Decoding Sentiments: Enhancing COVID-19 Tweet Analysis through BERT-RCNN Fusion
DOI:
https://doi.org/10.53469/jtpes.2024.04(01).12Keywords:
COVID-19, Sentiment Analysis, BERT-RCNNAbstract
In the era of the COVID-19 pandemic, the surge in information sharing on social media, particularly Twitter, necessitates a nuanced understanding of sentiments. Conventional sentiment analysis methods face challenges in capturing the evolving discourse's contextual nuances. This study introduces a novel approach, employing BERT-RCNN for sentiment classification of COVID-19-related tweets. BERT's bidirectional contextual insights combined with RCNN's feature extraction enhance our model's accuracy. The labels 'Neutral,' 'Positive,' and 'Negative' provide a nuanced emotional analysis. Our methodology overcomes traditional limitations, offering a context-aware sentiment analysis. By leveraging BERT-RCNN, this research contributes to a deeper understanding of public sentiments during the pandemic, addressing evolving challenges in sentiment classification.
References
Al-Garadi, M. A., Yang, Y. C., Lakamana, S., & Sarker, A. . A text classification approach for the automatic detection of twitter posts containing self-reported covid-19 symptoms.2020.
Samuel, J., Ali, G. M. N., Rahman, M. M., Esawi, E., & Samuel, Y. . Covid-19 public sentiment insights and machine learning for tweets classification. Information, 2020,11(6): 314.
Wisesty, U. N., Rismala, R., Munggana, W., & Purwarianti, A. (2021, August). Comparative study of covid-19 tweets sentiment classification methods. In 2021 9th International conference on information and communication technology (ICoICT) (pp. 588-593). IEEE.
Ezhilan, A., Dheekksha, R., Anahitaa, R., & Shivani, R. (2021, June). Sentiment analysis and classification of COVID-19 tweets. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 821-828). IEEE.
Shamrat, F. M. J. M., Chakraborty, S., Imran, M. M., Muna, J. N., Billah, M. M., Das, P., & Rahman, O. M. . Sentiment analysis on twitter tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm. Indonesian Journal of Electrical Engineering and Computer Science,2021, 23(1), 463-470.
Didi, Y., Walha, A., & Wali, A. . COVID-19 tweets classification based on a hybrid word embedding method. Big Data and Cognitive Computing,2022, 6(2), 58.
Shahi, T. B., Sitaula, C., & Paudel, N. (2022). A hybrid feature extraction method for Nepali COVID-19-related tweets classification. Computational Intelligence and Neuroscience, 2022.
Ding, J., Li, B., Xu, C., Qiao, Y., & Zhang, L. . Diagnosing crop diseases based on domain-adaptive pre-training BERT of electronic medical records. Applied Intelligence,2023. 53(12), 15979-15992.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Lai, S., Xu, L., Liu, K., & Zhao, J. (2015, February). Recurrent convolutional neural networks for text classification. In Proceedings of the AAAI conference on artificial intelligence (Vol. 29, No. 1).
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2022). Large language models can self-improve. arXiv preprint arXiv:2210.11610.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. . Language models are few-shot learners. Advances in neural information processing systems, 2020,33, 1877-1901.
Zhou, H., Lou, Y., Xiong, J., Wang, Y., & Liu, Y. . Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery. Frontiers in Computing and Intelligent Systems, 2023,6(1), 103-106.
Liu, S., Wu, K., Jiang, C., Huang, B., & Ma, D. (2023). Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach. arXiv preprint arXiv:2401.00534.
Su, J., Nair, S., & Popokh, L. (2023, February). EdgeGym: A Reinforcement Learning Environment for Constraint-Aware NFV Resource Allocation. In 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC) (pp. 1-7). IEEE.
Su, J., Nair, S., & Popokh, L. (2022, November). Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning. In 2022 IEEE Ninth International Conference on Communications and Networking (ComNet) (pp. 1-7). IEEE.
