Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning and Deep Learning for Sentiment Analysis Over Students' Reviews: An Overview Study

Version 1 : Received: 2 February 2021 / Approved: 3 February 2021 / Online: 3 February 2021 (10:11:54 CET)

How to cite: Yang, R. Machine Learning and Deep Learning for Sentiment Analysis Over Students' Reviews: An Overview Study. Preprints 2021, 2021020108 (doi: 10.20944/preprints202102.0108.v1). Yang, R. Machine Learning and Deep Learning for Sentiment Analysis Over Students' Reviews: An Overview Study. Preprints 2021, 2021020108 (doi: 10.20944/preprints202102.0108.v1).

Abstract

Now when the whole world is still under COVID-19 pandemic, many schools have transferred the teaching from physical classroom to online platforms. It is highly important for schools and online learning platforms to investigate the feedback to get valuable insights about online teaching process so that both platforms and teachers are able to learn which aspect they can improve to achieve better teaching performance. But handling reviews expressed by students would be a pretty laborious work if they were handled manually as well as it is unrealistic to handle large-scale feedback from e-learning platform. In order to address this problem, both machine learning algorithms and deep learning models are used in recent research to automatically process students' review getting the opinion, sentiment and attitudes expressed by the students. Such studies may play a crucial role in improving various interactive online learning platforms by incorporating automatic analysis of feedback. Therefore, we conduct an overview study of sentiment analysis in educational field presented in recent research, to help people grasp an overall understanding of the sentiment analysis research. Besides, according to the literature review, we identify three future directions that researchers can focus on in automatically feedback processing: high-level entity extraction, multi-lingual sentiment analysis, and handling of figurative language.

Keywords

Sentiment Analysis; Students' feedback; Students' reviews; Natural language processing; Data mining; Deep learning; Machine learning

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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