빅데이터를 활용한 코로나 1 9 시대의 창업·인성 관련 신문기사 분석 및 시사점 탐색

This study aims to explore the main implications of start-up and personality in the era of COVID-19 through the analysis of newspaper articles related to start-up and personalities using big data from January 2020 to March 2023. For this purpose, 844 articles were collected through the Korea Press Foundation’s BIGKINDS platform, and key word analysis was conducted using TEXTOM, a text analysis platform. In addition, Ucinet6 was used to analyze the semantic connection network of key words. The results of the study showed that, first, education, university, student, support, business, and startup (6th place) were the most common words in the word analysis. These were issues related to the operation of university education and support programs for students in relation to start-up and personality, and issues related to start-up and employment. In addition, the TF-IDF analysis of start-up and personality showed the following order: selection, university, student, major, and employment. These were issues related to university students’ schools, departments, majors, employment, education, and human resources related to start-up and personality. Second, the frequency-based semantic network clustering analysis revealed the categories of ‘start-up environment’, ‘start-up talent training’, ‘start-up program’, and ‘start-up technology’. In conclusion, it suggests that despite the sudden collapse of the start-up ecosystem due to COVID-19, there have been multifaceted efforts to support start-up education, strengthen future talent training, and create a start-up environment.