[Purpose] This study aimed to examine the direction of domestic research through text analysis using a qualitative method rather than an empirical study in the name of ‘public disclosure’, targeting domestic research that is mostly empirical research. [Methodology] This study conducted frequency analysis on the research sample, focusing on keywords from a total of 872 accounting-related public disclosure studies, and conducted four types of centrality and network analysis based on the frequency analysis. For this analysis, R and Python programs were used. [Findings] The results of this study are as follows. First, among a total of 872 domestic studies, the keywords with the highest frequency were accounting, profit, and company in that order. Second, as a result of the Degree centrality analysis, the key keywords were profit (0.0736), company (0.0664), and information (0.0564). Third, as a result of Closeness centrality analysis, the keywords showing connectivity that emphasized the distance between one node and all nodes in the entire network were profit (0.3468), company (0.3392), and information (0.3349). Fourth, as a result of Betweenness centrality analysis, the keywords shown to be most helpful in building a network of other nodes are profit (0.1499), company (0.1172), accounting (0.0990), and information (0.0842). Fifth, as a result of the Eigenvector centrality analysis, as an indicator that reflects the centrality of nodes in the weight, the keywords with the highest centrality were accounting (0.6136), international accounting standards (0.5371), and standards (0.3988). [Implications] Through this study, which is not an empirical study but a qualitative analysis using a text mining method, it is expected that later researchers will have an easier time analyzing research trends in previous studies, and furthermore, it is expected that further expansion of research topics will be possible.
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