Popokh, L., Su, J., Nair, S., & Olinick, E. (2021, September). IllumiCore: Optimization Modeling and Implementation for Efficient VNF Placement. In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-7). IEEE.
Jin, X., Katsis, C., Sang, F., Sun, J., Bertino, E., Kompella, R. R., & Kundu, A. (2023). Prometheus: Infrastructure Security Posture Analysis with AI-generated Attack Graphs. arXiv preprint arXiv:2312.13119.
Jin, X., & Wang, Y. (2023). Understand Legal Documents with Contextualized Large Language Models. arXiv preprint arXiv:2303.12135.
Jin, X., Manandhar, S., Kafle, K., Lin, Z., & Nadkarni, A. (2022, November). Understanding iot security from a market-scale perspective. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (pp. 1615-1629).
Jin, X., Pei, K., Won, J. Y., & Lin, Z. (2022, November). Symlm: Predicting function names in stripped binaries via context-sensitive execution-aware code embeddings. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (pp. 1631-1645).
Guo, Y. Q., Ma, H. D., Shi, K. B., Cao, H., Huang, L. Z., Yao, F. C., & Hu, T. Y. . Porous-grain-upper-boundary model and its application to Tarim Basin carbonates. Applied Geophysics, 2013,10(4), 411-422.
Yao, Y., Ma, H., Liu, Y., Peng, C., Mohapatra, G., Duncan, G., ... & Checkles, S. (2019). Improving images under complex salt with ocean bottom node data: 89th Annual International Meeting, SEG. Expanded Abstracts, https://doi. org/10.1190/segam2019-3216820.1.
Chen, S., Potsaid, B., Li, Y., Lin, J., Hwang, Y., Moult, E. M., ... & Fujimoto, J. G. . High speed, long range, deep penetration swept source OCT for structural and angiographic imaging of the anterior eye. Scientific reports, 2022,12(1), 992.
Lin, J., Wang, B., Yang, G., & Zhou, M. . Indoor localization based on weighted surfacing from crowdsourced samples. Sensors, 2018,18(9), 2990.
Dong, H., Xie, J., Jing, Z., & Ren, D. (2020). Variational autoencoder for anti-cancer drug response prediction. arXiv preprint arXiv:2008.09763.
Xiao, T., Zeng, L., Shi, X., Zhu, X., & Wu, G. (2022, September). Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 406-415). Cham: Springer Nature Switzerland.
Wang, X., Xiao, T., Tan, J., Ouyang, D., & Shao, J. (2020). MRMRP: multi-source review-based model for rating prediction. In Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings, Part II 25 (pp. 20-35). Springer International Publishing.
Wang, X., Xiao, T., & Shao, J. (2021). EMRM: Enhanced Multi-source Review-Based Model for Rating Prediction. In Knowledge Science, Engineering and Management: 14th International Conference, KSEM 2021, Tokyo, Japan, August 14–16, 2021, Proceedings, Part III 14 (pp. 487-499). Springer International Publishing.
Xu, Z., Xiao, T., He, W., Wang, Y., & Jiang, Z. (2023, November). Spatial knowledge-infused hierarchical learning: An application in flood mapping on earth imagery. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (pp. 1-10).
Xu, Z., Xiao, T., He, W., Wang, Y., & Jiang, Z. (2023, October). Infusing Spatial Knowledge into Deep Learning for Earth Science: A Hydrological Application. In NeurIPS 2023 AI for Science Workshop.
Deng, Y., Kesselman, C., Sen, S., & Xu, J. (2019, December). Computational operations research exchange (core): A cyber-infrastructure for analytics. In 2019 Winter Simulation Conference (WSC) (pp. 3447-3456). IEEE.
Xu, J., & Sen, S. (2023). Compromise policy for multi-stage stochastic linear programming: Variance and bias reduction. Computers & Operations Research, 153, 106132.
Xu, J., & Sen, S. (2021). Decision Intelligence for Nationwide Ventilator Allocation During the COVID-19 Pandemic. SN Computer Science, 2(6), 423.
