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COVID-19 is currently a global threat, so the World Health Organization has classified it as a pandemic. Due to the worldwide nature of the pandemic, many things including educational activities have been suspended. During COVID-19, most educational institutions have been shifted online. The purpose of this research is to examine how individuals feel about e-learning and draw conclusions regarding its efficacy. People’s opinions may be found on social media sites like Twitter, Instagram, Facebook, etc. In this investigation, a dataset consisting of 16,286 tweets related to online education has been used. Total 16 machine learning models, have been implemented in this study in which extreme Gradient Boosting (XGB) model with GWO and TF-IDF gives the highest 94.16% precision, 95.22% F-score, 94.87% Recall, and 95.81% accuracy.

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Optimize Machine Learning Model for Sentiment Analysis of Online Education During Covid-19 Pandemic

  • 会议论文

  • 发表日期：

    2025年

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  • Vipin Jain,
  • Kanchan Lata Kashyap

摘要

COVID-19 is currently a global threat, so the World Health Organization has classified it as a pandemic. Due to the worldwide nature of the pandemic, many things including educational activities have been suspended. During COVID-19, most educational institutions have been shifted online. The purpose of this research is to examine how individuals feel about e-learning and draw conclusions regarding its efficacy. People’s opinions may be found on social media sites like Twitter, Instagram, Facebook, etc. In this investigation, a dataset consisting of 16,286 tweets related to online education has been used. Total 16 machine learning models, have been implemented in this study in which extreme Gradient Boosting (XGB) model with GWO and TF-IDF gives the highest 94.16% precision, 95.22% F-score, 94.87% Recall, and 95.81% accuracy.