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Youtube Mukbang and Online Delivery Orders: Analysis of Impacts and Predictive Model
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유튜브 먹방과 온라인 배달 주문: 영향력 분석과 예측 모형

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Type
Academic journal
Author
Sarah Choi (한양대학교) Sang-Yong Tom Lee (한양대학교)
Journal
Korea Intelligent Information Systems Society Journal of Intelligence and Information Systems Vol.28 No.4 KCI Accredited Journals
Published
2022.12
Pages
119 - 133 (15page)

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Youtube Mukbang and Online Delivery Orders: Analysis of Impacts and Predictive Model
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Research history (3)

2022

Youtube Mukbang and Online Delivery Orders: Analysis of Impacts and Predictive Model
Sa ,  ah Choi ,  Sang-Yong Tom Lee Journal of Intelligence and Information Systems 2022.12 Academic journal

2021

배달이용건수 예측 모델 개발을 위한 유튜브 ‘먹방’ 댓글 데이터 활용
최사라 ,  이상용 한국지능정보시스템학회 학술대회논문집 2021.06 Proceeding
The prediction of food delivery based on Youtube "Meockbang" comments
최사라 경영 2021.01 Thesis
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Abstract· Keywords

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One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.
#온라인 배달 주문 #유튜브 먹방 #감성 분석 #머신 러닝 #예측 모형 #Online Delivery #Youtube Mukbang #Sentiment Analysis #Machine learning #Predictive model

Contents

1. 서론
2. 선행연구
3. 연구 방법
4. 분석 결과
5. 결론
참고문헌(References)
Abstract

References (23)

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권재영, 김시내, 박은지, & 송종우. (2015). 국내 배달음식 이용 건수 분석 및 예측. 응용통계연구, 28(5), 977-990. dbpia 권태용, 곽재영, 신주일, 양다영, 여현구, 이영재, & 윤상후. (2018). 기상자료와 기계학습을 통한 배달주문 수요예측. 한국기상학회 학술대회 논문집, 540-540. dbpia 김경재, 한인구, & 안현철. (2005). Support Vector Machine 을 이용한 고객구매예측모형. 지능정보연구, 11(3), 69-81. google schola 김대룡, 김다영, & 변수지. (2016). 날씨에 따른 배달음식 주문건수 예측. 한국기상학회 학술대회 논문집, 480-481. dbpia 김영민, 정석재, & 이석준. (2014). 소셜 미디어 감성분석을 통한 주가 등락 예측에 관한 연구. Entrue Journal of Information Technology, 13(3), 59-70. dbpia

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