BERT-Based Deep Learning Models for Analyzing Sentiments of COVID-19-Related Social Media Tweets:

N. Manikandan The New College, India https://orcid.org/0009-0002-6453-4776 Gnaneswari Gnanaguru CMR Institute of Technology, India V. Viswapriya Vels Institute of Science, Technology, and Advanced Studies, India S. Silvia Priscila Bharath Institute of Higher Education and Research, India https://orcid.org/0000-0002-6040-3149 Prasanna Ranjith Christodoss Messiah University, USA S. Saranya Dhaanish Ahmed College of Engineering, India BERT-Based Deep Learning Models for Analyzing Sentiments of COVID-19-Related Social Media Tweets

Social media data has become an important tool for understanding public attitudes. All over the world, the COVID-19 pandemic impacted people's lives in various ways. People worldwide utilize social media to express their thoughts and feelings about the pandemic. Because of the diversity of Twitter posts, researchers analyze sentiment and examine the public's numerous sentiments concerning COVID-19. In the meantime, people have shared their thoughts about immunization protection and efficacy on social media sites such as Twitter. Studies have demonstrated that it may strengthen ideas and impact the general opinion. This study focuses on analyzing the sentiment of Twitter data connected to the COVID-19 pandemic using bidirectional encoder representations from transformers (BERT) with random forest (RF), convolutional neural networks (CNN), and recurrent CNN (RCNN) classifiers.
chapter 2 2025 2 28 21 36 10.4018/979-8-3693-9375-8.ch002 20250423095631 https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-9375-8.ch002 https://www.igi-global.com/viewtitle.aspx?TitleId=376587 10.1109/ACCESS.2024.3371585 10.4018/979-8-3693-1301-5.ch006 Sentiment Analysis on Covid-19 data Using BERT Model. T.Arunkarthi 2024 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE) ArunkarthiT.ShanthiS.NirmaladeviK.AshwinthK.DevR. B.RajkumarN. (2024). Sentiment Analysis on Covid-19 data Using BERT Model. In 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE). IEEE. 10.69888/FTSTPL.2024.000227 10.3390/s23010506 10.35784/jcsi.5564 Impact of E-Payment System on Consumer Buying Behaviour. K. M.Chummar 2023 230 4 FMDB Transactions on Sustainable Social Sciences Letters ChummarK. M.RonaldB. J.RaoA. V.VadlamudiA. K. (2023). Impact of E-Payment System on Consumer Buying Behaviour.FMDB Transactions on Sustainable Social Sciences Letters, 1(4), 230–238. 1 Thorough analysis of deep learning methods for diagnosis of COVID-19 CT images G.Gnanaguru 2024 10.4018/979-8-3693-5946-4.ch004 46 Advances in Medical Technologies and Clinical Practice GnanaguruG.PriscilaS. S.SakthivanithaM.RadhakrishnanS.RajestS. S.SinghS. (2024). Thorough analysis of deep learning methods for diagnosis of COVID-19 CT images. In Advances in Medical Technologies and Clinical Practice (pp. 46–65). IGI Global. 10.3389/fpubh.2021.812735 Studying Price Dynamics of Bus Services Using Machine Learning Algorithms. P.Jani 2024 54 1 AVE Trends In Intelligent Computing Systems JaniP.NanbanD.SelvanJ.RichardsonN.SivakaniR.SubhashniR. (2024). Studying Price Dynamics of Bus Services Using Machine Learning Algorithms.AVE Trends In Intelligent Computing Systems, 1(1), 54–65. 1 10.1007/s13278-024-01240-x A Study on the Efficiency of Structured Instruction on Dengue Fever Prevention and Control among People. A.Kharayat 2024 25 1 AVE Trends In Intelligent Social Letters KharayatA.RawatD.PanwarN. (2024). A Study on the Efficiency of Structured Instruction on Dengue Fever Prevention and Control among People.AVE Trends In Intelligent Social Letters, 1(1), 25–40. 1 10.12785/ijcds/150105 10.14569/IJACSA.2024.0151069 A Systematic Review on Workforce Development in Healthcare Sector: Implications in the Post-COVID Scenario. P. S.Kuragayala 2023 36 1 FMDB Transactions on Sustainable Technoprise Letters KuragayalaP. S. (2023). A Systematic Review on Workforce Development in Healthcare Sector: Implications in the Post-COVID Scenario.FMDB Transactions on Sustainable Technoprise Letters, 1(1), 36–46. 