,消极情绪为辅。消极情绪多体现在服务、管理等方面,但游客本身也存在差异。通过对于网络旅游游记内容的分析,本文提出相关对策,提升游客感知。Online travel notes about Beidaihe tourism were extracted from Ctrip as research texts, and ROST CM6 and text analysis method was used to analyze online travel notes. Relevant data were obtained from high-frequency word analysis, semantic network analysis and emotion analysis with the help of CM6 software to study tourists’ perception of Beidaihe tourism. The study reveals that tourists’ perceptions of Beidaihe are primarily characterized by a strong focus on the seaside, followed by significant attention to scenic areas, attractions, accommodation, and cuisine. Perceptions of large-scale transportation are also relatively strong, while perceptions of internal transportation within scenic areas are weaker. Shopping in Beidaihe is perceived as less prominent. Semantic network analysis indicates a strong association between Beidaihe tourism and the seaside, parks, and Qinhuangdao, with secondary associations to locations such as Shanhaiguan. Sentiment analysis shows that tourists’ emotions toward Beidaihe tourism are predominantly positive and neutral, with negati
在体验经济时代下,人们对旅游服务体验的诉求不断提升。由于线上公众意见能够更加真实地表达游客的体验和反馈,越来越多的消费者依赖公众分享的信息辅助进行旅行决策。在旅行决策中,消费者的感知价值发挥着关键作用。传统基于用户内容的推荐方法研究多从用户行为偏好视角进行研究,忽视了用户感知价值的作用,影响了旅游服务的个性化推荐效果。因此本文从感知价值视角出发,提出适用于公众意见的用户感知价值评估方法。首先,将用户期望作为前景理论参照点,通过词性抽取规则和半监督学习方法,有效解决了公众文本中用户期望信息稀缺的问题;其次,提出融合用户期望的群体聚类优化方法,提升了群体期望构建的准确性。进而,将前景理论和多属性决策模型结合评估用户感知价值。通过概率语言决策矩阵刻画公众意见,基于前景理论构建概率语言感知矩阵,将公众意见转化为感知价值。以TODIM方法为基础,集结大群体公众意见得到备选方案的量化评估。最后,基于真实旅游评论数据的实证研究验证了该方法的有效性,为提升个性化推荐效果提供了新思路。In the era of experience economy, consumers’ demand for travel service experience is rising. Online public opinion can express tourists’ experiences and feedback more objectively, and more and more consumers rely on the information shared by the public to assist in their travel decisions. Consumers’ perceived value plays a key role in travel decision-making. The traditional user content-based recommendation methods are mostly studied from the perspective of user behavioral preferences, ignoring the influence of user perceived value, which affects the effect of personalized recommendation of travel services. Therefore, this paper proposes a user perceived value assessment method applicable to public opinion from the perspective of perceived value. Firstly, th
