Emotion Analysis Model of MOOC Course Review Based on BiLSTM

Authors

  • Shen Ji School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
  • Tan Fangbi School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China

DOI:

https://doi.org/10.3991/ijet.v16i08.18517

Keywords:

course review, sentiment analysis, deep learning, BiLSTM

Abstract


Online course review can objectively reflect the emotional tendency of learners towards the learning effect. This paper proposes a deep neural network based sentiment analysis model for MOOC course reviews. The model uses Bidirectional Long Short-Term Memory Network (BiLSTM) to analyze Chinese semantic. In order to deal with the imbalance of training data set, this paper introduces two methods to balance it and adds dropout mechanism to prevent the over fitting of the model. The model is then applied to the emotional evaluation of MOOC course of “Fundamentals of College Computer Application”. The application results show that the model has achieved good accuracy and can well realize the emotional orientation analysis of online course reviews so as to provide valuable reference for Course Builders.

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Published

2021-04-23

How to Cite

Ji, S., & Fangbi, T. (2021). Emotion Analysis Model of MOOC Course Review Based on BiLSTM. International Journal of Emerging Technologies in Learning (iJET), 16(08), pp. 93–105. https://doi.org/10.3991/ijet.v16i08.18517

Issue

Vol. 16 No. 08 (2021)

Section

Papers

License

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