Analysis of Domestic Digital and Energy Policy Trends Using Text Mining method

Abstract

Objectives The objective of this study is to understand digitalization and energy conversion trends and suggest future directions using text-mining-based analysis. Methods From 2015 to 2021, published domestic research reports and policy briefings related to digitalization and energy from government departments and major media outlets were analyzed using text-mining techniques. Frequency, time-series, and association analyses were conducted to understand current trends and patterns. Results and Discussion Frequency analysis of reports and articles published for the 2015-2021 period found that the most common keywords were, in descending order, ‘education’, ‘finance’, ‘hydrogen’, and ‘solar power’. This indicates that the core technologies of the fourth industrial revolution have been employed in various fields, with a specific focus on renewable energy for carbon neutrality. Time-series analysis confirmed that the direction of government policy has changed, and it was found that digital and energy conversion was accelerating before and after the outbreak of COVID-19 and the Korean version of the New Deal policies. Association analysis revealed that government policies associated with fourth industrial revolution technologies have been established in various fields and the commercialization of renewable energy has been active. Conclusion Analyzing domestic policy directions using text mining revealed an association between the fourth industrial revolution and carbon neutrality. Text mining techniques can be used to more effectively understanding of domestic policy trends, and it is expected that they will apply a wide variety of fields that can utilize them in the future.

Keywords

• text mining
• the fourth industrial revolution
• frequency analysis
• time series analysis
• association analysis