Abstract
In response to the COVID-19 pandemic, governments around the world are taking a wide range of measures. Previous research on COVID-19 has focused on disease spreading, epidemic curves, measures to contain it, confirmed cases, and deaths. In this work, we sought to explore another essential aspect of this pandemic, how do people feel and react to this reality and the impact on their emotional well-being. For that reason, we propose using epidemic indicators and government policy responses to estimate the sentiment, as this is expressed on Twitter. We develop a nowcasting approach that exploits the time series of epidemic indicators and the measures taken in response to the COVID-19 outbreak in the United States of America to predict the public sentiment at a daily frequency. Using machine learning models, we improve the short-term forecasting accuracy of autoregressive models, revealing the value of incorporating the additional data in the predictive models. We then provide explanations to the indicators and measures that drive the predictions for specific dates. Our work provides evidence that data about the way COVID-19 evolves along with the measures taken in response to the COVID-19 outbreak can be used effectively to improve sentiment nowcasting and gain insights into people’s current emotional state.
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References
Blavatnik School of Government, University of Oxford: Covid-19 government response tracker (2021). https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker#data. Accessed June 2021
Cobey, S.: Modeling infectious disease dynamics. Science 368(6492), 713–714 (2020)
Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440–8451, July 2020
De Myttenaere, A., Golden, B., Le Grand, B., Rossi, F.: Mean absolute percentage error for regression models. Neurocomputing 192, 38–48 (2016)
Della Penna, N., Huang, H., et al.: Constructing consumer sentiment index for us using google searches. Technical report (2010)
Deng, J., Ren, F.: A survey of textual emotion recognition and its challenges. IEEE Trans. Affect. Comput. (2021)
Duncan, B., Elkan, C.: Nowcasting with numerous candidate predictors. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 370–385. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44848-9_24
Gismero-Gonzalez, E., Bermejo-Toro, L., Cagigal, V., Roldan, A., Martinez-Beltran, M.J., Halty, L.: Emotional impact of COVID-19 lockdown among the Spanish population. Front. Psychol. 11, 3634 (2020)
Hale, T., et al.: A global panel database of pandemic policies (oxford COVID-19 government response tracker). Nat. Hum. Behav. 5(4), 529–538 (2021)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning. STS, vol. 103. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-7138-7
Kabir, Y., Madria, S.: EMOCOV: machine learning for emotion detection, analysis and visualization using COVID-19 tweets. Online Soc. Netw. Media 23, 100135 (2021)
Kompella, V., et al.: Reinforcement learning for optimization of COVID-19 mitigation policies. arXiv preprint arXiv:2010.10560 (2020)
Kwak, G.H., Ling, L., Hui, P.: Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic. Plos One 16(5), e0251550 (2021)
Lansdall-Welfare, T., Lampos, V., Cristianini, N.: Nowcasting the mood of the nation. Significance 9(4), 26–28 (2012)
Mohammad, S., Bravo-Marquez, F., Salameh, M., Kiritchenko, S.: SemEval-2018 task 1: affect in tweets. In: Proceedings of The 12th International Workshop on Semantic Evaluation, New Orleans, Louisiana, pp. 1–17. Association for Computational Linguistics, June 2018
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
Richard, Q., Alizon, S., Choisy, M., Sofonea, M.T., Djidjou-Demasse, R.: Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic. PLOS Comput. Biol. 17(3), 1–25 (2021). https://doi.org/10.1371/journal.pcbi.1008776
Ritchie, H., et al.: Coronavirus pandemic (COVID-19). Our World in Data (2020)
Tolles, J., Luong, T.: Modeling epidemics with compartmental models. Jama 323(24), 2515–2516 (2020)
Tsakalidis, A., Liakata, M., Damoulas, T., Jellinek, B., Guo, W., Cristea, A.: Combining heterogeneous user generated data to sense well-being. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3007–3018 (2016)
Yang, Q., et al.: SenWave: monitoring the global sentiments under the COVID-19 pandemic. JMIR Public Health Surveill. 6, e19447 (2020). https://doi.org/10.2196/19447
Yue, L., Chen, W., Li, X., Zuo, W., Yin, M.: A survey of sentiment analysis in social media. Knowl. Inf. Syst. 60(2), 617–663 (2019). https://doi.org/10.1007/s10115-018-1236-4
Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(4), e1253 (2018)
Acknowledgements
The work of IM and PP has been supported in part by the Digital Futures EXTREMUM project titled “Explainable and Ethical Machine Learning for Knowledge Discovery from Medical Data Sources”. The work of PP has also been supported in part by the Vinnova project titled “Artificial Intelligence for Mitigation of Pandemics”.
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Miliou, I., Pavlopoulos, J., Papapetrou, P. (2021). Sentiment Nowcasting During the COVID-19 Pandemic. In: Soares, C., Torgo, L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science(), vol 12986. Springer, Cham. https://doi.org/10.1007/978-3-030-88942-5_17
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