Since the beginning of the global COVID-19 pandemic, measuring public opinion has been considered as one of the most critical issues for decision-makers to fight against the pandemic, such as implementing a national lockdown, introducing quarantine procedure, providing health services, and the like. During the COVID-19 pandemic, decision-makers in several countries around the world made a number of critical decisions focused on public opinion to combat coronavirus. In the field of natural language processing, sentiment analysis has emerged for mining public opinion, while machine learning (ML) algorithms are very common for analyzing sentiment. In this research, approximately 12 thousand tweets from United Kingdom (UK) were rigorously annotated by three independent reviewers, and based on the labeled tweets, three different ensemble ML models were proposed to classify the tweet data into three sentiment labels: positive, negative, and neutral. The study found that stacking classifier (SC) showed the highest F1-score (83.5%), followed by the voting classifier (VC) (83.3%) and bagging classifier (BC) (83.2%).
- Machine learning
- Sentiment analysis
- Natural language processing
- Ensemble algorithms
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Chong, W. Y., Selvaretnam, B., & Soon, L. K. (2014). Natural language processing for sentiment analysis: An exploratory analysis on tweets. In 2014 4th international conference on artificial intelligence with applications in engineering and technology (pp. 212–217). IEEE.
Islam, M. N., & Islam, A. N. (2020). A systematic review of the digital interventions for fighting covid-19: The Bangladesh perspective. IEEE Access, 8, 114078–114087.
Islam, M. N., Inan, T. T., & Islam, A. N. (2020). Covid-19 and the Rohingya refugees in Bangladesh: The challenges and recommendations. Asia Pacific Journal of Public Health, 32(5), 283–284.
Laato, S., Islam, A. N., Islam, M. N., & Whelan, E. (2020). What drives unverified information sharing and cyberchondria during the covid-19 pandemic? European Journal of Information Systems, 29(3), 288–305.
Islam, M. N., Inan, T. T., Rafi, S., Akter, S. S., Sarker, I. H., & Islam, A. N. (2021). A systematic review on the use of AI and ML for fighting the covid-19 pandemic. IEEE Transactions on Artificial Intelligence.
Nichols, J. A., Chan, H. W. H., & Baker, M. A. (2019). Machine learning: Applications of artificial intelligence to imaging and diagnosis. Biophysical Reviews, 11(1), 111–118.
Islam, M. N., Mahmud, T., Khan, N. I., Mustafina, S. N., & Islam, A. N. (2020). Exploring machine learning algorithms to find the best features for predicting modes of childbirth. IEEE Access.
Khan, N. I., Mahmud, T., Islam, M. N., & Mustafina, S. N. (2020). Prediction of cesarean childbirth using ensemble machine learning methods. In Proceedings of the 22nd international conference on information integration and web-based applications & services (pp. 331–339).
Aishwarja, A. I., Eva, N. J., Mushtary, S., Tasnim, Z., Khan, N. I., & Islam, M. N. (2020). Exploring the machine learning algorithms to find the best features for predicting the breast cancer and its recurrence. In International conference on intelligent computing & optimization (pp. 546–558). Springer.
Khan, N. S., Muaz, M. H., Kabir, A., & Islam, M. N. (2017). Diabetes predicting mhealth application using machine learning. In 2017 IEEE international WIE conference on electrical and computer engineering (WIECON-ECE) (pp. 237–240). IEEE.
Dhaya, R. (2020). Deep net model for detection of covid-19 using radiographs based on ROC analysis. Journal of Innovative Image Processing (JIIP), 2(03), 135–140.
Zaman, A., Islam, M. N., Zaki, T., & Hossain, M. S. (2020). Ict intervention in the containment of the pandemic spread of covid-19: An exploratory study. arXiv:2004.09888
Omar, K. S., Mondal, P., Khan, N. S., Rizvi, M. R. K., & Islam, M. N. (2019). A machine learning approach to predict autism spectrum disorder. In 2019 international conference on electrical, computer and communication engineering (ECCE) (pp. 1–6). IEEE.
