基于UGC评论和TSE模型的我国游客爱国情感研究

doi:10.12054/lydk.bisu.159

摘要/Abstract

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

中图分类号:

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[J]. Tourism and Hospitality Prospects, 2021, 5(4): 79-96.

图/表 8

参考文献 31

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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

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

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%

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%

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