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.
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References
Baloglu S, McCleary KW, 1999. A model of destination image formation. Ann Tour Res, 26(4):p. 868–897. https://doi.org/10.1016/S0160-7383(99)00030-4
Chen Y, 2018. TagNet: toward tag-based sentiment analysis of large social media data. Proc IEEE Pacific Visualization Symp, p.190–194. https://doi.org/10.1109/PacificVis.2018.00032
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):p. 18–23. https://doi.org/10.1177/004728757901700404
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. https://doi.org/10.1016/j.jhtm.2018.01.002
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):p. 540–554. https://doi.org/10.1177/0047287517705225
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. https://doi.org/10.1080/14616680600922039
Hu M, Wongsuphasawat K, Stasko J, 2017. Visualizing social media content with sententree. IEEE Trans Vis Comput Graph, 23(1):p. 621–630. https://doi.org/10.1109/TVCG.2016.2598590
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
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):p. 112–125. https://doi.org/10.1177/1467358417704884
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. https://doi.org/10.1016/j.techfore.2017.01.001
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. https://doi.org/10.1016/j.tourman.2018.06.019
Li QS, Wu YD, Wang S, et al., 2016. VisTravel: visualizing tourism network opinion from the user generated content. J Visual, 19(3):p. 489–502. https://doi.org/10.1007/s12650-015-0330-x
Liu MC, Liu SX, Zhu XZ, et al., 2016. An uncertainty-aware approach for exploratory microblog retrieval. IEEE Trans Vis Comput Graph, 22(1):p. 250–259. https://doi.org/10.1109/TVCG.2015.2467554
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.
Lu S, Li GH, Xu M, 2020. The linguistic landscape in rural destinations: a case study of Hongcun village in China. Tour M anag, 77:104005. https://doi.org/10.1016/j.tourman.2019.104005
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):p. 2501–2515. https://doi.org/10.1109/TVCG.2017.2752166
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. https://doi.org/10.1007/978-3-319-72923-7_10
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):p. 503–527. https://doi.org/10.1177/1096348015584443
Prautzsch H, Boehm W, Paluszny M, 2002. Bézier and B-spline Techniques. Springer Science & Business Media, Berlin, Heidelberg.
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):p. 20–33.
Stepchenkova S, Zhan FZ, 2013. Visual destination images of Peru: comparative content analysis of DMO and user-generated photography. Tour M anag, 36:590–601. https://doi.org/10.1016/j.tourman.2012.08.006
Stylidis D, Shani A, Belhassen Y, 2017. Testing an integrated destination image model across residents and tourists. Tour M anag, 58:184–195. https://doi.org/10.1016/j.tourman.2016.10.014
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):p. 543–555. https://doi.org/10.1177/0047287514522882
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 M a n ag, 46:347–358. https://doi.org/10.1016/j.tourman.2014.07.012
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. https://doi.org/10.1109/ICSSSM.2018.8465084
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. https://doi.org/10.1109/VAST.2016.7883511
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):p. 2758–2772. https://doi.org/10.1109/TVCG.2017.2764459
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. https://doi.org/10.1109/ICDMW.2014.29
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):p. 1306–1319. https://doi.org/10.1016/j.ijinfomgt.2016.02.009
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. https://doi.org/10.1109/VAST.2014.7042496
Zheng XH, Chen W, Wang P, et al., 2016. Big data for social transportation. IEEE Trans Intell Transp Syst, 17(3):p. 620–630. https://doi.org/10.1109/TITS.2015.2480157
Acknowledgements
Authors wish to thank Wei TIAN, Di PENG, and Hao-tian ZHU for their help in the system design. At the same time, we would like to thank Zhi-hai XIE, Fan-zhen LIU, Yu-jia HU, Wen-tao WANG, Xiao-xiao XIONG, Qiu-hui ZHU, Qi-hong GAN, Ruo-yu JIA, and Edou LEOPOLD for improving the paper.
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Jing LIANG designed the research. Meng-qi CAO and Jing LIANG implemented the system. Meng-qi CAO drafted the manuscript. Min ZHU helped organize the manuscript. Meng-qi CAO, Ming-zhao LI, and Zheng-hao ZHOU revised and finalized the paper.
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Meng-qi CAO, Jing LIANG, Ming-zhao LI, Zheng-hao ZHOU, and Min ZHU declare that they have no conflict of interest. Informed consent was obtained from all individual participants included in the user study.
Project supported by the Science & Technology Department of Sichuan Province, China (No. 2018GZ0171) and the Chengdu Science and Technology Bureau, China (No. 2015-HM01-00484-SF)
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Cao, Mq., Liang, J., Li, Mz. et al. TDIVis: visual analysis of tourism destination images. Front Inform Technol Electron Eng 21, 536–557 (2020). https://doi.org/10.1631/FITEE.1900631
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DOI: https://doi.org/10.1631/FITEE.1900631
Key words
- Tourism user-generated content
- Information visualization
- Destination image
- Sentiment visualization
- Sequence visualization