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Pandemic Perspectives: A Review of Sentiment Analysis Approaches on COVID-19

by Satvika, Akhil Kaushik
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 21
Year of Publication: 2025
Authors: Satvika, Akhil Kaushik
10.5120/ijca2025924091

Satvika, Akhil Kaushik . Pandemic Perspectives: A Review of Sentiment Analysis Approaches on COVID-19. International Journal of Computer Applications. 187, 21 ( Jul 2025), 1-12. DOI=10.5120/ijca2025924091

@article{ 10.5120/ijca2025924091,
author = { Satvika, Akhil Kaushik },
title = { Pandemic Perspectives: A Review of Sentiment Analysis Approaches on COVID-19 },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 21 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number21/pandemic-perspectives-a-review-of-sentiment-analysis-approaches-on-covid-19/ },
doi = { 10.5120/ijca2025924091 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-26T00:55:56.385620+05:30
%A Satvika
%A Akhil Kaushik
%T Pandemic Perspectives: A Review of Sentiment Analysis Approaches on COVID-19
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 21
%P 1-12
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The social media has enabled the mundane man to raise his awareness, concern and voice for and against any issue. The social networking platforms empowered by cellphones have given tremendous rise in the involvement of people in countless affairs. During the lockdown and later periods, mostly persons have given their opinions regarding myriad topics related to COVID-19 like vaccination, masks, social distancing, etc. Many articles have been published on sentiment analysis related to this deadly virus. A standard systematic literature review is conducted in this paper, where multiple research articles on given subject are collected and analyzed. This paper discusses the chief findings in the examined papers, highlights the key points and conducts a critical assessment.

