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旅游导刊  2021, Vol. 5 Issue (4): 79-96    DOI: 10.12054/lydk.bisu.159
    
基于UGC评论和TSE模型的我国游客爱国情感研究
刘逸(),李广涵,李晓娟
中山大学旅游学院 广东广州 510275
A Study on the Patriotic Sentiment of Chinese Tourists Based on UGC Reviews and the TSE Model
LIU Yi(),LI Guanghan,LI Xiaojuan
School of Tourism Management, Sun Yat-sen University, Guangzhou 510275, China
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摘要:

爱国情感是社会科学研究领域的重要话题,然而旅游活动对爱国情感的影响机制尚未得到充分揭示。本文基于国内9个知名旅游目的地游客评论数据,构建旅游—爱国情感词库,借助TSE旅游情感评估模型,解析旅游目的地对我国游客爱国情感的影响内容与特征,由此解析我国游客爱国情感的表现。研究结论如下:(1)相对于一般评论,爱国情感相关的游客评论均带有较强的积极情绪,正面评论的占比较高;(2)旅游目的地的资源结构与区位对触发爱国情感有显著的影响作用,相对于自然类旅游资源,历史文化与民俗等人文类旅游资源更能够触发游客的爱国情感;(3)与国家政治因素密切相关的地理区位能够触发更强烈的爱国情感。本文的贡献在于创新性地运用旅游评论大数据对爱国情感进行了捕捉,发现了资源属性和区位两个显著的影响因素,同时也为爱国情感研究贡献了新的研究方法,有助于更好地制定爱国教育推广和目的地形象营销策略,具有较好的应用价值和宏观战略意义。

关键词: 爱国情感旅游情感评估旅游大数据    
Abstract:

Although patriotic sentimentis an important research topic in social science research, the influence mechanism of tourism activities on patriotic sentiment has not yet been fully explored. Based on the data of tourists’ reviews of nine well-known domestic tourist destinations, this article constructs a tourism-related patriotism corpus. Using the tourism sentiment evaluation (TSE) model, it analyzes the content and characteristics of the influence of tourist destinations on the patriotic sentiment of Chinese tourists, thus illustrating the emotional expressions of Chinese tourists’ patriotic sentiment.
The study draws the following conclusions: (1) compared with general reviews, tourists’ reviews related to patriotic sentiment demonstrate strong positive sentiments, accounting for a much higher percentage of all reviews; (2) the resource structure and location of tourist destinations have a marked influence on domestic visitors’ sense of patriotic sentiment. Meanwhile, compared with natural attractions, artificial attractions involving historical culture and folklore may arouse tourists’ emotions of patriotic sentiment more considerably; and (3) a geographical location closely related to national and political factors can trigger stronger emotions of patriotic sentiment. The contribution of this research lies in the innovative use of big data of travelers’ reviews to capture Chinese tourists’ patriotic sentiment and in identifying the two most significant influencing factors of resource attributes and location. At the same time, it also provides new research methods for the study of patriotic sentiment. Finally, this study helps promote patriotic education and formulate better destination marketing strategies, thereby adding practical value and holding macro-strategic significance.

Key words: patriotic sentiment    tourism sentiment evaluation    tourism big data
收稿日期: 2020-04-22 出版日期: 2021-08-27
PACS:  F59  
基金资助: 教育部人文社科基金项目(19YJAZH060);广东省哲学社科项目(GD20SQ21)
作者简介: 刘 逸(1980— ),男,广东汕头人,中山大学旅游学院副教授,博士生导师,研究方向:旅游地理、旅游大数据、情感挖掘。E-mail: liuyi89@mail.sysu.edu.cn。|李广涵(1996— ),男,黑龙江佳木斯人,中山大学传播与设计学院硕士研究生,研究方向:大数据传播。|李晓娟(1999— ),女,海南乐东人,中山大学旅游学院本科生,研究方向:旅游情感地理。
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引用本文:

刘逸,李广涵,李晓娟. 基于UGC评论和TSE模型的我国游客爱国情感研究[J]. 旅游导刊, 2021, 5(4): 79-96.

LIU Yi,LI Guanghan,LI Xiaojuan. A Study on the Patriotic Sentiment of Chinese Tourists Based on UGC Reviews and the TSE Model. Tourism and Hospitality Prospects, 2021, 5(4): 79-96.

