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The Comparative Performance of Machine Learning Models for COVID-19 Sentiment Analysis

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Recent Advances in Soft Computing and Data Mining (SCDM 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 457))

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Abstract

The COVID-19 outbreak is moving individuals globally. Monitoring social media and internet news is now vital to grasping this phenomenon and its impact. This study’s goal is to offer a method for capturing important concepts and themes addressed in the mainstream media and social networks and then apply it to the COVID-19 outbreak. This study compares articles and news, then visualizes the evolution and influence of the COVID-19 epidemic articles. The COVID-19 articles dataset from Kaggle is utilized for experiments. Various articles use the dataset to examine COVID-19 reviews. The studies use datasets and models such as Decision Tree (DT), Logistic Regression (LR), and Extra Tree Classifier (ETC), with F1 score, precision, and recall being evaluated. To improve accuracy, Term Frequency-Inverse Document Frequency (TF-IDF) is applied to the extracted keywords. The assembled model enhances this research, and the Logistic Regression (LR) model provides the highest accuracy at 90%.

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Authors and Affiliations

  1. Department of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan

    Syeda Fiza Rubab, Muhammad Faheem Mushtaq,  Amna, Ali Samad & Ghulam Gilanie

  2. Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan

    Muhammad Hussain Tahir

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Muhammad Ghulam Ghouse

Authors
  1. Syeda Fiza Rubab
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  2. Muhammad Faheem Mushtaq
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  3. Muhammad Hussain Tahir
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  4. Amna
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  5. Ali Samad
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  6. Ghulam Gilanie
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  7. Muhammad Ghulam Ghouse
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Corresponding author

Correspondence to Muhammad Faheem Mushtaq .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

  4. School of Information Technology Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nureize Arbaiy

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Rubab, S.F. et al. (2022). The Comparative Performance of Machine Learning Models for COVID-19 Sentiment Analysis. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_37

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