References
  1. Zanke, A. A., Thenge, R. R., & Adhao, V. S. (2020). COVID-19: A pandemic declare by world health organization. IP International Journal of Comprehensive and Advanced Pharmacology, 5(2), 49-57.
  2. Chen, T. (2021). Fomites and the COVID-19 pandemic: An evidence review on its role in viral transmission. National Collaborating Centre for Environmental Health: Vancouver, BC, Canada, 1-24.
  3. Madabhavi, I., Sarkar, M., & Kadakol, N. (2020). COVID-19: a review. Monaldi Archives for Chest Disease, 90(2).
  4. Web Desk, "X-rays size up coronavirus protein structure at room temperature", Article Date: 30 Jun., 2020, Access Date: 31 Oct., 2024, An online article available on https://www.theweek.in/news/health/2020/06/30/xrays-size-up-coronavirus-structure-at-room-temperature.html.
  5. Shapiro, L. C., Thakkar, A., Campbell, S. T., Forest, S. K., Pradhan, K., Gonzalez-Lugo, J. D., ... & Halmos, B. (2022). Efficacy of booster doses in augmenting waning immune responses to COVID-19 vaccine in patients with cancer. Cancer Cell, 40(1), 3-5.
  6. Auxier, B., & Anderson, M. (2021). Social media use in 2021. Pew Research Center, 1(1), 1-4.
  7. Kang, H., Wang, Y., Wang, M., Al Imran Yasin, M., Osman, M. N., & Ang, L. H. (2023). Navigating digital network: Mindfulness as a shield against cyberbullying in the knowledge economy era. Journal of the Knowledge Economy, 1-39.
  8. Alam, S., & Yao, N. (2019). The impact of preprocessing steps on the accuracy of machine learning algorithms in sentiment analysis. Computational and Mathematical Organization Theory, 25, 319-335.
  9. SA image - Devendra, "Sentiment Analysis with NLP & Deep Learning", Article Date: 24 Feb., 2022, Access Date: 13 Nov., 2024, An online article available on https://www.analyticsvidhya.com/blog/2022/02/sentiment-analysis-with-nlp-deep-learning/.
  10. Sadia, A., Khan, F., & Bashir, F. (2018, February). An overview of lexicon-based approach for sentiment analysis. In 2018 3rd International Electrical Engineering Conference (IEEC 2018) (pp. 1-6).
  11. Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).
  12. Tsoy, D., Tirasawasdichai, T., & Kurpayanidi, K. I. (2021). Role of social media in shaping public risk perception during COVID-19 pandemic: A theoretical review. International Journal of Management Science and Business Administration, 7(2), 35-41.
  13. Ghosh, S. K. (2022). MASK AS A FASHION PRODUCT: A CULTURAL STUDY OF COVID-19 IN INDIA. Society, Pedagogy, Politics: A Multidimensional Approach to COVID-19, 130.
  14. Bing Liu, "Sentiment Analysis: mining sentiments, opinions, and emotions", 2nd edition, Cambridge University Press, 2020.
  15. Păvăloaia, V. D., Teodor, E. M., Fotache, D., & Danileţ, M. (2019). Opinion mining on social media data: sentiment analysis of user preferences. Sustainability, 11(16), 4459.
  16. Sánchez-Núñez, P., Cobo, M. J., De Las Heras-Pedrosa, C., Pelaez, J. I., & Herrera-Viedma, E. (2020). Opinion mining, sentiment analysis and emotion understanding in advertising: a bibliometric analysis. IEEE Access, 8, 134563-134576.
  17. Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023). Sentiment analysis and opinion mining on educational data: A survey. Natural Language Processing Journal, 2, 100003.
  18. Soong, H. C., Jalil, N. B. A., Ayyasamy, R. K., & Akbar, R. (2019, April). The essential of sentiment analysis and opinion mining in social media: Introduction and survey of the recent approaches and techniques. In 2019 IEEE 9th symposium on computer applications & industrial electronics (ISCAIE) (pp. 272-277). IEEE.
  19. Stefanis, C., Giorgi, E., Kalentzis, K., Tselemponis, A., Nena, E., Tsigalou, C., ... & Bezirtzoglou, E. (2023). Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models. Frontiers in Public Health, 11, 1191730.
  20. Iwendi, C., Mohan, S., Ibeke, E., Ahmadian, A., & Ciano, T. (2022). Covid-19 fake news sentiment analysis. Computers and electrical engineering, 101, 107967.
  21. Dubey, A. D. (2020). Twitter sentiment analysis during COVID-19 outbreak. Available at SSRN 3572023.
  22. Manguri, K. H., Ramadhan, R. N., & Amin, P. R. M. (2020). Twitter sentiment analysis on worldwide COVID-19 outbreaks. Kurdistan Journal of Applied Research, 54-65.
