Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter January 13, 2022

Automatic sentiment analysis of public opinion on nuclear energy

Hong Xu, Tao Tang, Baorui Zhang and Yuechan Liu
From the journal Kerntechnik


Opinion mining and sentiment analysis based on social media has been developed these years, especially with the popularity of social media and the development of machine learning. But in the community of nuclear engineering and technology, sentiment analysis is seldom studied, let alone the automatic analysis by using machine learning algorithms. This work concentrates on the public sentiment mining of nuclear energy in German-speaking countries based on the public comments of nuclear news in social media by using the automatic methodology, since compared with the news itself, the comments are closer to the public real opinions. The results showed that majority comments kept in neutral sentiment. 23% of comments were in positive tones, which were approximate 4 times those in negative tones. The concerning issues of the public are the innovative technology development, safety, nuclear waste, accidents and the cost of nuclear power. Decision tree, random forest and long short-term memory networks (LSTM) are adopted for the automatic sentiment analysis. The results show that all of the proposed methods can be applied in practice to some extent. But as a deep learning algorithm, LSTM gets the highest accuracy approximately 85.6% with also the best robustness of all.

Corresponding author: Hong Xu, Energy technology R&D division, Jinyuyun Energy Technology Co., Ltd., Chongqing, China, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Allam, T.M., Abdullkader, H.M., and Sallam, A.A. (2014). Managed N-gram language model based on hadoop framework and a hbase tables. In: The 9th international conference on INFOrmatics and systems (INFOS2014). Cairo, Egypt: Parallel and Distributed Computing Track, PDC-58, pp. 15–17.Search in Google Scholar

Allen, D.E. and McAleer, M. (2018). President Trump tweets supreme leader Kim Jong-Un on nuclear weapons: a comparison with climate change. Sustainability 10: 2310, in Google Scholar

Ansari, A.F., Seenivasan, A., Anandan, A., and Lakshmanan, R. (2017). Twitter sentiment analysis, Available at: in Google Scholar

Brouard, S. and Guinaudeau, I. (2015). Policy beyond politics? Public opinion, party politics and the French pro-nuclear energy policy. J. Publ. Pol. 35: 137–170, in Google Scholar

Cao, Z.Z. and Ye, C.M. (2021). Application of improved CNN-LSTM model in fault diagnosis of rolling bearings. Comput. Syst. Appl. 30: 126–133, in Google Scholar

Du, Q. and Han, Z. (2020). The framing of nuclear energy in Chinese media discourse: a comparison between national and local newspapers. J. Clean. Prod. 245: 118695, in Google Scholar

Hackeling, G. (2017). Mastering machine learning with Scikit-learn. Birmingham, UK: Packt Publishing.Search in Google Scholar

Hasegawa, S., Suzuki, T., Yagahara, A., and Kanda, R. (2020). Changing emotions about Fukushima related to the Fukushima nuclear power station accident—how rumors determined people’s attitudes: social media sentiment analysis. J. Med. Internet Res. 22: e18662, in Google Scholar

Hassard, H.A., Swee, J.K.Y., Ghanem, M., and Unesaki, H. (2013). Assessing the impact of the Fukushima nuclear disaster on policy dynamics and the public sphere. In: Procedia environmental sciences 17 (The 3rd international conference on sustainable future for human security SUSTAIN 2012), pp. 566–575.Search in Google Scholar

Irajzad, F., Kafi, M., and Shahriari, H. (2017). A rhetorical analysis of English and Persian online comments on the news articles related to Iran’s nuclear issue. Obs. J. 11: 95–110, in Google Scholar

Jeong, S.Y., Kim, J.W., Kim, Y.S., Joo, H.Y., and Moon, J.H. (2020). Sentiment analysis of nuclear energy-related articles and their comments on a portal site in Rep. of Korea in 2010–2019. Nucl. Eng. Technol. 53: 1013–1019, in Google Scholar

Khatua, A., Cambria, E., Ho, S.S., and Na, J.C. (2020). Deciphering public opinion of nuclear energy on twitter. In: 2020 International joint Conference on neural networks (IJCNN). Glasgow, UK, 19–24 July, pp. 1–8.Search in Google Scholar

Kim, D.S. and Kim, J.W. (2014a). Public opinion mining on social media: a case study of twitter opinion on nuclear power. Adv. Sci. Technol. Lett. 51 (CES-CUBE 2014): 224–228, in Google Scholar

