Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework
Received: 22 February 2022 | Revised: 16 March 2022 | Accepted: 25 March 2022 | Online: 14 April 2022
While different variants of COVID-19 dramatically affected the lives of millions of people across the globe, a new version of COVID-19, "SARS-CoV-2 Omicron," emerged. This paper analyzes the public attitude and sentiment towards the emergence of the SARS-CoV-2 Omicron variant on Twitter. The proposed approach relies on the text analytics of Twitter data considering tweets, retweets, and hashtags' main themes, the pandemic restriction, the efficacy of covid-19 vaccines, transmissible variants, and the surge of infection. A total of 18,737 tweets were pulled via Twitter Application Programming Interface (API) from December 3, 2021, to December 26, 2021, using the SentiStrength software that employs a lexicon of sentiment terms and a set of linguistic rules. The analysis was conducted to distinguish and codify subjective content and estimate the strength of positive and negative sentiment with an average of 95% confidence intervals based upon emotion strength scales of 1-5. It is found that negativity was dominated after the outbreak of Omicron and scored 31.01% for weak, 16.32% for moderate, 5.36% for strong, and 0.35% for very strong sentiment strength. In contrast, positivity decreased gradually and scored 16.48% for weak, 11.19% for moderate, 0.80% for strong, and 0.04% for very strong sentiment strength. Identifying the public emotional status would help the concerned authorities to provide appropriate strategies and communications to relieve public worries about pandemics.
Keywords:sentiment analysis, social media, SARS-CoV-2 Omicron, Twitter, text analytics, data mining
M. S. Neethu and R. Rajasree, "Sentiment analysis in twitter using machine learning techniques," in Fourth International Conference on Computing, Communications and Networking Technologies, Tiruchengode, India, Jul. 2013, pp. 1–5. DOI: https://doi.org/10.1109/ICCCNT.2013.6726818
P. Baid, A. Gupta, and N. Chaplot, "Sentiment Analysis of Movie Reviews using Machine Learning Techniques," International Journal of Computer Applications, vol. 179, no. 7, pp. 45–49, Dec. 2017. DOI: https://doi.org/10.5120/ijca2017916005
M. Mahyoob, "Online Learning Effectiveness During the COVID-19 Pandemic: A Case Study of Saudi Universities," International Journal of Information and Communication Technology Education, vol. 17, no. 4, pp. 1–14, Oct. 2021. DOI: https://doi.org/10.4018/IJICTE.20211001.oa7
X. He, W. Hong, X. Pan, G. Lu, and X. Wei, "SARS-CoV-2 Omicron variant: Characteristics and prevention," MedComm, vol. 2, no. 4, pp. 838–845, 2021. DOI: https://doi.org/10.1002/mco2.110
"GISAID - hCov19 Variants." https://www.gisaid.org/hcov19-variants/ (accessed Mar. 28, 2022).
A. D. Dubey, Twitter Sentiment Analysis during COVID19 Outbreak. Jaipur, India: Jaipuria Institute of Management, 2020. DOI: https://doi.org/10.2139/ssrn.3572023
N. A. Khan, H. Al-Thani, and A. El-Menyar, "The emergence of new SARS-CoV-2 variant (Omicron) and increasing calls for COVID-19 vaccine boosters-The debate continues," Travel Medicine and Infectious Disease, vol. 45, 2022, Art. no. 102246. DOI: https://doi.org/10.1016/j.tmaid.2021.102246
W. Fan and M. D. Gordon, "The power of social media analytics," Communications of the ACM, vol. 57, no. 6, pp. 74–81, Mar. 2014. DOI: https://doi.org/10.1145/2602574
Fornacciari, M. Mordonini, and M. Tomauiolo, Social Network and Sentiment Analysis on Twitter: Towards a Combined Approach. Parma, Italy: University of Parma, 2015.