  23. Nemes, L., & Kiss, A. (2021). Social media sentiment analysis based on COVID-19. Journal of Information and Telecommunication, 5(1), 1-15.
  24. Gupta, P., Kumar, S., Suman, R. R., & Kumar, V. (2020). Sentiment analysis of lockdown in india during covid-19: A case study on twitter. IEEE Transactions on Computational Social Systems, 8(4), 992-1002.
  25. de Las Heras-Pedrosa, C., Sánchez-Núñez, P., & Peláez, J. I. (2020). Sentiment analysis and emotion understanding during the COVID-19 pandemic in Spain and its impact on digital ecosystems. International journal of environmental research and public health, 17(15), 5542.
  26. Boon-Itt, S., & Skunkan, Y. (2020). Public perception of the COVID-19 pandemic on Twitter: sentiment analysis and topic modeling study. JMIR public health and surveillance, 6(4), e21978.
  27. Al-Shabi, M. (2020). Evaluating the performance of the most important Lexicons used to Sentiment analysis and opinions Mining. IJCSNS, 20(1), 1.
  28. Pastor, C. K. Sentiment analysis of Filipinos and effects of extreme community quarantine due to coronavirus (COVID-19) pandemic. 2020. Available at SSRN.
  29. Imran, A. S., Daudpota, S. M., Kastrati, Z., & Batra, R. (2020). Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on COVID-19 related tweets. Ieee Access, 8, 181074-181090.
  30. Aljameel, S. S., Alabbad, D. A., Alzahrani, N. A., Alqarni, S. M., Alamoudi, F. A., Babili, L. M., ... & Alshamrani, F. M. (2021). A sentiment analysis approach to predict an individual’s awareness of the precautionary procedures to prevent COVID-19 outbreaks in Saudi Arabia. International journal of environmental research and public health, 18(1), 218.
  31. Sitaula, C., Basnet, A., Mainali, A., & Shahi, T. B. (2021). Deep Learning‐Based Methods for Sentiment Analysis on Nepali COVID‐19‐Related Tweets. Computational Intelligence and Neuroscience, 2021(1), 2158184.
  32. Yin, H., Song, X., Yang, S., & Li, J. (2022). Sentiment analysis and topic modeling for COVID-19 vaccine discussions. World Wide Web, 25(3), 1067-1083.
  33. Singh, C., Imam, T., Wibowo, S., & Grandhi, S. (2022). A deep learning approach for sentiment analysis of COVID-19 reviews. Applied Sciences, 12(8), 3709.
  34. Shofiya, C., & Abidi, S. (2021). Sentiment analysis on COVID-19-related social distancing in Canada using Twitter data. International Journal of Environmental Research and Public Health, 18(11), 5993.
  35. Sanders, A. C., White, R. C., Severson, L. S., Ma, R., McQueen, R., Paulo, H. C. A., ... & Bennett, K. P. (2021). Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse. AMIA Summits on Translational Science Proceedings, 2021, 555.
  36. Kausar, M. A., Soosaimanickam, A., & Nasar, M. (2021). Public sentiment analysis on Twitter data during COVID-19 outbreak. International Journal of Advanced Computer Science and Applications, 12(2).
  37. Chandra, R., & Krishna, A. (2021). COVID-19 sentiment analysis via deep learning during the rise of novel cases. PloS one, 16(8), e0255615.
  38. Mujahid, M., Lee, E., Rustam, F., Washington, P. B., Ullah, S., Reshi, A. A., & Ashraf, I. (2021). Sentiment analysis and topic modeling on tweets about online education during COVID-19. Applied Sciences, 11(18), 8438.
  39. Lyu, J. C., Han, E. L., & Luli, G. K. (2021). COVID-19 vaccine–related discussion on Twitter: topic modeling and sentiment analysis. Journal of medical Internet research, 23(6), e24435.
  40. Liu, S., & Liu, J. (2021). Public attitudes toward COVID-19 vaccines on English-language Twitter: A sentiment analysis. Vaccine, 39(39), 5499-5505.
  41. Marcec, R., & Likic, R. (2022). Using twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines. Postgraduate medical journal, 98(1161), 544-550.
  42. Nezhad, Z. B., & Deihimi, M. A. (2022). Twitter sentiment analysis from Iran about COVID 19 vaccine. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 16(1), 102367.
  43. Pristiyono, Ritonga, M., Ihsan, M. A. A., Anjar, A., & Rambe, F. H. (2021, February). Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm. In IOP Conference Series: Materials Science and Engineering (Vol. 1088, No. 1, p. 012045). IOP Publishing.
