Abstract
An increased usage of social media was observed as the Covid-19 pandemic progressed. We extract tweets relevant to the pandemic from publicly available Twitter data and analyse them to understand the change in public emotions over the year along with a detailed analysis of the topics of discussion. We find public health and politics to be the two most dominant topics. Hence, we perform a study where we compare the performance of existing unsupervised classification methods for the task of detecting whether a tweet is medically relevant or politically motivated.
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Mukherjee, A., Bhattacharyya, S., Ray, K., Gupta, B., Das, A.K. (2022). A Study of Public Sentiment and Influence of Politics in COVID-19 Related Tweets. In: Das, A.K., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition . Advances in Intelligent Systems and Computing, vol 1349. Springer, Singapore. https://doi.org/10.1007/978-981-16-2543-5_56
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DOI: https://doi.org/10.1007/978-981-16-2543-5_56
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