빅데이터 분석방법론을 활용한 코로나19 전･후 주거스트레스 관련 키워드 변화 - 2018~2022년 신문 기사를 중심으로 -

초록

The purpose of this study was to explore keyword changes related to housing stress before and after the outbreak of COVID-19 using big data analysis methodology. Newspaper articles for 4 years before and after COVID-19 were collected in relation to housing stress from Naver News platform, and 27,898 keywords were extracted. The collected data was refined using R4.2.2 program and analyzed using vocabulary usage frequency analysis, Odd ratio analysis, Phi analysis, Semantic Network Analysis, and Topic Modeling. Major findings are as follows. 1) After COVID-19, the frequency of occurrence of the keyword ‘residential environment’ was the highest, and ‘housing complex’ was the most important keyword in the semantic network results. 2) The keyword ‘bathroom’ was very highly appeared after COVID-19, reflecting the increased interest in hygiene, health, and safety. 3) As remote education, telecommuting, and care within a dwelling have expanded due to COVID-19, the need for more space and flexible housing planning to accommodate them has been highlighted. 4) While COVID-19 has made daily life more difficult for the vulnerable, it has been confirmed that single-parent households were particularly stressed about housing. 5) The housing stress of young people continued to be at a high level before and after COVID-19. 6) It was confirmed that the increase in housing cost was the main factor causing housing stress.

키워드