Exploring Tourism Activities of Tourists and Residents through Convolutional Neural Network-based SNS Photo Classification

Youngok Kang; Nahye Cho; Soyeon Park; Jiyeon Kim

doi:10.22776/kgs.2021.56.3.247

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2021 Vol.56, Issue 3 Next Page

Research Article

Exploring Tourism Activities of Tourists and Residents through Convolutional Neural Network-based SNS Photo Classification 합성곱신경망을 활용한 SNS 사진 분류 및 관광객과 거주자의 관광 활동 특성 분석

30 June 2021. pp. 247-264

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Abstract

In the tourism field, Social Network Service (SNS) data is valuable as a major source in grasping the image that users feel about tourist destinations. As the importance of SNS data in the tourism field is emerging, a number of studies have been conducted to identify major destinations or to analyze images and emotions of tourist destinations using text information posted on SNS. However, a few researches have been performed to analyze the content of photos posted on SNS. This study aims to analyze the characteristics of tourists and residents’ tourism activities by analyzing photos posted on Flickr, a photo-sharing SNS. In this study, after crawling 147,669 photos posted in the Seoul area from 2013 to 2018, Flickr users were classified as tourists and residents. And then photos were classified using a Convolutional Neural Networks(CNN) model trained in accordance with the photo classification system for the purpose of tourism. As a result of photo classification, it was confirmed that there are differences in major places to visit and preferred tourism activities between tourists and residents. Tourists' region of attractions(ROA) were concentrated in the city center, but residents' ROAs were expanded to the Gangnam area in addition to the city center and showed a more dispersed form. It was found that tourists enjoyed street food in Myeong-dong, Insa-dong, DDP, etc. shopping in traditional markets such as Gwangjang Market and Namdaemun Market, experiencing traditional hanbok in Gyeongbokgung Palace and Namdaemun Market, changing of the guards’ ceremony in City Hall, trick art in Ihwa Mural Village and Hongdae while residents visited a lot more various places than tourists in the 'Cityscape' and 'Food & Restaurant' categories. This study has significance in that it is the first approach to analyze the tourism activities of tourists and residents with the photos posed on Location-based Social Network Service. In addition, this study is meaningful in that it showed a method of analyzing photo data of a location-based social network by applying a deep learning model in view of academics and it proposed how SNS image data can be used for tourism marketing by classifying a large volume of data quickly and systematically in view of practices.

Keywords

Flickr

Location Based Social Network Service(LBSN)

Deep learning

Convolutional Neural Networks

Artificial Intelligence

Tourism

관광 분야에서 소셜네트워크서비스(SNS)는 사용자들이 관광지에 대해 느끼는 이미지를 파악하는데 주요 자료로 가치를 갖는다. 관광 분야에서 SNS 데이터의 중요성을 인식하면서 주요 방문지 분석이나 SNS에 게시된 텍스트 정보를 활용하여 관광지에 대한 이미지나 감성을 분석하는 연구는 있었지만, SNS에 게시된 사진의 콘텐츠를 분석하는 연구는 이뤄지지 못했다. 본 연구의 목적은 사진 공유 SNS인 플리커에 게시된 사진을 분석하여 관광객과 거주자의 관광 활동 특성을 분석하는 것이다. 이를 위해 2013년부터 2018년까지 서울지역에 게시된 사진 147,669장을 크롤링한 후, 플리커 사용자를 관광객과 거주자로 분류하고, 관광목적 사진 분류체계에 맞춰 훈련된 합성곱신경망 모델을 사용하여 사진을 분류하였다. 사진 분류결과 관광객과 거주자 간 주요 방문지 및 선호하는 관광 활동에 차이가 있음을 확인하였다. 관광객의 주요 방문지는 도심에 집중되었지만, 거주자는 도심뿐 아니라 강남지역까지 확대되어 있고 더 분산된 형태를 보였으며, 관광객은 명동, 인사동, DDP 등에서의 길거리 음식, 광장시장 및 남대문시장 등 재래시장에서의 쇼핑, 경복궁과 남대문시장에서의 전통한복체험, 시청에서의 수문장 교대식, 이화 벽화 마을과 홍대에서의 트릭아트를 선호함을 알 수 있었으며, 거주자의 경우는 ‘도시경관’ 및 ‘음식 및 식당’ 카테고리에서 관광객보다 훨씬 다양한 곳을 방문하는 특징이 있음을 알 수 있었다. 본 연구는 학문적으로는 위치기반 SNS의 사진 데이터에 딥러닝 모델을 적용하여 체계적으로 분석하는 방법을 제시하였다는 점에 의의가 있으며, 실용적 측면에서는 SNS 이미지 데이터가 관광마케팅에 어떻게 활용될 수 있는지를 구체적으로 제시하였다는 점에 의의가 있다.

키워드

플리커

위치기반 소셜네트워크서비스

딥러닝

합성곱신경망

인공 지능

관광

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Information

Publisher :The Korean Geographical Society
Publisher(Ko) :대한지리학회
Journal Title :Journal of the Korean Geographical Society
Journal Title(Ko) :대한지리학회지
Volume : 56
No :3
Pages :247-264
Received Date : 2021-04-05
Revised Date : 2021-05-14
Accepted Date : 2021-05-18
DOI :https://doi.org/10.22776/kgs.2021.56.3.247

Journal of the Korean Geographical Society ISSN:1225-6633(Print) 대한지리학회지

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