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Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic

Author

Listed:
  • Thavavel Vaiyapuri

    (College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Sharath Kumar Jagannathan

    (Frank J. Guarini School of Business, Saint Peter’s University, 2641 John F. Kennedy Boulevard, Jersey City, NJ 07306, USA)

  • Mohammed Altaf Ahmed

    (Department of Computer Engineering, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • K. C. Ramya

    (Department of EEE, Sri Krishna College of Engineering and Technology, Coimbatore 641008, India)

  • Gyanendra Prasad Joshi

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea)

  • Soojeong Lee

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea)

  • Gangseong Lee

    (Ingenium College, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea)

Abstract

The COVID-19 outbreak is a disastrous event that has elevated many psychological problems such as lack of employment and depression given abrupt social changes. Simultaneously, psychologists and social scientists have drawn considerable attention towards understanding how people express their sentiments and emotions during the pandemic. With the rise in COVID-19 cases with strict lockdowns, people expressed their opinions publicly on social networking platforms. This provides a deeper knowledge of human psychology at the time of disastrous events. By applying user-produced content on social networking platforms such as Twitter, the sentiments and views of people are analyzed to assist in introducing awareness campaigns and health intervention policies. The modern evolution of artificial intelligence (AI) and natural language processing (NLP) mechanisms has revealed remarkable performance in sentimental analysis (SA). This study develops a new Marine Predator Optimization with Natural Language Processing for Twitter Sentiment Analysis (MPONLP-TSA) for the COVID-19 Pandemic. The presented MPONLP-TSA model is focused on the recognition of sentiments that exist in the Twitter data during the COVID-19 pandemic. The presented MPONLP-TSA technique undergoes data preprocessing to convert the data into a useful format. Furthermore, the BERT model is used to derive word vectors. To detect and classify sentiments, a bidirectional recurrent neural network (BiRNN) model is utilized. Finally, the MPO algorithm is exploited for optimal hyperparameter tuning process, and it assists in enhancing the overall classification performance. The experimental validation of the MPONLP-TSA approach can be tested by utilizing the COVID-19 tweets dataset from the Kaggle repository. A wide comparable study reported a better outcome of the MPONLP-TSA method over current approaches.

Suggested Citation

  • Thavavel Vaiyapuri & Sharath Kumar Jagannathan & Mohammed Altaf Ahmed & K. C. Ramya & Gyanendra Prasad Joshi & Soojeong Lee & Gangseong Lee, 2023. "Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6404-:d:1119105
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    References listed on IDEAS

    as
    1. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
    2. Ashkan Ebadi & Pengcheng Xi & Stéphane Tremblay & Bruce Spencer & Raman Pall & Alexander Wong, 2021. "Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 725-739, January.
    3. Alfonso Chaves-Montero & Fernando Relinque-Medina & Manuela Á. Fernández-Borrero & Octavio Vázquez-Aguado, 2021. "Twitter, Social Services and Covid-19: Analysis of Interactions between Political Parties and Citizens," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    4. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Hong Fan & Laith Abualigah & Mohamed Abd Elaziz, 2020. "Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
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