Automatic sentiment analysis of public opinion on nuclear energy
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Hong Xu
,
Tao Tang
Abstract
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.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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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.
References
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. 15–17.10.1109/INFOS.2014.7036678Suche 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, https://doi.org/10.3390/su10072310.Suche in Google Scholar
Ansari, A.F., Seenivasan, A., Anandan, A., and Lakshmanan, R. (2017). Twitter sentiment analysis, Available at: https://github.com/abdulfatir/twitter-sentiment-analysis/blob/master/docs/report.pdf.Suche 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, https://doi.org/10.1017/S0143814X14000221.Suche 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, https://doi.org/10.15888/j.cnki.csa.007830.Suche 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, https://doi.org/10.1016/j.jclepro.2019.118695.Suche in Google Scholar
Hackeling, G. (2017). Mastering machine learning with Scikit-learn. Birmingham, UK: Packt Publishing.Suche 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, https://doi.org/10.2196/18662.Suche in Google Scholar PubMed PubMed Central
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. 566–575.10.1016/j.proenv.2013.02.072Suche 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, https://doi.org/10.15847/obsOBS1102017882.Suche 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, https://doi.org/10.1016/j.net.2020.07.031.Suche 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. 1–8.10.1109/IJCNN48605.2020.9206903Suche 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, https://doi.org/10.14257/astl.2014.51.51.Suche 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, https://doi.org/10.14257/ijmue.2014.9.11.36.Suche 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, https://doi.org/10.2307/3096903.Suche 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), https://doi.org/10.2139/ssrn.3782826.Suche 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, https://doi.org/10.1007/s12559-018-9549-x.Suche 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, https://doi.org/10.1007/s10617-018-9205-4.Suche in Google Scholar
Pal, M. (2005). Random forest classifier for remote sensing classification. Int. J. Rem. Sens. 26: 217–222, https://doi.org/10.1080/01431160412331269698.Suche 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, https://doi.org/10.1016/j.net.2018.10.025.Suche 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, https://doi.org/10.1007/s11042-019-07788-7.Suche in Google Scholar
Roh, S. (2017). Big data analysis of public acceptance of nuclear power in Korea. Nucl. Eng. Technol. 49: 850–854, https://doi.org/10.1016/j.net.2016.12.015.Suche 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, https://doi.org/10.1086/269425.Suche 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, https://doi.org/10.1109/21.97458.Suche 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, https://doi.org/10.13053/cys-21-4-2783.Suche in Google Scholar
Schmidhuber, J. (2015). Deep learning in neural networks: an overview. Neural Netw. 61: 85–117, https://doi.org/10.1016/j.neunet.2014.09.003.Suche in Google Scholar PubMed
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, https://doi.org/10.1016/j.enpol.2019.111054.Suche 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, https://doi.org/10.1002/asi.21416.Suche 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, https://doi.org/10.1016/j.eswa.2016.03.028.Suche 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, https://doi.org/10.1038/srep37854.Suche in Google Scholar PubMed PubMed Central
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, https://doi.org/10.1016/j.jss.2010.01.002.Suche 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.10.1007/978-981-13-3402-3_14Suche 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, https://doi.org/10.1109/ACCESS.2020.2976196.Suche 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, https://doi.org/10.1016/j.jhydrol.2019.124488.Suche 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, https://doi.org/10.1109/ACCESS.2017.2672677.Suche in Google Scholar
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Artikel in diesem Heft
- Frontmatter
- Study of a perforated hollow cylinder and twisted tape inserts as a compound device in a circular tube for heat transfer enhancement
- Pool boiling performance of oxide nanofluid on a downward-facing heating surface
- The CANDLE burnup strategy applied to small modular pressurized water reactor loading with fully ceramic microencapsulated fuel
- Automatic sentiment analysis of public opinion on nuclear energy
- One-dimensional and three-dimensional coupling simulation research of centrifugal cascade hydraulics
- Analysis of the anticipated transient without scram (ATWS) initiated by emergency power mode through the full scope simulator
- Application of radio analytical tracer technique to study the performance of industrial grade ion exchange resin exposed to UV radiations
- Radioactive waste treatment technology: a review
- Positron annihilation lifetime spectroscopy of annealed tungsten
- Simulation of cobalt-60 production in research reactors using OpenMC Monte Carlo code
- Half-space albedo problem for the Anlı-Güngör scattering function
- Calendar of events
Artikel in diesem Heft
- Frontmatter
- Study of a perforated hollow cylinder and twisted tape inserts as a compound device in a circular tube for heat transfer enhancement
- Pool boiling performance of oxide nanofluid on a downward-facing heating surface
- The CANDLE burnup strategy applied to small modular pressurized water reactor loading with fully ceramic microencapsulated fuel
- Automatic sentiment analysis of public opinion on nuclear energy
- One-dimensional and three-dimensional coupling simulation research of centrifugal cascade hydraulics
- Analysis of the anticipated transient without scram (ATWS) initiated by emergency power mode through the full scope simulator
- Application of radio analytical tracer technique to study the performance of industrial grade ion exchange resin exposed to UV radiations
- Radioactive waste treatment technology: a review
- Positron annihilation lifetime spectroscopy of annealed tungsten
- Simulation of cobalt-60 production in research reactors using OpenMC Monte Carlo code
- Half-space albedo problem for the Anlı-Güngör scattering function
- Calendar of events
