This study suggests a big data analyzing method with K-LIWC (Korean-Linguistic Inquiry and Word Count), sentiment and query analysis methods, and investigates public attitudes, positive and negative emotional statements about nuclear energy with the collected data sets of wellknown social media and network services in Korea over time. | Positive or negative? Public perceptions of nuclear energy in South Korea: Evidence from Big Data Nuclear Engineering and Technology 51 (2019) 626e630 Contents lists available at ScienceDirect Nuclear Engineering and Technology journal homepage: Original Article Positive or negative? Public perceptions of nuclear energy in South Korea: Evidence from Big Data Eunil Park* Department of Interaction Science, Sungkyunkwan University, Seoul, Republic of Korea a r t i c l e i n f o a b s t r a c t Article history: After several significant nuclear accidents, public attitudes toward nuclear energy technologies and fa- Received 22 April 2018 cilities are considered to be one of the essential factors in the national energy and electricity policy- Received in revised form making process of several nations that employ nuclear energy as their key energy resource. However, 28 October 2018 it is difficult to explore and capture such an attitude, because the majority of prior studies analyzed Accepted 30 October 2018 Available online 31 October 2018 public attitudes with a limited number of respondents and fragmentary opinion polls. In order to sup- plement this point, this study suggests a big data analyzing method with K-LIWC (Korean-Linguistic Inquiry and Word Count), sentiment and query analysis methods, and investigates public attitudes, Keywords: Nuclear energy positive and negative emotional statements about nuclear energy with the collected data sets of well- Public perception known social media and network services in Korea over time. Results show that several events and Big Data accidents related to nuclear energy have consistent or temporary effects on the attitude and ratios of the K-LIWC statements, depending on the kind of events and accidents. The presented methodology and the use of Query analysis big data in relation to the energy industry is suggested as it can be helpful in .