M. Madhukar and S. Verma, "Hybrid Semantic Analysis of Tweets: A Case Study of Tweets on Girl-Child in India," Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2014–2016, Oct. 2017. DOI: https://doi.org/10.48084/etasr.1246
U. P. Gurav and S. Kotrappa, "Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6356–6361, Oct. 2020. DOI: https://doi.org/10.48084/etasr.3805
I. A. Kandhro, S. Z. Jumani, F. Ali, Z. U. Shaikh, M. A. Arain, and A. A. Shaikh, "Performance Analysis of Hyperparameters on a Sentiment Analysis Model," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6016–6020, Aug. 2020. DOI: https://doi.org/10.48084/etasr.3549
H. Yin, S. Yang, and J. Li, "Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media," in 16th International Conference on Advanced Data Mining and Applications, Foshan , China, Dec. 2020, pp. 610–623. DOI: https://doi.org/10.1007/978-3-030-65390-3_46
Md. S. Satu et al., "TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets," Knowledge-Based Systems, vol. 226, Aug. 2021, Art. no. 107126. DOI: https://doi.org/10.1016/j.knosys.2021.107126
K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, "Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media," Applied Soft Computing, vol. 97, Dec. 2020, Art. no. 106754. DOI: https://doi.org/10.1016/j.asoc.2020.106754
N. Andrews et al., "Effectiveness of COVID-19 vaccines against the Omicron (B.1.1.529) variant of concern." medRxiv, pp. 1–16, Dec. 14, 2021.
N. Imtiaz Khan, T. Mahmud, and M. Nazrul Islam, "COVID-19 and black fungus: Analysis of the public perceptions through machine learning," Engineering Reports, 2021, Art. no. e12475. DOI: https://doi.org/10.1002/eng2.12475
S. Rao and M. Singh, "The Newly Detected B.1.1.529 (Omicron) Variant of SARS-CoV-2 With Multiple Mutations," DHR Proceedings, vol. 1, pp. 7–10, 2021. DOI: https://doi.org/10.47488/dhrp.v1iS5.35
P. T. Damavandi, "The Omicron variant and potential predictions of secondary infections," Nov. 2021.
M. G. T. Akbar and D. B. Srisulistiowati, "Analisa Sentimen Efektifitas Vaksin terhadap Varian COVID 19 Omicron Berbasis Leksikon," Journal of Information and Information Security, vol. 2, no. 2, pp. 251–258, Dec. 2021.
D. Giuliani, M. M. Dickson, G. Espa, and F. Santi, "Modelling and predicting the spatio-temporal spread of COVID-19 in Italy," BMC Infectious Diseases, vol. 20, no. 1, Sep. 2020, Art. no. 700. DOI: https://doi.org/10.1186/s12879-020-05415-7
M. Thelwall and A. Mas-Bleda, "YouTube science channel video presenters and comments: female friendly or vestiges of sexism?," Aslib Journal of Information Management, vol. 70, no. 1, pp. 28–46, Jan. 2018. DOI: https://doi.org/10.1108/AJIM-09-2017-0204
M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas, "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, vol. 61, no. 12, pp. 2544–2558, 2010. DOI: https://doi.org/10.1002/asi.21416
J. Algaraady and M. Mahyoob, "Public Sentiment Analysis in Social Media on the SARS-CoV-2 Vaccination Using VADER Lexicon Polarity," Humanities and Educational Sciences Journal, no. 22, pp. 591–609, Mar. 2022. DOI: https://doi.org/10.55074/2152-000-022-021
L. T. Brandal et al., "Outbreak caused by the SARS-CoV-2 Omicron variant in Norway, November to December 2021," Eurosurveillance, vol. 26, no. 50, Dec. 2021, Art. no. 2101147. DOI: https://doi.org/10.2807/1560-7917.ES.2021.26.50.2101147
X. Liu et al., "Public mental health problems during COVID-19 pandemic: a large-scale meta-analysis of the evidence," Translational Psychiatry, vol. 11, no. 1, pp. 1–10, Jul. 2021. DOI: https://doi.org/10.1038/s41398-021-01501-9
Y. Yang, W. Li, Q. Zhang, L. Zhang, T. Cheung, and Y.-T. Xiang, "Mental health services for older adults in China during the COVID-19 outbreak," The Lancet Psychiatry, vol. 7, no. 4, Apr. 2020, Art. no. e19. DOI: https://doi.org/10.1016/S2215-0366(20)30079-1
Z. Su, D. McDonnell, J. Ahmad, A. Cheshmehzangi, and Y.-T. Xiang, "Mind the ‘worry fatigue’ amid Omicron scares," Brain, Behavior, and Immunity, vol. 101, pp. 60–61, Mar. 2022. DOI: https://doi.org/10.1016/j.bbi.2021.12.023
How to Cite
MetricsAbstract Views: 226
PDF Downloads: 127
Copyright (c) 2022 M. Mahyoob, J. Algaraady, M. Alrahiali, A. Alblwi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.