Impact of Online Education and Sentiment Analysis from Twitter Data using Topic Modeling Algorithms

Abstract

During a pandemic, all industries suffer greatly, and every sector of the world suffers in some way, including the education sector. Internet expressions reflect users' feelings about a product or service. The polarity of information in source data toward a subject under investigation is determined by sentiment analysis processes. The goal of this study is to examine social media expressions about online teaching and learning, as online education will become a part of everyday life in the future. We collected data from Twitter using keywords related to online education and Google form from engineering undergraduate students for prototype implementation. This analysis will assist teachers, parents, and the student community in understanding the benefits and drawbacks of the education industry, allowing for further improvement in educational outcomes. We used aspect-based sentiment analysis and topic modeling to determine sentiment polarity and important topics for education sector stakeholders. To begin, we used TextBlob Python package to determine sentiment polarity, and Bag of Words, LDA and LSA model for discovering topics. After modeling topics from the collected data, topic Coherence is used to assess the degree of semantic similarity between high-scoring words in the topic. The word cloud and LDAvis are used to visualize data. The experimental results are promising and it will assist education stakeholders in addressing the concerns that have been identified as social media expressions to work on.