Vol.71, No.1, 2022, pp.941-959, doi:10.32604/cmc.2022.021839
OPEN ACCESS
ARTICLE
Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization
  • Alaa A. El-Demerdash, Sherif E. Hussein, John FW Zaki*
Department of Computer and Systems, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
* Corresponding Author: John FW Zaki. Email:
Received 16 July 2021; Accepted 01 September 2021; Issue published 03 November 2021
Abstract
Sentiment analysis attracts the attention of Egyptian Decision-makers in the education sector. It offers a viable method to assess education quality services based on the students’ feedback as well as that provides an understanding of their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. After dataset preprocessing to remove symbols, necessary stemming and lemmatization is performed for features extraction. This is followed by several machine learning techniques and a proposed Long Short-Term Memory (LSTM) classifier optimized by the Salp Swarm Algorithm (SSA) and measured the corresponding performance. Then, the validity and accuracy of commonly used classifiers, such as Support Vector Machine, Logistic Regression Classifier, and Naive Bayes classifier, were reviewed. Moreover, LSTM based on the SSA classification model was compared with Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB). Finally, as LSTM based SSA achieved the highest accuracy, it was applied to predict the sentiments of students’ feedback and evaluate their association with the course outcome evaluations for education quality purposes.
Keywords
Sentiment analysis; course evaluation; deep learning; Bi-LSTM; opinion mining; students feedback; natural language processing; machine learning; tweets analysis; SSA
Cite This Article
El-Demerdash, A. A., Hussein, S. E., Zaki, J. F. (2022). Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization. CMC-Computers, Materials & Continua, 71(1), 941–959.
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