Kim, D.S. and Kim, J.W. (2014b). Public opinion sensing and trend analysis on social media: a study on nuclear power on Twitter. Int. J. Multimedia Ubiquitous Eng. 9: 373–384, in Google Scholar

Koopmans, R. and Duyvendak, J.W. (1995). The political construction of the nuclear energy issue and its impact on the mobilization of anti-nuclear movements in Western Europe. Soc. Probl. 42: 235–251, in Google Scholar

Liang, J., Lou, J., and Siegel, J.E. (2021). Public awareness on nuclear energy development in China: evidence from online discussions on Zhihu (February 9, 2021), in Google Scholar

Ma, Y., Peng, H., Khan, T., Cambria, E., and Hussain, A. (2018). Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis. Cognit. Comput. 10: 639–650, in Google Scholar

Mathan, K., Kumar, P.M., Panchatcharam, P., Manogaran, G., and Varadharajan, R. (2018). A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Des. Autom. Embed. Syst. 22: 225–242, in Google Scholar

Pal, M. (2005). Random forest classifier for remote sensing classification. Int. J. Rem. Sens. 26: 217–222, in Google Scholar

Park, E. (2018). Positive or negative? Public perceptions of nuclear energy in South Korea: evidence from big data. Nucl. Eng. Technol. 51: 626–630, in Google Scholar

Rehman, A.U., Malik, A.K., Raza, B., and Ali, W. (2019). A hybrid CNN-LSTM model for improving accuracy of movie reviews sentiment analysis. Multimed. Tool. Appl. 78: 26597–26613, in Google Scholar

Roh, S. (2017). Big data analysis of public acceptance of nuclear power in Korea. Nucl. Eng. Technol. 49: 850–854, in Google Scholar

Rosa, E.A. and Dunlap, R.E. (1994). The polls—poll trends: nuclear power: three decades of public opinion. Publ. Opin. Q. 58: 295–325, in Google Scholar

Safavian, S.R. and Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21: 660–674, in Google Scholar

Satapathy, R., Chaturvedi, I., Cambria, E., Ho, S.S., and Na, J.C. (2017). Subjectivity detection in nuclear energy tweets. Comput. Sist. 21: 657–664, in Google Scholar

Schmidhuber, J. (2015). Deep learning in neural networks: an overview. Neural Netw. 61: 85–117, in Google Scholar

Teirilä, J. (2020). The value of the nuclear power plant fleet in the German power market under the expansion of fluctuating renewables. Energy Pol. 136: 111054, in Google Scholar

Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., and Kappas, A. (2010). Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61: 2544–2558, in Google Scholar

Tripathy, A., Agrawal, A., and Rath, S.K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Syst. Appl. 57: 117–126, in Google Scholar

Valdes, G., Luna, J.M., Eaton, E., Simone, C.B.II, Ungar, L.H., and Solberg, T.D. (2016). MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine. Sci. Rep. 6: 37854, in Google Scholar

Wang, T., Qin, Z., Jin, Z., and Zhang, S. (2010). Handling over-fitting in test cost-sensitive decision tree learning by feature selection, smoothing and pruning. J. Syst. Softw. 83: 1137–1147, in Google Scholar

Wang, S., Ren, W., Zhang, Y., and Liang, F. (2019). Random forest classifier for distributed multi-plant order allocation. In: Huang, G., Chien, C.F., and Dou, R. (Eds.), Proceeding of the 24th international conference on industrial engineering and engineering management 2018. Singapore: Springer.Search in Google Scholar

Wang, W., Li, B., Feng, D., Zhang, A., and Wan, S. (2020). The OL-DAWE model: tweet polarity sentiment analysis with data augmentation. IEEE Access 8: 40118–40128, in Google Scholar

Yu, Z., Yang, K., Luo, Y., and Shang, C. (2019). Spatial-temporal process simulation and prediction of chlorophyll-a concentration in dianchi lake based on wavelet analysis and long-short term memory network. J. Hydrol. 582: 124488, in Google Scholar

Zhao, J. and Gui, X. (2017). Comparison research on text pre-processing methods on twitter sentiment analysis. IEEE Access 5: 2870–2879, in Google Scholar

Received: 2021-05-09
Published Online: 2022-01-13
Published in Print: 2022-04-26

© 2021 Walter de Gruyter GmbH, Berlin/Boston