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CLC number: TP399

On-line Access: 2020-04-21

Received: 2019-11-18

Revision Accepted: 2020-02-04

Crosschecked: 2020-03-06

Cited: 0

Clicked: 1524

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Meng-qi Cao

https://orcid.org/0000-0001-6693-6870

Ming-zhao Li

https://orcid.org/0000-0001-6164-6401

Min Zhu

https://orcid.org/0000-0002-5664-1558

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.4 P.536-557

http://doi.org/10.1631/FITEE.1900631


TDIVis: visual analysis of tourism destination images


Author(s):  Meng-qi Cao, Jing Liang, Ming-zhao Li, Zheng-hao Zhou, Min Zhu

Affiliation(s):  College of Computer Science, Sichuan University, Chengdu 610065, China; more

Corresponding email(s):   mengqi.cao@scuvis.org, jing.liang@scuvis.org, mingzhao.li@rmit.eud.au, zhenghao@vt.edu, zhumin@scu.edu.cn

Key Words:  Tourism user-generated content, Information visualization, Destination image, Sentiment visualization, Sequence visualization


Meng-qi Cao, Jing Liang, Ming-zhao Li, Zheng-hao Zhou, Min Zhu. TDIVis: visual analysis of tourism destination images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(4): 536-557.

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pages="536-557",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900631"
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Abstract: 
The study of tourism destination images is of great significance in the tourism discipline. tourism user-generated content (UGC), i.e., the feedback on tourism websites, provides rich information for constructing a destination image. However, it is difficult for tourism researchers to obtain a relatively complete and intuitive destination image due to the unintuitive destination image display, the significant variance in departure time and data length, and the destination type in UGC. We propose TDIVis, a carefully designed visual analytics system, aimed at obtaining a relatively comprehensive destination image. Specifically, a keyword-based sentiment visualization method is proposed to associate the cognitive image with the emotional image, and by this method, both time evolution analysis and classification analysis are considered; a multi-attribute association double sequence visualization method is proposed to associate two different types of text sequences and provide a dynamic visual encoding interaction method for the multi-attribute characteristics of sequences. The effectiveness and usability of TDIVis are demonstrated through four cases and a user study.

TDIVis:旅游目的地形象可视分析


曹梦琦1,梁晶1,李明召2,周峥澔3,朱敏1
1四川大学计算机学院,中国成都市,610065
2皇家墨尔本理工大学科学学院,澳大利亚维多利亚州墨尔本,3000
3弗吉尼亚理工大学机械科学学院,美国弗吉尼亚州布莱克斯堡,24061

摘要:旅游目的地形象研究在旅游学科中具有重要意义。旅游用户生成内容(UGC),即旅游网站中的用户反馈,为构建目的地形象提供了丰富信息。然而,由于UGC中目的地形象展示不够直观,且旅行出发时间、文本长度和目的地形象类别差异较大,旅游研究者难以获得较为完整、直观的目的地形象。我们设计一个可视分析系统TDIVis,旨在获得一个相对全面的目的地形象。具体而言,提出一种基于关键字的情感可视化方法,关联认知形象与情感形象,同时兼顾时序演变分析和分类对比分析;提出一种多属性关联双序列可视化方法,关联两种不同类型的文本序列,并为序列的多属性特征提供一种动态可视化编码交互方式。最后,通过4个案例和1个用户研究,验证TDIVis的有效性和可用性。

关键词:旅游用户生成内容;信息可视化;目的地形象;情感可视化;时序可视化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Baloglu S, McCleary KW, 1999. A model of destination image formation. Ann Tour Res, 26(4):868-897.

[2]Chen Y, 2018. TagNet: toward tag-based sentiment analysis of large social media data. Proc IEEE Pacific Visualization Symp, p.190-194.

[3]Crompton JL, 1979. An assessment of the image of Mexico as a vacation destination and the influence of geographical location upon that image. J Travel Res, 17(4):18-23.

[4]da Silva MA, Costa RA, Moreira AC, 2018. The influence of travel agents and tour operators’ perspectives on a tourism destination. The case of Portuguese intermediaries on Brazil’s image. J Hosp Tour Manag, 34:93-104.

[5]Gkritzali A, Gritzalis D, Stavrou V, 2018. Is Xenios Zeus still alive? Destination image of Athens in the years of recession. J Travel Res, 57(4):540-554.

[6]Hernández-Lobato L, Solis-Radilla MM, Moliner-Tena MA, et al., 2006. Tourism destination image, satisfaction and loyalty: a study in Ixtapa-Zihuatanejo, Mexico. Tour Geogr, 8(4):343-358.

