Full Text:   <2002>

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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: 4934

Citations:  Bibtex RefMan EndNote GB/T7714


Meng-qi Cao


Ming-zhao Li


Min Zhu


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


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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DOI - 10.1631/FITEE.1900631

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.





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


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