We have developed the Korean news sentiment index (NSI) to gauge the economic sentiment of Korea on a daily basis by analyzing news texts gathered from the Internet. Our framework utilizes cutting-edge natural language processing techniques to compute the NSI and examine keywords, offering insights into its fluctuations. We designed a sentiment classifier using transformer neural networks that effectively process extensive news samples to compute the NSI of Korea. We compute the NSI more frequently and immediately than official indices that rely on monthly surveys. Through this, we can identify changes in economic sentiment before official statistics are released. Moreover, the proposed framework offers keyword analysis and sector indices to clarify why economic sentiments fluctuate. Our comprehensive assessments demonstrate that the NSI is a valuable leading index and an essential tool for identifying inflection points in economic sentiment.