链接本文:

https://lydk.bisu.edu.cn/CN/10.12054/lydk.bisu.159        https://lydk.bisu.edu.cn/CN/Y2021/V5/I4/79

目的地
Attractions
百度
旅游
Baidu Travel
去哪儿
景点
Qunar
携程
Ctrip
马蜂窝
Mafeng-wo
猫途鹰
Trip-adviser
美团
Meituan
去哪儿
门票
Tickets Version of Qunar
途牛
Tuniu
驴妈妈
Lvma-ma
总计
Total
黄山 5 571 9 787 20 903 24 310 2 900 25 460 3 256 19 941 3 425 115 553
喀纳斯 972 792 4 510 5 425 157 2 267 745 19 65 14 952
开封 4 130 914 15 843 7 050 185 50 238 11 657 510 3 577 94 104
洛阳 9 211 2 339 16 554 12 467 1 154 129 712 13 541 810 2 700 188 488
西双版纳 4 571 3 116 15 032 5 235 210 25 660 8 861 465 1 800 64 950
阳朔 7 176 2 997 26 400 30 306 2 287 22 752 12 185 963 3 743 108 809
张家界 9 825 14 631 22 966 20 860 2 377 25 730 15 902 2 966 6 621 121 878
北京 4 997 560 35 827 6 725 640 1 320 619 287 480 52 455
常熟 661 0 9 280 893 650 4 041 1 543 720 5 599 23 387
总计 47 114 35 136 167 315 113 271 10 560 287 180 68 309 26 681 28 010 784 576
表1  9个旅游目的地的数据采集情况
总数
排名
Total ranking
目的地
Attractions
爱国情感
评论数量
Number of Comments
评论
数量
排名Ranking
正面评论
数量
Number of Positive Comments
正面评论
占比
Percentage of Positive Comments
正面
比例
排名Ranking
负面评论
数量
Number of Negative Comments
负面评论
占比
Percentage of Negative Comments
7 西双版纳 2 191 6 2 110 96.30% 1 9 0.41%
8 北京 9 811 1 9 422 96.04% 2 60 0.61%
6 喀纳斯 751 9 718 95.61% 3 6 0.80%
1 洛阳 3 740 3 3 538 94.60% 4 14 0.37%
3 黄山 2 037 7 1 923 94.40% 5 8 0.39%
4 开封 4 850 2 4 531 94.32% 6 22 0.45%
9 常熟 1 155 8 1 071 92.73% 7 3 0.26%
5 阳朔 2 975 5 2 731 91.80% 8 40 1.34%
2 张家界 3 105 4 2 780 89.53% 9 57 1.84%
表2  “爱国情感类评论”统计特征
排名Ranking 词汇Words 排名Ranking 词汇Words 排名Ranking 词汇Words 排名Ranking 词汇Words 排名Ranking 词汇Words
1 历史 6 公园 11 门票 16 旅游 21 城墙
2 景区 7 国家 12 古代 17 世界 22 民族
3 文化 8 博物馆 13 时间 18 风景 23 傣族
4 中国 9 景点 14 故宫 19 兵马俑 24 景色
5 地方 10 建筑 15 森林 20 传说 25 广场
表3  旅游目的地总体在线评论高频词
图1  9个旅游目的地的游客评论高频词
目的地 Attractions 节点数
Number of Nodes
排名
Ranking
边数
Number of lines
排名
Ranking
中心点数
Number of Core Points
喀纳斯 53 1 115 1 1
阳朔 48 2 93 5 1
黄山 47 3 84 8 1
北京 46 4 100 2 1
洛阳 43 5 99 4 2
开封 41 6 93 6 2
西双版纳 34 7 92 7 1
常熟 34 8 66 9 1
张家界 26 9 100 3 1(① 张家界的游客评论中,“张家界国家森林公园”为首要高频词,也是情感画像结构的中心词。由于本文所使用的软件暂时无法将4个字以上的词语完全展示,故该情感画像结构图中将其呈现为“张家界”“国家”“森林”“公园”,其实质是反映“张家界”这一旅游目的地,即中心节点数应为1。)
表4  旅游目的地评论情感画像统计特征
图2a  喀纳斯游客情感画像 图2b 北京游客情感画像 图2c 西双版纳游客情感画像
图3a  黄山游客情感画像 图3b 洛阳游客情感画像 图3c 开封游客情感画像
图4a  常熟游客情感画像 图4b 张家界游客情感画像 图4c 阳朔游客情感画像3.小结
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