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An Innovative Sentiment Analysis Model for COVID-19 Tweets

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Numerical Computations: Theory and Algorithms (NUMTA 2023)

Abstract

Worldwide health problems and feelings of worry and anxiety have been brought on by COVID-19, a terrible pandemic that the WHO has declared. Sentiment analysis is a vital technique for figuring out how people are responding to the pandemic. In order to find the most pertinent and instructive aspects of the embeddings, the research suggests a novel sentiment analysis model for COVID-19 tweets using CT-BERT as a base model and MAX Pooling function on the last four layers. The generated embeddings are joined with the classification (CLS) token before being sent to a classifier, which generates a probability distribution across all potential classes. The proposed technique acquired 91 and 92 % accuracy, 93 and 94% recall and 90 and 92% F-measure for positive and negative sentiment classification respectively. Insights into public sentiment and emotions have been gained during COVID-19 that have been useful for informing decision-making and communication tactics.

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Correspondence to Areeba Umair .

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Umair, A., Masciari, E. (2025). An Innovative Sentiment Analysis Model for COVID-19 Tweets. In: Sergeyev, Y.D., Kvasov, D.E., Astorino, A. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2023. Lecture Notes in Computer Science, vol 14478. Springer, Cham. https://doi.org/10.1007/978-3-031-81247-7_34

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  • DOI: https://doi.org/10.1007/978-3-031-81247-7_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-81246-0

  • Online ISBN: 978-3-031-81247-7

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