  44. Rahman, M. M., & Islam, M. N. (2022). Exploring the performance of ensemble machine learning classifiers for sentiment analysis of COVID-19 tweets. In Sentimental Analysis and Deep Learning: Proceedings of ICSADL 2021 (pp. 383-396). Springer Singapore.
  45. Basiri, M. E., Nemati, S., Abdar, M., Asadi, S., & Acharrya, U. R. (2021). A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems, 228, 107242.
  46. Chintalapudi, N., Battineni, G., & Amenta, F. (2021). Sentimental analysis of COVID-19 tweets using deep learning models. Infectious disease reports, 13(2), 329-339.
  47. Kaur, H., Ahsaan, S. U., Alankar, B., & Chang, V. (2021). A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets. Information Systems Frontiers, 23(6), 1417-1429.
  48. Naseem, U., Razzak, I., Khushi, M., Eklund, P. W., & Kim, J. (2021). COVIDSenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis. IEEE transactions on computational social systems, 8(4), 1003-1015.
  49. Dangi, D., Dixit, D. K., & Bhagat, A. (2022). Sentiment analysis of COVID-19 social media data through machine learning. Multimedia tools and applications, 81(29), 42261-42283.
  50. Qorib, M., Oladunni, T., Denis, M., Ososanya, E., & Cotae, P. (2023). Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset. Expert Systems with Applications, 212, 118715.
  51. Braig, N., Benz, A., Voth, S., Breitenbach, J., & Buettner, R. (2023). Machine learning techniques for sentiment analysis of COVID-19-related twitter data. IEEE Access, 11, 14778-14803.
  52. Joloudari, J. H., Hussain, S., Nematollahi, M. A., Bagheri, R., Fazl, F., Alizadehsani, R., ... & Talukder, A. (2023). BERT-deep CNN: State of the art for sentiment analysis of COVID-19 tweets. Social Network Analysis and Mining, 13(1), 99.
  53. Storey, V. C., & O’Leary, D. E. (2024). Text analysis of evolving emotions and sentiments in COVID-19 Twitter communication. Cognitive Computation, 16(4), 1834-1857.
  54. Abiola, O., Abayomi-Alli, A., Tale, O. A., Misra, S., & Abayomi-Alli, O. (2023). Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser. Journal of Electrical Systems and Information Technology, 10(1), 5.
  55. Ainapure, B. S., Pise, R. N., Reddy, P., Appasani, B., Srinivasulu, A., Khan, M. S., & Bizon, N. (2023). Sentiment analysis of COVID-19 tweets using deep learning and lexicon-based approaches. Sustainability, 15(3), 2573.
  56. Sandu, A., Cotfas, L. A., Delcea, C., Crăciun, L., & Molănescu, A. G. (2023). Sentiment Analysis in the Age of COVID-19: A Bibliometric Perspective. Information, 14(12), 659.
  57. J Mir, A. A., & Sevukan, R. (2024). Sentiment analysis of Indian Tweets about Covid-19 vaccines. Journal of Information Science, 50(5), 1308-1320.
  58. Alqarni, A., & Rahman, A. (2023). Arabic tweets-based sentiment analysis to investigate the impact of COVID-19 in KSA: a deep learning approach. Big Data and Cognitive Computing, 7(1), 16.
  59. Thakur, N. (2023). Sentiment analysis and text analysis of the public discourse on Twitter about COVID-19 and MPox. Big Data and Cognitive Computing, 7(2), 116.
  60. Catelli, R., Pelosi, S., Comito, C., Pizzuti, C., & Esposito, M. (2023). Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy. Computers in Biology and Medicine, 158, 106876.
  61. Ariff, M. I. M., Zubir, N. E. S., Azizan, A., Ahmad, S., & Arshad, N. I. (2024). Malaysian views on COVID-19 vaccination program: a sentiment analysis study using Twitter. Bulletin of Electrical Engineering and Informatics, 13(1), 436-443.
  62. Xiong, J., Feng, M., Wang, X., Jiang, C., Zhang, N., & Zhao, Z. (2024). Decoding sentiments: Enhancing covid-19 tweet analysis through bert-rcnn fusion. Journal of Theory and Practice of Engineering Science, 4(01), 86-93.
  63. Park, B., Jang, I. S., & Kwak, D. (2024). Sentiment analysis of the COVID-19 vaccine perception. Health Informatics Journal, 30(1), 14604582241236131.
  64. Ahammad, T. (2024). Identifying hidden patterns of fake COVID-19 news: An in-depth sentiment analysis and topic modeling approach. Natural Language Processing Journal, 6, 100053.
  65. Chen, L., & Xu, N. (2024). To live or to stay alive? A thematic and sentiment analysis of public posts on social media during the 2022 Shanghai COVID-19 outbreak. Digital Health, 20552076241288731.
Index Terms

Computer Science
Information Sciences

Keywords

COVID-19 Coronavirus Disease Pandemic Sentiment Analysis