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This research delves into a comprehensive analysis of public sentiments and adverse drug reactions (ADRs) associated with COVID-19 vaccines and hydroxychloroquine, leveraging data collected from Twitter and Google Form responses. This research work uses Machine Learning (ML) and Deep Learning (DL) models for data collecting, preprocessing, feature extraction, and evaluation. For uniformity, user names, punctuation, links, and stop words are deleted, and text is changed to lowercase during preparation. N-grams-based feature extraction algorithms are then used to extract relevant Twitter messages from preprocessed data. ML methods, specifically “Tri-grams with Q-SVM,” are next tested for COVISHIELD ADR prediction. Deep learning models including LSTM, Bi-LSTM, CNN, and VAE-GANs analyze COVID-19 vaccine emotions. The analysis culminates by underscoring the accuracy of “Tri-grams with Q-SVM” for ADR prediction and highlighting the efficacy of the VAE-GANs model in sentiment analysis. The abstract concludes by discussing the implications of the findings for policymakers and healthcare professionals, emphasizing the importance of accurate sentiment analysis in gauging public opinions. Additionally, it suggests future directions for enhanced vaccination campaigns and public health interventions.

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Determining Adverse Effects of COVID-19 Vaccination Using Machine Learning and Deep Learning Classification and Identification

  • 会议论文

  • 发表日期：

    2025年

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  • K. Priya,
  • A. Anbarasi

摘要

This research delves into a comprehensive analysis of public sentiments and adverse drug reactions (ADRs) associated with COVID-19 vaccines and hydroxychloroquine, leveraging data collected from Twitter and Google Form responses. This research work uses Machine Learning (ML) and Deep Learning (DL) models for data collecting, preprocessing, feature extraction, and evaluation. For uniformity, user names, punctuation, links, and stop words are deleted, and text is changed to lowercase during preparation. N-grams-based feature extraction algorithms are then used to extract relevant Twitter messages from preprocessed data. ML methods, specifically “Tri-grams with Q-SVM,” are next tested for COVISHIELD ADR prediction. Deep learning models including LSTM, Bi-LSTM, CNN, and VAE-GANs analyze COVID-19 vaccine emotions. The analysis culminates by underscoring the accuracy of “Tri-grams with Q-SVM” for ADR prediction and highlighting the efficacy of the VAE-GANs model in sentiment analysis. The abstract concludes by discussing the implications of the findings for policymakers and healthcare professionals, emphasizing the importance of accurate sentiment analysis in gauging public opinions. Additionally, it suggests future directions for enhanced vaccination campaigns and public health interventions.