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Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches

Author

Listed:
  • Bharati Sanjay Ainapure

    (Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411056, Maharashtra, India)

  • Reshma Nitin Pise

    (Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune 411056, Maharashtra, India)

  • Prathiba Reddy

    (Department of Electronics and Telecommunication Engineering, G. H. Raisoni College of Engineering and Management, Pune 412207, Maharashtra, India)

  • Bhargav Appasani

    (School of Electronics Engineering, Kalinga Institute of Industrial Technology, Patia 751024, Bhubaneswar, India)

  • Avireni Srinivasulu

    (Department of Electronics & Communication Engineering, Mohan Babu University, Tirupati 517102, Andhra Pradesh, India)

  • Mohammad S. Khan

    (Department of Computer & Information Sciences, East Tennessee State University, Johnson City, TN 37614, USA)

  • Nicu Bizon

    (Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania
    ICSI Energy Department, National Research and Development Institute for Cryogenic and Isotopic Technologies, 240050 Ramnicu Valcea, Romania
    Doctoral School, University Politehnica of Bucharest, Splaiul Independentei Street No. 313, 060042 Bucharest, Romania)

Abstract

Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks.

Suggested Citation

  • Bharati Sanjay Ainapure & Reshma Nitin Pise & Prathiba Reddy & Bhargav Appasani & Avireni Srinivasulu & Mohammad S. Khan & Nicu Bizon, 2023. "Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2573-:d:1052965
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    References listed on IDEAS

    as
    1. Meng Hsiu Tsai & Yingfeng Wang, 2021. "Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19," IJERPH, MDPI, vol. 18(12), pages 1-14, June.
    2. Jae-Geum Shim & Kyoung-Ho Ryu & Sung Hyun Lee & Eun-Ah Cho & Yoon Ju Lee & Jin Hee Ahn, 2021. "Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea," IJERPH, MDPI, vol. 18(12), pages 1-9, June.
    3. Carol Shofiya & Samina Abidi, 2021. "Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data," IJERPH, MDPI, vol. 18(11), pages 1-10, June.
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    Cited by:

    1. Bahareh Farhoudinia & Selcen Ozturkcan & Nihat Kasap, 2024. "Emotions unveiled: detecting COVID-19 fake news on social media," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.

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