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Exploring the Performance of Ensemble Machine Learning Classifiers for Sentiment Analysis of COVID-19 Tweets

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Sentimental Analysis and Deep Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1408))

Abstract

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%).

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Notes

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References

  1. 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.

    Google Scholar 

  2. 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.

    Article  Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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.

    Google Scholar 

  6. 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.

    Article  Google Scholar 

  7. 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.

    Google Scholar 

  8. 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).

    Google Scholar 

  9. 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.

    Google Scholar 

  10. 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.

    Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. 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

  13. 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.

    Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. 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.

    Google Scholar 

  16. 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).

    Google Scholar 

  17. 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.

    Google Scholar 

  18. 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.

    Google Scholar 

  19. 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.

    Google Scholar 

  20. Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., & Shah, Z. Top concerns of tweeters during the covid-19 pandemic: A surveillance study.

    Google Scholar 

  21. 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.

    Google Scholar 

  22. 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.

    Article  Google Scholar 

  23. 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.

    Article  Google Scholar 

  24. 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.

    Article  MathSciNet  Google Scholar 

  25. Artstein, R., & Poesio, M. (2008). Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4), 555–596.

    Article  Google Scholar 

  26. Hays, R. D., & Revicki, D. (2005). Reliability and validity (including responsiveness). Assessing Quality of Life in Clinical Trials, 2, 25–39.

    Google Scholar 

  27. Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5), 429–449.

    Article  Google Scholar 

  28. 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.

    Google Scholar 

  29. Dai, A. M., Olah, C., & Le, Q. V. (2015). Document embedding with paragraph vectors. arXiv:1507.07998

  30. Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv:1908.10084

  31. 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

  32. 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.

    Google Scholar 

  33. 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.

    Article  Google Scholar 

  34. Ruta, D., & Gabrys, B. (2005). Classifier selection for majority voting. Information Fusion, 6(1), 63–81.

    Article  Google Scholar 

  35. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.

    MathSciNet  MATH  Google Scholar 

  36. Bühlmann, P., Yu, B., et al. (2002). Analyzing bagging. The Annals of Statistics, 30(4), 927–961.

    Article  MathSciNet  Google Scholar 

  37. Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. CRC Press.

    Google Scholar 

  38. Džeroski, S., & Ženko, B. (2004). Is combining classifiers with stacking better than selecting the best one? Machine Learning, 54(3), 255–273.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka, 1216, Bangladesh

    Md. Mahbubar Rahman & Muhammad Nazrul Islam

Authors
  1. Md. Mahbubar Rahman
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  2. Muhammad Nazrul Islam
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Corresponding author

Correspondence to Md. Mahbubar Rahman .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal

    Subarna Shakya

  2. Intelligent Systems Research Centre, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Songkla University, Songkhla, Thailand

    Sinchai Kamolphiwong

  4. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

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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

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