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: 5489
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.
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