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• W4319316173 abstract "Social media are the influential Internet community for general netizens to disseminate information, views, and opinions extensively. Recently, the research on Sentiment analysis has intensified due to the vast amount of data obtained from these numerous social networking platforms. During this COVID-19 pandemic, one of the great social networking sites viz. Twitter has experienced a significant increase in online posts and reviews concerning COVID-19 related tweets. In the view of Twitter, the spread of news concerning coronavirus variants has impacted many aspects of the public by providing messages around different issues and opinions to its potential users. In this study, we have used Twitter as a source of data for searching the index keywords and hashtag versions for the terms like “Pandemic”, “Coronavirus”, “COVID-19”, “SARS-CoV-2”, and “Omicron”, and we have examined the psychological and emotional impact it had on the public. This paper provides analytical information about people’s perceptions of coronavirus (tweets were collected from March 2020 to December 2021). With those tweets, sentiment analysis was performed using the TextBlob text processing python module to acquire the people’s subjective data (opinions and feelings) polarity concerning the effects of coronavirus. Furthermore, for effective text classification, we have applied classification methodologies like Support Vector Machine (SVM), K Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT). The SVM concerning feature extraction produced the results with 99.49% accuracy. This study assists the government organizations in attaining COVID-19 insights by implying public mental health sentiments in social networks." @default.
• W4319316173 created "2023-02-08" @default.
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• W4319316173 date "2023-01-01" @default.
• W4319316173 modified "2023-09-27" @default.
• W4319316173 title "Sentiment Analysis of COVID-19 Tweets Using TextBlob and Machine Learning Classifiers" @default.
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• W4319316173 doi "https://doi.org/10.1007/978-981-19-6634-7_8" @default.
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