Earticle

초록

영어

In this study, social network analysis was performed to compare and analyze changes in domestic tourism trends before and after the outbreak of COVID-19 in a situation where the damage to the tourism industry due to COVID-19 is increasing. Using Textom, a big data analysis service, data were collected using the keywords “travel destination” and “travel trend” based on the collection period of 2019 and 2020, when the epidemic spread to the world and became chaotic. After extracting a total of 80 key words through text mining, centrality was analyzed using NetDraw of Ucinet6, and clustered into 4 groups through CONCOR analysis. Through this, we compared and analyzed changes in domestic tourism trends before and after the outbreak of COVID-19, and it is judged to provide basic data for tourism marketing strategies and tourism product development in the post-COVID-19.

목차

Abstract
1. INTRODUCTION
2. PREVIOUS RESEACH
3. RESEARCH METHOD
3.1 Research project
3.2 Data collection
3.3 Analysis Method
4. RESEARCH RESULTS
4.1 Keyword Frequency and TF-IDF Analysis
4.2 Semantic Network Analysis
4.3 CONCOR Analysis
5. CONCLUSION
REFERENCES

키워드

저자

• Kyoung-mi Yoo [ Professor, Department of Tourism management, Institute of Information Technology, KwangWoon University ]
• Youn-hee Choi [ Ph. D student, Department of Immersive Content Convergence, Graduate School of KwangWoon University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380