Xu, J., & Sen, S. (2023). Ensemble Variance Reduction Methods for Stochastic Mixed-Integer Programming and their Application to the Stochastic Facility Location Problem. INFORMS Journal on Computing.
Li, H., & Zhao, H. (2022, June). Applying an Online Learning Platform to Enhance Students’ Online Education Classroom Learning Experience during COVID-19. In 2022 8th International Conference on Humanities and Social Science Research (ICHSSR 2022) (pp. 2837-2842). Atlantis Press.
Wang, Z., Cai, L., Chen, Y., Li, H., & Jia, H. (2021). The teaching design methods under educational psychology based on deep learning and artificial intelligence. Frontiers in Psychology, 12, 711489.
Zhao, D., Li, H., Xu, A., & Song, T. (2022). Psychological Mobilization of Innovative Teaching Methods for Students' Basic Educational Curriculum Reform Under Deep Learning. Frontiers in Psychology, 13, 843493.
Shangguan, Z., Lin, L., Wu, W., & Xu, B. (2021). Neural process for black-box model optimization under bayesian framework. arXiv preprint arXiv:2104.02487.
Lin, L., Wu, W., Shangguan, Z., Wshah, S., Elmoudi, R., & Xu, B. (2020, December). Hpt-rl: Calibrating power system models based on hierarchical parameter tuning and reinforcement learning. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1231-1237). IEEE.
Lai, L., Shangguan, Z., Zhang, J., & Ohn-Bar, E. (2023). XVO: Generalized visual odometry via cross-modal self-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10094-10105).
Liao, J., Sanchez, V., & Guha, T. (2022, October). Self-Supervised Frontalization and Rotation Gan with Random Swap for Pose-Invariant Face Recognition. In 2022 IEEE International Conference on Image Processing (ICIP) (pp. 911-915). IEEE.
Liao, J., Guha, T., & Sanchez, V. Self-Supervised Random Mask Attention Gan in Tackling Pose-Invariant Face Recognition. Available at SSRN 4583223.
Bao, W., Che, H., & Zhang, J. (2020, December). Will_Go at SemEval-2020 Task 3: An accurate model for predicting the (graded) effect of context in word similarity based on BERT. In Proceedings of the Fourteenth Workshop on Semantic Evaluation (pp. 301-306).
Qiao, Y., Jin, J., Ni, F., Yu, J., & Chen, W. (2023). APPLICATION OF MACHINE LEARNING IN FINANCIAL RISK EARLY WARNING AND REGIONAL PREVENTION AND CONTROL: A SYSTEMATIC ANALYSIS BASED ON SHAP. WORLD TRENDS, REALITIES AND ACCOMPANYING PROBLEMS OF DEVELOPMENT, 331.
YUXIN, Q., & FANGHAO, N. (2023). COOPERATIVE GENERATIVE ADVERSARIAL NETWORKS: A DEEP DIVE INTO COLLABORATIVE INNOVATION IN GANS. СОВРЕМЕННЫЕ НАУЧНЫЕ ИССЛЕДОВАНИЯ: АКТУАЛЬНЫЕ ВОПРОСЫ, 28.
Wang, L., & Xia, W. (2022). Power‐type derivatives for rough volatility with jumps. Journal of Futures Markets, 42(7), 1369-1406.
Wang, L., & Carvalho, L. (2023). Deviance matrix factorization. Electronic Journal of Statistics, 17(2), 3762-3810.
Wang, L., Lauriola, I., & Moschitti, A. (2023). Accurate training of web-based question answering systems with feedback from ranked users.
Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
Jordan, M. I. (1997). Serial order: A parallel distributed processing approach. In Advances in psychology (Vol. 121, pp. 471-495). North-Holland.
Graves, A., & Graves, A. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37-45.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
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Copyright (c) 2024 Jize Xiong, Mingyang Feng, Xiaosong Wang, Chufeng Jiang, Ning Zhang, Zhiming Zhao
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