Clustering Travelers’ Lifestyle Destination Image from Five Asian Traveler-Generated Content

Ping-Heng Tsai (), Chia-Chi Hsaio, Yan-Ru Li and Chun-Chieh Lin
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Ping-Heng Tsai: Department of Finance, Chihlee University of Technology, New Taipei City 220, Taiwan
Chia-Chi Hsaio: Department of Information Management, Yuan-Ze University, Taoyuan City 320, Taiwan
Yan-Ru Li: Department of Digital and Humanities, Aletheia University, New Taipei City 251, Taiwan
Chun-Chieh Lin: Department of Information Management, National Taipei University of Business, Taipei 100, Taiwan

Sustainability, 2023, vol. 15, issue 7, 1-18

Abstract: This study examines the destination image and lifestyle experience via traveler-generated comments. To understand the travelers’ behavior, we first established a crawler, which helps us to gather the travelers’ comments from tourism social media. After conducting a content analysis, text mining, and factor analysis of a sampling of 23,019 travelers’ comments, this study found that travelers based on their activities and experiences constructed their image. Additionally, we also found that the travelers’ emotions and impressions showed up with their images. From the result of factor analysis, we extract the 13 clustering results and perform the one-way ANOVA with Scheffe’s method to compare the difference among each group. Finally, we used the related sentences to draw a relation map to explain the inner difference between travelers. This study’s results suggest that traveler-generated comments can be especially useful for destination image analysis and market segments in tourism marketing and management. This study also highlights the importance of understanding destination image and marketing segment from the travelers’ comments and challenges for those in tourism marketing to narrow the gap.

Keywords: traveler-generated content; destination image formation; correspondence analysis; text mining; factor analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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