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Sentimental Analysis and Deep Learning pp 383–396Cite as

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

Part of the Advances in Intelligent Systems and Computing book series (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%).

Keywords

  • COVID-19
  • Machine learning
  • Tweet
  • Sentiment analysis
  • Natural language processing
  • Ensemble algorithms

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Notes

  1. 1.

    https://www.tensorflow.org/hub.

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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

    Dr. Subarna Shakya

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

    Dr. Valentina Emilia Balas

  3. Songkla University, Songkhla, Thailand

    Sinchai Kamolphiwong

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

    Dr. Ke-Lin Du

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Cite this paper

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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