基于文本挖掘的城市旅游社区形象感知研究——以北京市为例

发布时间：2018-06-09 13:00

本文选题：城市 + 旅游社区　；参考：《地理研究》2017年06期

【摘要】：城市旅游社区是承载多元文化、彰显城市魅力的空间载体,是城市旅游特色目的地体系的重要组成部分。网络点评客观地记录了游客对旅游地的感受,为城市旅游社区形象感知的研究提供了一个新的角度。运用文本挖掘法对旅游者发表在百度旅游、携程旅行网等有关北京市典型城市旅游社区的点评进行质性研究。通过分析国内旅游者对南锣鼓巷地区、798艺术区、三里屯、什刹海、大栅栏地区等5个旅游社区认知形象、情感形象和总体形象感知,得出以下结论:(1)城市旅游社区认知形象要素可归纳为建筑类、饮食类、文化类、相关地名、(适游)群体、业态等6个群组;(2)各旅游社区情感意象各有特点,体现出一种对认知形象的延伸,但总体而言,文化和氛围类的情感意象较为突出;(3)旅游者对旅游社区的情感态度以喜欢和肯定为主,但在文化理解和感受等方面,各旅游社区存在差异;(4)旅游社区的类型可归纳为历史文化型、现代文化型、综合型。根据研究结论,提出社区旅游可持续发展的对策与建议。
[Abstract]:Urban tourism community is the carrier of multi culture, the space carrier showing the city charm and an important part of the urban tourism characteristic destination system. The network review objectively records the feelings of tourists to the tourist sites, and provides a new angle for the research on the perception of urban tourism community image. Through the analysis of the qualitative research on the Baidu tourism, Ctrip Travel Network and other Beijing typical urban tourist communities, the following conclusions are drawn by analyzing the cognitive image, emotional image and overall image perception of 5 tourist communities in Nanluogu Lane area, 798 art area, Sanlitun, Shichahai and the great palisade area. (1) urban tourism The elements of community cognitive image can be summed up in 6 groups: architecture, diet, culture, related place names, (suitable travel) groups and business forms; (2) the emotional images of each tourist community have their own characteristics, showing an extension of the cognitive image, but on the whole, the emotional images of culture and atmosphere are more prominent; (3) the emotional state of tourists to the tourist community. There are differences in the tourist communities in cultural understanding and feeling. (4) the types of tourist communities can be summed up as historical and cultural, modern culture and comprehensive. According to the conclusions of the study, the countermeasures and proposals for the sustainable development of community tourism are put forward.
【作者单位】：中国科学院地理科学与资源研究所;中国科学院区域可持续发展分析与模拟重点实验室;中国科学院大学;北京师范大学地理学与遥感科学学院;
【分类号】：F592.7

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