Abstract
With the rise in patients affected by the coronavirus, the World Health Organization declared it a pandemic. Globally, people are forced to stay at home to maintain social distancing. People are sharing their feelings through social media platform like Twitter. Twitter data helps to understand grief and pain due to coronavirus. In this paper, a Twitter dataset has been used for sentiment analysis of people's opinions related to coronavirus (COVID-19) that is a vital issue these days all over the world, and various countries are affected by this pandemic. So analyze the people sentiment regarding this pandemic using machine learning techniques and sentiment analysis model such as Naive Bayes, Support Vector Machine, Logistic Regression, and Random Forest Classifier has been proposed to analyze sentiment more effectively. Further, a comparison of these models has been done to prove extremely effective and accurate based on the analysis of feelings and opinions regarding coronavirus (COVID-19).
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Kaur, C., Sharma, A. (2021). COVID-19 Sentimental Analysis Using Machine Learning Techniques. In: Panigrahi, C.R., Pati, B., Pattanayak, B.K., Amic, S., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1299. Springer, Singapore. https://doi.org/10.1007/978-981-33-4299-6_13
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DOI: https://doi.org/10.1007/978-981-33-4299-6_13
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