1 10.1007/s10462-021-09973-3 10.13053/cys-28-2-4568 10.69888/FTSML.2024.000159 Experimental Analysis of UAV Networks Using Oppositional Glowworm Swarm Optimization and Deep Learning Clustering and Classification. M. S.Minu 2023 124 3 FMDB Transactions on Sustainable Computing Systems MinuM. S.Subashka RameshS. S.CanessaneR.Al-AminM.Bin SulaimanR. (2023). Experimental Analysis of UAV Networks Using Oppositional Glowworm Swarm Optimization and Deep Learning Clustering and Classification.FMDB Transactions on Sustainable Computing Systems, 1(3), 124–134. 1 10.1007/s00354-023-00227-0 Importance of Business Financial Risk Analysis in SMEs According to COVID-19. M. P.Ocoró 2023 12 1 FMDB Transactions on Sustainable Management Letters OcoróM. P.PoloO. C. C.KhandareS. (2023). Importance of Business Financial Risk Analysis in SMEs According to COVID-19.FMDB Transactions on Sustainable Management Letters, 1(1), 12–21. 1 Untangling Economic Threads through an In-depth Analysis of Universal Basic Income’s Influence on Socio-economic Dynamics. D.Philip 2024 50 1 AVE Trends In Intelligent Social Letters PhilipD.SinghF.SinghS. (2024). Untangling Economic Threads through an In-depth Analysis of Universal Basic Income’s Influence on Socio-economic Dynamics.AVE Trends In Intelligent Social Letters, 1(1), 50–60. 1 Analysis of Machine Learning and Deep Learning Methods for Superstore Sales Prediction. S. S.Priscila 2023 1 1 FMDB Transactions on Sustainable Computer Letters PriscilaS. S.RajestS. S.TadiboinaS. N.ReginR.AndrásS. (2023). Analysis of Machine Learning and Deep Learning Methods for Superstore Sales Prediction.FMDB Transactions on Sustainable Computer Letters, 1(1), 1–11. 1 10.1016/j.eswa.2022.118715 10.4018/979-8-3693-1301-5 10.69888/FTSSSL.2024.000220 10.1016/j.eswa.2023.119862 Performance and Risk Analysis of Sustainable Mutual Funds in India: A Comparative Study. B. J.Ronald 2024 13 1 AVE Trends In Intelligent Social Letters RonaldB. J.JosephS. G. M.KumarP.HsuC.-Y.MehmoodA. (2024). Performance and Risk Analysis of Sustainable Mutual Funds in India: A Comparative Study.AVE Trends In Intelligent Social Letters, 1(1), 13–24. 1 10.69888/FTSIN.2024.000156 Family resilience during COVID-19: A study of adaptation and well-being among social work students. J. E.Sebastian 2024 93 2 AVE Trends in Intelligent Social Letters SebastianJ. E.BabuJ.AshifaK. M. (2024). Family resilience during COVID-19: A study of adaptation and well-being among social work students.AVE Trends in Intelligent Social Letters, 1(2), 93–103. 1 10.1109/CCWC60891.2024.10427866 Srivastava, S., Sarkar, M. K., & Chakraborty, C. (2024). Machine learning approaches for COVID-19 sentiment analysis: Unveiling the power of BERT. 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC). IEEE. Deciphering Product Review Sentiments Using BERT and TensorFlow. D.Suraj 2023 77 2 FMDB Transactions on Sustainable Computing Systems SurajD.DineshS.BalajiR.DeepikaP.AjilaF. (2023). Deciphering Product Review Sentiments Using BERT and TensorFlow.FMDB Transactions on Sustainable Computing Systems, 1(2), 77–88. 1 Tripathi, S., & Al-Zubaidi, A. (2023). A Study within Salalah’s Higher Education Institutions on Online Learning Motivation and Engagement Challenges during Covid-19. FMDB Transactions on Sustainable Techno Learning, 1(1), 1–10. Decoding sentiments: Enhancing covid-19 tweet analysis through bert-rcnn fusion. J.Xiong 2024 86 1 Journal of Theory and Practice of Engineering Science XiongJ.FengM.WangX.JiangC.ZhangN.ZhaoZ. (2024). Decoding sentiments: Enhancing covid-19 tweet analysis through bert-rcnn fusion.Journal of Theory and Practice of Engineering Science, 4(1), 86–93. 4 A Comparative Study on Work from Home During Covid-19: Employees Perception and Experiences. C.Yturralde 2023 231 4 FMDB Transactions on Sustainable Technoprise Letters YturraldeC.RamosJ. (2023). A Comparative Study on Work from Home During Covid-19: Employees Perception and Experiences.FMDB Transactions on Sustainable Technoprise Letters, 1(4), 231–243. 1