Villavicencio, C., Macrohon, J. J., Inbaraj, X. A., Jeng, J. H., & Hsieh, J. G. (2021). Twitter sentiment analysis towards covid-19 vaccines in the philippines using naïve bayes. Information, 12(5), 204.
Khan, R., Shrivastava, P., Kapoor, A., Tiwari, A., & Mittal, A. (2020). Social media analysis with AI: Sentiment analysis techniques for the analysis of twitter covid-19 data. Journal of Critical Review, 7(9), 2761–2774.
Kaur, H., Ahsaan, S. U., Alankar, B., & Chang, V. (2021). A proposed sentiment analysis deep learning algorithm for analyzing covid-19 tweets. In Information Systems Frontiers (pp. 1–13).
Gupta, M., Bansal, A., Jain, B., Rochelle, J., Oak, A., & Jalali, M. S. (2021). Whether the weather will help us weather the covid-19 pandemic: Using machine learning to measure twitter users’ perceptions. International Journal of Medical Informatics, 145, 104340.
Garcia, K., & Berton, L. (2021). Topic detection and sentiment analysis in twitter content related to covid-19 from Brazil and the USA. Applied Soft Computing, 101, 107057.
de Melo, T., & Figueiredo, C. M. (2021). Comparing news articles and tweets about covid-19 in Brazil: Sentiment analysis and topic modeling approach. JMIR Public Health and Surveillance, 7(2), e24585.
Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., & Shah, Z. Top concerns of tweeters during the covid-19 pandemic: A surveillance study.
Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A., & Choi, G. S. (2021). A performance comparison of supervised machine learning models for covid-19 tweets sentiment analysis. Plos One, 16(2), e0245909.
Anderson, R. M., Hollingsworth, T. D., Baggaley, R. F., Maddren, R., & Vegvari, C. (2020). Covid-19 spread in the UK: The end of the beginning? The Lancet, 396(10251), 587–590.
Armstrong, D., Gosling, A., Weinman, J., & Marteau, T. (1997). The place of inter-rater reliability in qualitative research: An empirical study. Sociology, 31(3), 597–606.
Gwet, K. L. (2008). Computing inter-rater reliability and its variance in the presence of high agreement. British Journal of Mathematical and Statistical Psychology, 61(1), 29–48.
Artstein, R., & Poesio, M. (2008). Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4), 555–596.
Hays, R. D., & Revicki, D. (2005). Reliability and validity (including responsiveness). Assessing Quality of Life in Clinical Trials, 2, 25–39.
Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5), 429–449.
He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) (pp. 1322–1328). IEEE.
Dai, A. M., Olah, C., & Le, Q. V. (2015). Document embedding with paragraph vectors. arXiv:1507.07998
Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv:1908.10084
Cer, D., Yang, Y., Kong, S. Y., Hua, N., Limtiaco, N., John, R. S., Constant, N., Guajardo-Céspedes, M., Yuan, S., Tar, C., et al. (2018). Universal sentence encoder. arXiv:1803.11175
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12, 2825–2830.
Ghawi, R., & Pfeffer, J. (2019). Efficient hyperparameter tuning with grid search for text categorization using knn approach with bm25 similarity. Open Computer Science, 9(1), 160–180.
Ruta, D., & Gabrys, B. (2005). Classifier selection for majority voting. Information Fusion, 6(1), 63–81.
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Bühlmann, P., Yu, B., et al. (2002). Analyzing bagging. The Annals of Statistics, 30(4), 927–961.
Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. CRC Press.
Džeroski, S., & Ženko, B. (2004). Is combining classifiers with stacking better than selecting the best one? Machine Learning, 54(3), 255–273.
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Rahman, M.M., Islam, M.N. (2022). Exploring the Performance of Ensemble Machine Learning Classifiers for Sentiment Analysis of COVID-19 Tweets. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_30
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5156-4
Online ISBN: 978-981-16-5157-1