{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:32:42Z","timestamp":1768343562593,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["71874022"],"award-info":[{"award-number":["71874022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>With the rapid development of social network platforms, Sina Weibo has become the main carrier for modern netizens to express public views and emotions. How to obtain the tendency of public opinion and analyze the text\u2019s emotion more accurately and reasonably has become one of the main challenges for the government to monitor public opinion in the future. Due to the sparseness of Weibo text data and the complex semantics of Chinese, this paper proposes an emotion analysis model based on the Bidirectional Encoder Representation from Transformers pre-training model (BERT), Fast Gradient Method (FGM) and the bidirectional Gated Recurrent Unit (BiGRU), namely BERT-FGM-BiGRU model. Aiming to solve the problem of text polysemy and improve the extraction effect and classification ability of text features, this paper adopts the BERT pre-training model for word vector representation and BiGRU for text feature extraction. In order to improve the generalization ability of the model, this paper uses the FGM adversarial training algorithm to perturb the data. Therefore, a BERT-FGM-BiGRU model is constructed with the goal of sentiment analysis. This paper takes the Chinese text data from the Sina Weibo platform during COVID-19 as the research object. By comparing the BERT-FGM-BiGRU model with the traditional model, and combining the temporal and spatial characteristics, it further studies the changing trend of user sentiment. Finally, the results show that the BERT-FGM-BiGRU model has the best classification effect and the highest accuracy compared with other models, which provides a scientific method for government departments to supervise public opinion. Based on the classification results of this model and combined with the temporal and spatial characteristics, it can be found that public sentiment is spatially closely related to the severity of the pandemic. Due to the imbalance of information sources, the public showed negative emotions of fear and worry in the early and middle stages, while in the later stage, the public sentiment gradually changed from negative to positive and hopeful with the improvement of the epidemic situation.<\/jats:p>","DOI":"10.3390\/systems11030129","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T01:36:09Z","timestamp":1677634569000},"page":"129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["User Sentiment Analysis of COVID-19 via Adversarial Training Based on the BERT-FGM-BiGRU Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1824-4826","authenticated-orcid":false,"given":"Zhaohui","family":"Li","sequence":"first","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Luli","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Xueru","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Hongyu","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Wenli","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Dalian University of Technology, Dalian 116024, China"}]},{"given":"Jiehan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southampton Business School, University of Southampton, Southampton SO17 1BJ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"ref_1","unstructured":"(2020, February 11). Naming the Coronavirus Disease (COVID-19) and the Virus that Causes It. Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/technical-guidance\/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C.S., and Ho, R.C. (2020). Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. Int. J. Environ. Res. Public Health., 17.","DOI":"10.3390\/ijerph17051729"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false news online","volume":"359","author":"Vosoughi","year":"2018","journal-title":"Science"},{"key":"ref_4","first-page":"25","article-title":"Perceptions of \u2018Public Opinion\u2019 and \u2018Public\u2019 Opinion Expression","volume":"13","author":"Scheufele","year":"2001","journal-title":"Int. J. Prod. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1111\/j.1540-5907.2010.00485.x","article-title":"How Public Opinion Constrains the U.S. Supreme Court","volume":"55","author":"Casillas","year":"2011","journal-title":"Am. J. Political Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Elbattah, M., Arnaud, \u00c9., Gignon, M., and Dequen, G. (2021, January 3\u20137). The Role of Text Analytics in Healthcare: A Review of Recent Developments and Applications. Proceedings of the International Conference on Health Informatics, Victoria, BC, Canada.","DOI":"10.5220\/0010414508250832"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"Huang","year":"2020","journal-title":"Lancet"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1093\/cid\/ciaa247","article-title":"A Comparative Study on the Clinical Features of Coronavirus 2019 (COVID-19) Pneumonia With Other Pneumonias","volume":"71","author":"Zhao","year":"2020","journal-title":"Clin. Infect. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1093\/rapstu\/raaa008","article-title":"The Unprecedented Stock Market Reaction to COVID-19","volume":"10","author":"Baker","year":"2020","journal-title":"Rev. Asset Pricing Stud."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"S94","DOI":"10.1093\/oxrep\/graa033","article-title":"Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective","volume":"36","author":"Mealy","year":"2020","journal-title":"Oxford Rev. Econ. Policy."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e21279","DOI":"10.2196\/21279","article-title":"Effects of COVID-19 on College Students\u2019 Mental Health in the United States: Interview Survey Study","volume":"22","author":"Son","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Qin, L., Sun, Q., Wang, Y., Wu, K.-F., Chen, M., Shia, B.-C., and Wu, S.-Y. (2020). Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index. Int. J. Environ. Res. Public Health., 17.","DOI":"10.2139\/ssrn.3552829"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, S., Wang, Y., Xue, J., Zhao, N., and Zhu, T. (2020). The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. Int. J. Environ. Res. Public Health., 17.","DOI":"10.3390\/ijerph17062032"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e18700","DOI":"10.2196\/18700","article-title":"Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study","volume":"6","author":"Li","year":"2020","journal-title":"JMIR Public Health Surveill"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e18825","DOI":"10.2196\/18825","article-title":"Chinese Public\u2019s Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study","volume":"22","author":"Zhao","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, X., Li, Z., and Tian, Y. (2021). Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities. Electronics, 10.","DOI":"10.3390\/electronics10232921"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e22152","DOI":"10.2196\/22152","article-title":"Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data","volume":"22","author":"Wang","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1093\/jtm\/taaa031","article-title":"The pandemic of social media panic travels faster than the COVID-19 outbreak","volume":"27","author":"Depoux","year":"2020","journal-title":"J. Travel Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1162\/tacl_a_00039","article-title":"Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification","volume":"6","author":"Chen","year":"2018","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, M., Chen, W., Zhang, W., Wang, H., and Zhang, M. (2018, January 2\u20137). Adversarial Learning for Chinese NER from Crowd Annotations. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11507"},{"key":"ref_21","first-page":"2129","article-title":"Automatic image annotation based on generative confrontation network","volume":"39","author":"Shui","year":"2019","journal-title":"Int. J. Comput."},{"key":"ref_22","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., and Fergus, R. (2013). Intriguing properties of neural networks. arXiv."},{"key":"ref_23","unstructured":"Goodfellow, I.-J., Shlens, J., and Szegedy, C. (2014). Explaining and Harnessing Adversarial Examples. arXiv."},{"key":"ref_24","unstructured":"Miyato, T., Andrew, M.-D., and Goodfellow, I. (2017). Adversarial training methods for semi-supervised text classification. arXiv."},{"key":"ref_25","first-page":"113","article-title":"Name entity recognition based on local adversarial training","volume":"58","author":"Li","year":"2021","journal-title":"J. Sichuan Univ. (Nat. Sci. Ed.)"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yasunaga, M., Kasai, J., and Radev, D. (2018, January 1\u20136). Robust Multilingual Part-of-Speech Tagging via Adversarial Training. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, LA, USA.","DOI":"10.18653\/v1\/N18-1089"},{"key":"ref_27","unstructured":"Zhou, J.-T., Zhang, H., Jin, D., Zhu, H.-Y., Fang, M., Goh, B.-S.-M., and Kwok, K. (August, January 28). Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_28","first-page":"65","article-title":"A Survey on Aspect-Based Sentiment Classification","volume":"55","author":"Brauwers","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"14719","DOI":"10.1007\/s00521-020-04824-8","article-title":"Constructing domain-dependent sentiment dictionary for sentiment analysis","volume":"32","author":"Ahmed","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mujahid, M., Lee, E., Rustam, F., Washington, P.B., Ullah, S., Reshi, A.A., and Ashraf, I. (2021). Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19. Appl. Sci., 11.","DOI":"10.3390\/app11188438"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Qin, Y. (2022). The Impact of Shanghai Epidemic, China, 2022 on Public Psychology: A Sentiment Analysis of Microblog Users by Data Mining. Sustain Sci., 14.","DOI":"10.3390\/su14159649"},{"key":"ref_32","first-page":"3558","article-title":"Text sentiment classification model based on BiGRU-attention neural network","volume":"36","author":"Wang","year":"2019","journal-title":"Appl. Res. Comput."},{"key":"ref_33","first-page":"113","article-title":"Text sentiment analysis during epidemic using TCN and BiLSTM+Attention models","volume":"1","author":"Gui","year":"2021","journal-title":"J. Xi\u2019an Univ. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"38856","DOI":"10.1109\/ACCESS.2019.2905048","article-title":"Sentiment Analysis of Chinese Microblog Based on Stacked Bidirectional LSTM","volume":"7","author":"Zhou","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","first-page":"50","article-title":"Polarity discrimination of user comments based on TextCNN","volume":"48","author":"Liu","year":"2019","journal-title":"Electron. World"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102121","DOI":"10.1016\/j.ipm.2019.102121","article-title":"Arabic text classification using deep learning models","volume":"57","author":"Elnagar","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_37","first-page":"103539","article-title":"Comparing deep learning architectures for sentiment analysis on drug reviews","volume":"110","year":"2022","journal-title":"J. Biomed. Infor."},{"key":"ref_38","first-page":"2068","article-title":"Multi-category sentiment analysis method of microblog based on bilingual dictionary","volume":"9","author":"Li","year":"2016","journal-title":"Acta Electron. Sin."},{"key":"ref_39","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_40","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_41","unstructured":"Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent Neural Network Regularization. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cho, K., Merrienboer, B.V., G\u00fcl\u00e7ehre, \u00c7., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_45","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","volume":"16","author":"Abadi","year":"2016","journal-title":"Osdi"},{"key":"ref_47","unstructured":"Chollet, F., and Keras (2022, November 15). GitHub Repository. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_48","first-page":"392","article-title":"Comparative analysis of Baidu index and microindex in influenza surveillance in China","volume":"2","author":"Lu","year":"2016","journal-title":"Comput. Appl. Res."},{"key":"ref_49","unstructured":"STEVENF (1986). Crisis Management: Planning for the Inevitable, American Management Association."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/3\/129\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:44:47Z","timestamp":1760121887000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/3\/129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["systems11030129"],"URL":"https:\/\/doi.org\/10.3390\/systems11030129","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,1]]}}}