Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets

Yassine Drias, Habiba Drias

University of Algiers, Algiers, Algeria, USTHB – LRIA, Algiers, Algeria

Cite: Drias Y., Drias H. Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets. J. Digit. Art Humanit., 2(2), 3-21.

Abstract. This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions between users on topics related to the pandemic. The third contribution is a sentiment analysis, which explores the evolution of emotions throughout time, while the fourth one is an association rule mining task. The indicators determined by statistics and the results obtained from sentiment analysis and association rule mining are eloquent. For instance, signs of an upcoming worldwide economic crisis were clearly detected at an early stage in this study. Overall results are promising and can be exploited in the prediction of the aftermath of COVID-19 and similar crisis in the future.

Keywords: COVID-19, Twitter Dataset, Tweets Analytics, Sentiment Analysis, Sentiment Evolution, Data Mining, Association Rule Mining, FP-growth.


  1. Z. Wu and  J.M. McGoogan, “Characteristics of and important lessons from the coronavirus disease (COVID-19) outbreak in China: Summary of a Report of 72314 cases from the Chinese center for disease control and prevention”.  JAMA. (2020).
  2. J. Li and X. Guo, “Global Deployment Mappings and Challenges of Contact-tracing Apps for COVID-19”, SSRN Electronic Journal, (2020).
  3. D.N. Maxwell, T.M. Perl and J.B. Cutrell, “The art of war in the era of coronavirus disease (COVID-19)”, Clinical Infectious Diseases, ciaa229, (2019).
  4. D.R. Bild, Y. Liu, R.P. Dick and Z. Morley Mao, “Aggregate characterization of user behavior in Twitter and analysis of the retweet graph”, ACM Transactions on Internet Technology (TOIT), vol. 15, no 4, (2015).
  5. S.S. Ercetin and N.B. Neyisci, “Social network analysis: A brief introduction to the theory”, In: Ercetin S. (eds) Chaos, Complexity and Leadership, Springer Proceedings in Complexity, Springer, Cham, 167-171, (2014).
  6. Q. Yan, L. Wu and L. Zheng, “Social network based microblog user behavior analysis. Physica A: Statistical mechanics and its applications”, 7(392), 1712-1723, (2013).
  7. T.D. Baruah, “Effectiveness of social media as a tool of communication and its potential for technology enabled connections: A micro-level study”. International Journal of Scientific and Research Publications, 2(5), pp: 1-10, (2012).
  8. F. A. Pozzi, E. Fersini, E. Messina and B. Liu, B, “Sentiment analysis in social media”. Morgan Kaufmann, (2016).
  9. K.S. Houtan, T. Gagne, C.N. Jenkins and L. Joppa, “Sentiment analysis of conservation studies captures successes of species reintroductions”. Patterns 1, 100005, (2020).
  10. M. Thelwall, K. Buckley and G. Paltoglou, “Sentiment strength detection for the social web”. JASIST, 63(1); pp:163-173, (2012).
  11. B. Liu, “Sentiment analysis and subjectivity”. Handbook of Natural Language Processing, 2nd edition, (2010).
  12. X. Guo and J. Li, “A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency”. International Conference on Social Networks Analysis, Management and Security, (2019).
  13. W. Medhat, A. Hassan and H. Korashy, “Sentiment analysis algorithms and applications: A survey”, Ain Shams Engineering Journal, 5(4), Elsevier, (2014).
  14. J. Han, J. Pei and M. Kamber, “Data mining: concepts and techniques”. Elsevier, (2011).
  15. X. Wu, X. Zhu and G. Wu, “Data mining with big data”, IEEE transactions on knowledge and data engineering, 1(26); pp:97-107, (2013).
  16. K. Heraguemi, N. Kamel and H. Drias, “Association Rule Mining Based on Bat Algorithm”, Bio-Inspired Computing – Theories and Applications, Springer, (2014).
  17. C.C. Aggarwal, A.B. Mansurul and A.H. Mohammad, “Frequent pattern mining algorithms: A survey”. Springer, Cham; pp:19-64, (2014).
  18. P. Fournier-Viger, J.C.W. Lin, B. Vo, T.T.  Chi, J. Zhang and H.B. Le, “A Survey of itemset mining”, WIREs data mining and knowledge discovery, Wiley, (2017).
  19. H. Drias, C. Hireche and A. Douib, “Datamining techniques and swarm intelligence for problem solving: Application to SAT”. World Congress on Nature and Biologically Inspired Computing, NaBIC, (2013).
  20. Y. Drias and G. Pasi, “Credible Information Foraging on Social Media”, Trends and Innovations in Information Systems and Technologies, Advances in Intelligent Systems and Computing, vol 1159 Springer, (2020).
  21. C. Timberg and E. Dwoskin, “Twitter is sweeping out fake accounts like never before, putting user growth at risk”, The Washington Post, July 6, 2018, (2018).
  22. M. Kearney, “Tweetbotornot: Detecting Twitter bots”. web app:,(2018).
  23. Neviarouskaya, H. Prendinger and M. Ishizuka, “Sentiful: A lexicon for sentiment analysis”, IEEE Transactions on Affective Computing, 2; pp:22-36, (2011).
  24. Y. Drias and H. Drias, “COVID-19 Tweets: A dataset contaning more than 600k tweets on the novel Coronavirus (Version 1.0) [Data set]”, Zenodo, (2020).
  25. Z. Wood-Doughty, P. Mahajan, M. Dredze, and J. Hopkins, “Classifying Individuals versus Organizations on Twitter”, Proceedings of the Second Workshop on Computational Modeling of People Opinions, Personality, and Emotions in Social Media, pages 56-61 New Orleans, Louisiana, (2018).

Published online 29.12.2021