빅데이터를 활용한 ‘한국어교육’ 키워드 분석

Abstract

This study aims to focus on the keyword `Korean Language Education', utilizing big data from NAVER, Daum, and Google amidst the growing trend of using big data in various fields. The aim is to uncover important trends and patterns related to Korean language education by analyzing news data from the past 10 years. For the research, TEXTOM, a text mining tool, is employed to analyze the text data. The study is divided into two periods: the first half (first five years) and the second half (last five years). In each period, word frequency analysis, matrix analysis, network analysis, and sentiment analysis are conducted to observe common trends and differences between the two periods. This approach aims to elucidate the trends in Korean language education and the utilization of big data in this field. The results reveal that during the first period, discussions on Korean language education were frequently derived from significant social issues. However, in the second period, there was an increasing trend in discussions about education using internet platforms. Sentiment analysis of words related to Korean language education shows that, in both periods, the proportion of positive sentiment intensity was significantly higher than that of negative sentiment intensity. This indicates that Korean language education continues to be evaluated positively. The findings of this study are expected to provide new insights into the field of Korean language education and suggest directions for future progress.