| 텍스트 마이닝 기법을 활용한 국내 코로나19 유행 시기별 신재생에너지 뉴스 기사 분석 | |
| Analysis of News Articles on Renewable Energy During COVID-19 Epidemic Periods Using Text Mining Techniques | |
| 김영선*† ․ 이승언** ․ 최경석** | |
| Kim, Youngsun*† ･ Lee, Seung-Eon** and Choi, Gyeong-Seok** | |
| This study analyzed news articles related to renewable energy, which shows different patterns from most energy sources, focusing on a decrease in supply and demand during the COVID-19 pandemic. A total of 20,602 news articles containing both renewable energy and COVID-19 keywords were collected in Python 3.9.6 from December 31, 2019, when the first case of COVID-19 was confirmed, to April 30, 2022, which included the period after social distancing was lifted. These articles were analyzed by text mining techniques to derive the flow and change of issues related to renewable energy. The collected news articles were refined with TEXTOM 6.0 and then the top 30 most frequently used words were extracted to understand the flow of issues on renewable energy. The significance of these words was evaluated through CONCOR analysis in order to determine the changes and characteristics of renewable energy issues according to the COVID-19 pandemic period. The results of this study can be used as a basis for establishing policies or developing business plans by identifying major issues related to renewable energy for situations related to the ongoing COVID-19 pandemic. | |
| Renewable Energy, COVID-19, News Articles, Text Mining | |
 1306-10.pdf | |
| 2093-5919 | |
| 2586-2782 | |
| 2022-12 |
