CLC number: TP399
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-03-06
Cited: 0
Clicked: 5793
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0001-6693-6870
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.
@article{title="TDIVis: visual analysis of tourism destination images",
author="Meng-qi Cao, Jing Liang, Ming-zhao Li, Zheng-hao Zhou, Min Zhu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="4",
pages="536-557",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900631"
}
%0 Journal Article
%T TDIVis: visual analysis of tourism destination images
%A Meng-qi Cao
%A Jing Liang
%A Ming-zhao Li
%A Zheng-hao Zhou
%A Min Zhu
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 4
%P 536-557
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900631
TY - JOUR
T1 - TDIVis: visual analysis of tourism destination images
A1 - Meng-qi Cao
A1 - Jing Liang
A1 - Ming-zhao Li
A1 - Zheng-hao Zhou
A1 - Min Zhu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 4
SP - 536
EP - 557
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900631
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.
[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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