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Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19

Authors: Muhammad Mujahid, Ernesto Lee, Furqan Rustam, Patrick Bernard Washington, Saleem Ullah, Aijaz Ahmad Reshi, Imran Ashraf
Publication date: 2021/1
Journal: Applied Sciences
Volume: 11
Issue: 18
Pages: 8438
Publisher: Multidisciplinary Digital Publishing Institute


Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high …




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M Mujahid, E Lee, F Rustam, PB Washington, S Ullah… – Applied Sciences, 2021
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