View : 621 Download: 0
Classifying Tourists’ Photos and Exploring Tourism Destination Image Using a Deep Learning Model
- Title
- Classifying Tourists’ Photos and Exploring Tourism Destination Image Using a Deep Learning Model
- Authors
- Cho N.; Kang Y.; Yoon J.; Park S.; Kim J.
- Ewha Authors
- 강영옥
- SCOPUS Author ID
- 강영옥
- Issue Date
- 2022
- Journal Title
- Journal of Quality Assurance in Hospitality and Tourism
- ISSN
- 1528-008X
- Citation
- Journal of Quality Assurance in Hospitality and Tourism
- Keywords
- convolutional neural network; deep learning model; inception -v3 model; tourism destination image; Tourists’ photo classification
- Publisher
- Routledge
- Indexed
- SCOPUS
- Document Type
- Article
- Abstract
- As social network service usage is rapidly surging in our daily life, social network service data plays a crucial role in identifying region of attractions and analyzing tourism destination image. In recent years, the computer vision technology is just beginning to be applied in the tourism field through the transfer learning of a deep learning model. However, the pre-trained models have limitations of properly classifying the photos with the unique landscape or specific elements of the tourism destination. With the purpose of going beyond these limitations, we generated a tourists’ photo classification reflecting regional characteristics and developed a deep learning model to classify photos according to this classification. Through the analysis of 168,216 Flickr photos, we analyzed the tourism destination image of Seoul. Key findings are that (1) tourists prefer to enjoy local food, to visit authentic traditional palaces, and to see inherent cityscape which can be uniquely enjoyed in Seoul, (2) tourist attractive factors differ by region of attractions, (3) tourist preferences differ by continent. This study has novelty in that it develops a tourist’s photo classification suitable for regional characteristics and analyzes tourism destination image by classifying photos using an artificial intelligence technology. © 2022 Taylor & Francis Group, LLC.
- DOI
- 10.1080/1528008X.2021.1995567
- Appears in Collections:
- 사범대학 > 사회과교육과 > Journal papers
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML