Development and Application of a Big Data Analysis-Based Procedure to Identify Concerns about Renewable Energy

To achieve carbon neutrality by 2050, Korea has been expanding its investment in renewal energy distribution and technology development. However, with this rapid expansion of renewable energy, public concern about it has grown. This study developed and used a big data analysis-based procedure to analyze the questions registered on Naver, the largest portal site in Korea, from 2008 to 2020 to identify public concern over renewable energy. The big data analysis-based procedure consisted of two steps. The first was a frequency analysis to identify the most frequently registered words. The second was to classify questions using term frequency-inverse document frequency (TF-IDF) weight and cosine similarity based on word2vec. The analysis revealed the most frequently registered words related to renewable energy, such as “solar power,” “power generation,” “energy,” and “wind power.” It also revealed the most frequently registered questions, such as those related to solar panel installation, renewable energy generation methods, and certificates. To continue expanding renewable energy, it is becoming increasingly important to understand the public’s concerns and create a method to resolve their objections to renewable energy. It is expected that the procedure in this study may provide relevant insight for the method.