TY - JOUR
T1 - Classifying Tourists’ Photos and Exploring Tourism Destination Image Using a Deep Learning Model
AU - Cho, Nahye
AU - Kang, Youngok
AU - Yoon, Jiyoung
AU - Park, Soyeon
AU - Kim, Jiyeon
N1 - Funding Information:
This work was supported by the Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government [20CTAP-C151886-02].
Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Tourists’ photo classification
KW - convolutional neural network
KW - deep learning model
KW - inception -v3 model
KW - tourism destination image
UR - http://www.scopus.com/inward/record.url?scp=85125067760&partnerID=8YFLogxK
U2 - 10.1080/1528008X.2021.1995567
DO - 10.1080/1528008X.2021.1995567
M3 - Article
AN - SCOPUS:85125067760
SN - 1528-008X
VL - 23
SP - 1480
EP - 1508
JO - Journal of Quality Assurance in Hospitality and Tourism
JF - Journal of Quality Assurance in Hospitality and Tourism
IS - 6
ER -