[7]Hu M, Wongsuphasawat K, Stasko J, 2017. Visualizing social media content with sententree. IEEE Trans Vis Comput Graph, 23(1):621-630.

[8]Huang ZS, Zhao Y, Chen W, et al., 2019. A natural-language-based visual query approach of uncertain human trajectories. https://arxiv.org/abs/1908.00277

[9]Jeng CR, Snyder AT, Chen CF, 2019. Importance-performance analysis as a strategic tool for tourism marketers: the case of Taiwan’s destination image. Tour Hosp Res, 19(1):112-125.

[10]Kim K, Park OJ, Yun S, et al., 2017. What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technol Forecast Soc Change, 123:362-369.

[11]Kotsi F, Pike S, Gottlieb U, 2018. Consumer-based brand equity (CBBE) in the context of an international stopover destination: perceptions of Dubai in France and Australia. Tour Manag, 69:297-306.

[12]Li QS, Wu YD, Wang S, et al., 2016. VisTravel: visualizing tourism network opinion from the user generated content. J Visual, 19(3):489-502.

[13]Liu MC, Liu SX, Zhu XZ, et al., 2016. An uncertainty-aware approach for exploratory microblog retrieval. IEEE Trans Vis Comput Graph, 22(1):250-259.

[14]Liu ZY, Huang WY, Zheng YB, et al., 2010. Automatic keyphrase extraction via topic decomposition. Proc Conf on Empirical Methods in Natural Language Processing, p.366-376.

[15]Lu S, Li GH, Xu M, 2020. The linguistic landscape in rural destinations: a case study of Hongcun village in China. Tour Manag, 77:104005.

[16]Lu YF, Wang H, Landis S, et al., 2018. A visual analytics framework for identifying topic drivers in media events. IEEE Trans Vis Comput Graph, 24(9):2501-2515.

[17]Oh M, Chan ICC, Mehraliyev F, 2018. Ethnic restaurant selection patterns of U.S. tourists in Hong Kong: an application of association rule mining. In: Stangl B, Pesonen J (Eds.), Information and Communication Technologies in Tourism 2018. Springer, Sweden, p.117-128.

[18]Papadimitriou D, Kaplanidou K, Apostolopoulou A, 2018. Destination image components and word-of-mouth intentions in urban tourism: a multigroup approach. J Hosp Tour Res, 42(4):503-527.

[19]Prautzsch H, Boehm W, Paluszny M, 2002. Bézier and B-spline Techniques. Springer Science & Business Media, Berlin, Heidelberg.

[20]Rekha RS, 2018. {Exploring the cognitive image of tourists for visiting Cox’s bazar as a tourism destination in Bangladesh. J Bus Stud PUST, 1(1):20-33.}

[21]Stepchenkova S, Zhan FZ, 2013. Visual destination images of Peru: comparative content analysis of DMO and user-generated photography. Tour Manag, 36:590-601.

[22]Stylidis D, Shani A, Belhassen Y, 2017. Testing an integrated destination image model across residents and tourists. Tour Manag, 58:184-195.

[23]Sun MH, Ryan C, Pan S, 2015. Using Chinese travel blogs to examine perceived destination image: the case of New Zealand. J Travel Res, 54(4):543-555.

[24]Tseng C, Wu BH, Morrison AM, et al., 2015. Travel blogs on China as a destination image formation agent: a qualitative analysis using Leximancer. Tour Manag, 46:347-358.

[25]Wang R, Hao JX, 2018. Gender difference on destination image and travel options: an exploratory text-mining study. Proc 15th Int Conf on Service Systems and Service Management, p.1-5.

[26]Wang XT, Liu SX, Chen Y, et al., 2016. How ideas flow across multiple social groups. Proc IEEE Conf on Visual Analytics Science and Technology, p.51-60.

[27]Wu YC, Chen ZT, Sun GD, et al., 2018. StreamExplorer: a multi-stage system for visually exploring events in social streams. IEEE Trans Vis Comput Graph, 24(10):2758-2772.

[28]Yuan H, Xu HL, Qian Y, et al., 2014. Towards summarizing popular information from massive tourism blogs. Proc IEEE Int Conf on Data Mining Workshops, p.409-416.

[29]Yuan H, Xu HL, Qian Y, et al., 2016. Make your travel smarter: summarizing urban tourism information from massive blog data. Int J Inform Manag, 36(6):1306-1319.

[30]Zhao J, Gou L, Wang F, et al., 2014. Pearl: an interactive visual analytic tool for understanding personal emotion style derived from social media. Proc IEEE Conf on Visual Analytics Science and Technology, p.203-212.

[31]Zheng XH, Chen W, Wang P, et al., 2016. Big data for social transportation. IEEE Trans Intell Transp Syst, 17(3):620-630.

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