Nuclear Engineering and Technology

  • 제55권6호
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  • Pages.2026-2033
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  • 2023
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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2009-2022 Thailand public perception analysis of nuclear energy on social media using deep transfer learning technique

  • Wasin Vechgama (Thailand Institute of Nuclear Technology (Public Organization)) ;
  • Watcha Sasawattakul (Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University) ;
  • Kampanart Silva (National Energy Technology Center, National Science and Technology Development Agency)
  • 투고 : 2023.02.01
  • 심사 : 2023.03.27
  • 발행 : 2023.06.25
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초록

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

키워드

과제정보

This work was mainly supported by Thailand Science Research and Innovation (TSRI) and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT: Ministry of Science and ICT) (No. RS-2022-00143695). Moreover, the authors must thank the opportunity from the Ministry of Higher Education, Science, Research and Innovation (MHESI), Thailand, Korea Atomic Energy Research Institute (KAERI), and University of Science and Technology (UST) for time resources and support in period of the publication process.

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