This study aims to derive policy implications by examining environmental, social, and governance (ESG) issues in the public sector through text mining. According to the results of this study, the most frequent words based on the TF-IDF system are management, social, sustainability, public, ESG, and disclosure. Furthermore, a CONCOR hierarchical clustering analysis identified four clusters: social structure, external reporting, capacity building, and financial budgeting. Based on these results, this study provides four suggestions. First, in building policies for ESG in the public sector, each public institution should establish ESG strategies that link to its own characteristics. Second, public institutions need to make additional efforts to increase ESG information disclosure reliability by conducting third-party verification and evaluation. Third, ESG programs can be applied in collaboration with other sectors. Lastly, public institutions should strategically manage their funds in accordance with the principles and values of fund management.

□ Key Words: ESG, Big Data, Text Mining, Public Sector, Issue Trend