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- W4320802015 abstract "In recent years, the majority of the world's population has been impacted by the COVID-19 pandemic, but owing to the invention of vaccinations, the epidemic has been brought under control. Most people are hesitant to share their experiences on official platforms after being vaccinated. As a result, information about vaccine-related adverse effects other than clinical trial results is challenging to identify. However, most people have shared their opinions about vaccines on social media since the COVID-19 immunization program began worldwide. This study aims to assess, using social media, the adverse effects of the COVID-19 vaccination as perceived by the general population. The authors of the previous studies did not categorize tweets on the COVID-19 vaccine adverse effects as personal experience, informative, or advice-seeking. The authors of this study aim to classify tweets in the manner described above to fill a research gap and increase public awareness of the COVID-19 vaccine's side effects. The Kaggle repository collected tweets pertaining to COVID-19 vaccinations for this investigation. The authors manually classified collected tweets into two categories: those connected to COVID-19 vaccinations' adverse effects and those unrelated to COVID-19 vaccines' adverse effects. Then, valid tweets were further classified into three categories: personal experience, informative, and seeking advice. The authors then used the data to train four ML models. There are also SVM, Logistic Regression, LSTM, and ANN. The LSTM algorithm generated the most outstanding results, with an accuracy of 97.64&. In addition, the researchers conclude that the SVM may not be suitable for planned research since it gave the lowest degree of accuracy, 80%." @default.
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- W4320802015 date "2022-10-25" @default.
- W4320802015 modified "2023-10-16" @default.
- W4320802015 title "Classification of Covid19 Vaccine-Related Tweets Using Deep Learning" @default.
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- W4320802015 doi "https://doi.org/10.1109/icdabi56818.2022.10041615" @default.
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