PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Wasin Vechgama Co-author(s): Mr. Watcha Sasawattakul, Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, 17th floor, Engineering 4 Building (Charoenvidsavakham), Phayathai Road, Wang Mai, Pathumwan, Bangkok, 10330, Thailand, Email: watcha.sasawattakul@gmail.com.
| Development of Classification Model for Public Perception of Nuclear Energy in Social Media Platform using Machine Learning: Facebook Platform in Thailand |
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| Due to the nuclear consequences of the severe accidents of Nuclear Power Plants (NPPs) of Fukushima Daiichi in 2011, the public acceptance of nuclear energy has been decreased significantly in many countries including Thailand. Since 2011, the Thailand Government had continuously postponed the NPP project until in 2018 the NPP project was not contained in the latest Power Development Plan. Apart from the concerns of the safety of NPPs, public apprehension was the important reason affecting the nuclear energy plan of Thailand. In the past, public acceptance surveys have been conducted by using questionnaires to reflect the people's opinion about nuclear energy in Thailand, especially after the Fukushima disaster. However, the surveys using the questionnaire had the limitation of people access, and the high cost and time consumption. Since, nowadays, the key role of computational code and social media is influential to people around the world significantly including Thailand, data collections from the direct and indirect surveys in various fields have been evaluated through social media platforms. The objective of this study was to develop classification models for public perception of nuclear energy of Thai people in social media platforms using machine learning focusing on the Facebook platform. The comment data from the Facebook Pages having nuclear energy news and information in their posts were extracted by web scraping. Then the extracted data were classified and prepared for proper machine learning model inputs consisting of train data and test data. Train data were used to generate machine learning models to classify the public perception of nuclear energy of Thai people to understand their positive and negative perceptions. The results of generating machine learning models were validated by test data to suggest appropriate models for the public perception of nuclear energy of Thai people. Facebook was selected in the study because now there are Thailand Facebook users with more than 50 million accounts that account for around 70 percent of the total people in Thailand. Besides, machine learning was applied to the study since it allows to generate the specific models to classify the expected data from complex sentences including spoken and written languages in social media platforms. The developed classification model of this study is expected to widely understand the public perception of nuclear energy of Thai people in social platforms in order to provide approaches and activities to increase the public acceptance of nuclear energy in Thailand in the future. |
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Author and Presentation Info
Lead Author Name: Wasin Vechgama (wasinvechgama@gmail.com)
Bio: I graduated Master's Degree in Nuclear Engineering from Chulalongkorn University, Thailand, in 2017. I have worked as a nuclear engineer in the Nuclear Technology Research and Development Center, Thailand Institute of Nuclear Technology for 5 years. Currently, I am a Ph.D. student in Risk Assessment at the Risk Assessment and Management Team, Korea Atomic Energy Research Institute (KAERI School), University of Science and Technology, South Korea.
Country: South Korea
Company: 1) Korea Atomic Energy Research Institute (KAERI School) - University of Science and Technology (South Korea), 2) Thailand Institute of Nuclear Techno
Job Title: Ph.D. Student
Bio: I graduated Master's Degree in Nuclear Engineering from Chulalongkorn University, Thailand, in 2017. I have worked as a nuclear engineer in the Nuclear Technology Research and Development Center, Thailand Institute of Nuclear Technology for 5 years. Currently, I am a Ph.D. student in Risk Assessment at the Risk Assessment and Management Team, Korea Atomic Energy Research Institute (KAERI School), University of Science and Technology, South Korea.
Country: South Korea
Company: 1) Korea Atomic Energy Research Institute (KAERI School) - University of Science and Technology (South Korea), 2) Thailand Institute of Nuclear Techno
Job Title: Ph.D. Student