SENTIMENT ANALYSIS OF COVID-19 TWEETS

  • Azeez A. Nureni
  • Victor E. Ogunlusi
  • Emmanuel Junior Uloko
Keywords: Sentiment analysis, Covid-19, Tweet, Algorithms, Dataset, machine learning

Abstract

Sentiment analysis involves techniques used in analyzing texts in order to identify the sentiment and emotion dominant in such texts and classify them accordingly. Techniques involved include but not limited to preprocessing of texts and the use a machine learning or lexical based approach in classifying these texts. In this research, attempt was made to adopt a machine learning approach to classify tweets on Covid-19 which is considered a global pandemic. To achieve this noble objective, a cross-dataset approach was applied to train four machine learning classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB), as well as K-Nearest Neighbors algorithm (KNN). The final result will not only assist us in knowing the best performing algorithm, it will also assist in creating awareness on Covid-19 with the final objective of destigmatizing the patients through the analysis of sentiments and emotions on Covid-19  and finally use the same result for containing the spread of the pandemic

References

Go A., Bhayani R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. Processing, 150.

Lab-Manager. (2020, 03 16). COVID-19: A History of Coronavirus. Retrieved from Lab Manager: https://www.labmanager.com/lab-health-and-safety/covid-19-a-history-of-coronavirus-22021

Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). Learning Word Vectors for Sentiment Analysis. In Proceedings of the 49th Annual
Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 142-150). Portland: Association for Computational Linguistics.

Mäntylä, M. V., Graziotin, D., & Kuutilaa, M. (2018, February). The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16-32.

W.H.O. (2020, 04 17). Q&A on coronaviruses (COVID-19). Retrieved from World Health Organization: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/q-a-coronaviruses

Worldometer. (2020, 05 27). Countries where COVID-19 has spread. Retrieved from worldometer: https://www.worldometers.info/coronavirus/countries-where-coronavirus-has-spread/

Worldometer. (2020, 05 28). Covid-19 Coronavirus Pandemic. Retrieved from Worldometer: https://www.worldometers.info/coronavirus/

Medford, R.J., Saleh, N.S., Sumarsono A, Perl M. T., Lehmann U. C. (2020). An "Infodemic": Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak

Barkur G, Vibha, Kamath GB. Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: Evidence from India. Asian Journal of Psychiatry. 2020 Apr; 51:102089. DOI: 10.1016/j.ajp.2020.102089.

Li, S.; Wang, Y.; Xue, J.; Zhao, N.; Zhu, T. The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. Int. J. Environ. Res. Public Health 2020, 17, 2032.

Dubey, A. D., Twitter Sentiment Analysis during COVID-19 Outbreak (April 9, 2020).

Huang, Chaolin, Yeming Wang, Xingwang Li, Lili Ren, Jianping Zhao, Yi Hu, Li Zhang. "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China." The Lancet 395, no. 10223 (2020): 497-506. 2.

(WHO), World Health Organization. “Preliminary Investigations Conducted by the Chinese Authorities Have Found No Clear Evidence of Human-to-Human Transmission of the Novel #Coronavirus (2019- NCoV) Identified in #Wuhan, #China🇨🇳. Pic.twitter.com/Fnl5P877VG.” Twitter, Twitter, 14 Jan. 2020, twitter.com/WHO/status/1217043229427761152.

NA. Azeez, OE Adio, AW Yekinni and CJ Onyema (2020) " Evaluation of Machine Learning Algorithms for Filtering and Isolating Spammed Messages" FUTA Journal of Research in Sciences, Vol. 16(1), April, 2020: 26-38

N.A. Azeez, S.O. Idiakose, C.J. Onyema, and C.V Vyver (2021) "Cyberbullying Detection in Social Networks: Artificial Intelligence Approach" Journal of Cyber Security and Mobility, Vol. 10 4, 1–30. doi: 10.13052/jcsm2245-1439.1046

N.A Azeez, Ihotu Agbo Margaret, Misra Sanjay (2021) “Adopting Automated White-List (AWL) Approach for Anti-Phishing Solution" Elsevier Journal of Computers & Security 108 (2021) 102328, pp. 1-18

Neppalli, V. K., Caragea, C., Squicciarini, A., Tapia, A., & Stehle, S. (2017). Sentiment analysis during hurricane Sandy in emergency response. International Journal of Disaster Risk Reduction, 21, 213–222.

Gandhe, K., Varde, A. S., & Du, X. (2018). Sentiment analysis of twitter data with hybrid learning for recommender applications. In , 2018. 2018 9th IEEE annual ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON), New York City, NY, USA (pp. 57–63). https://doi.org/10.1109/UEMCON.2018.8796661.

Flores, R. D. (2017). Do anti-immigrant laws shape public sentiment? A study of Arizona’s SB 1070 using twitter data. American Journal of Sociology, 123(2), 333–384.

Dubey, A. D. (2020). Twitter sentiment analysis during COVID19 outbreak. Available at: https://ssrn.com/abstract=3572023.

Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Liu, X., & Zhu, T (2020). Twitter discussions and emotions about COVID-19 pandemic: A machine learning approach. (2020). arXiv:2005.12830.

Samuel, J., Ali, G.G.M.N., Rahman, M.M., Esawi, E., Samuel, Y. (2020) COVID-19 public sentiment insights and machine learning for tweets classification, Information 11 (2020) 314, http://dx.doi.org/10.3390/info11060314, URL: https://www.mdpi.com/2078-2489/11/6/314, number: 6 Publisher: Multidisciplinary Digital Publishing Institute.

Gencoglu, O. (2020) Large-scale, language-agnostic discourse classification of tweets during COVID-19, Mach. Learn. Knowl. Extraction 2 (2020) 603–616, http://dx.doi.org/10.3390/make2040032, URL: https://www.mdpi. com/2504-4990/2/4/32, number: 4 Publisher: Multidisciplinary Digital Publishing Institute.

Al-Rakhami, M.S and Al-Amri, A.M.(2020) Lies kill facts save: Detecting COVID-19 misinformation in Twitter, IEEE Access 8 (2020) 155961–155970, http: //dx.doi.org/10.1109/ACCESS.2020.3019600, conference Name: IEEE Access.

Azeez, N.A, Salaudeen, B.B, Misra, S. Damasevicius, R. Maskeliunas, R. (2019) "Identifying Phishing Attacks in Communication Networks using URL Consistency Features" International Journal of Electronic Security and Digital Forensics (InderScience). https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijesdf
Published
2021-11-02
How to Cite
Nureni, A. A., Ogunlusi, V. E., & Uloko, E. J. (2021). SENTIMENT ANALYSIS OF COVID-19 TWEETS. FUDMA JOURNAL OF SCIENCES, 5(3), 36 - 47. https://doi.org/10.33003/fjs-2021-0503-740

Most read articles by the same author(s)