Performance Analysis of Hyperparameters on a Sentiment Analysis Model

  • I. A. Kandhro Department of Computer Science, Sindh Madressatul Islam University, Pakistan
  • S. Z. Jumani Department of Computer Science, Sindh Madressatul Islam University, Pakistan
  • F. Ali Department of Software Engineering, Sir Syed University of Engineering and Technology , Karachi, Pakistan
  • Z. U. Shaikh Department of Software Engineering, Sindh Madressatul Islam University, Pakistan
  • M. A. Arain Department of Software Engineering, Sindh Madressatul Islam University, Pakistan
  • A. A. Shaikh Department of Software Engineering, Sindh Madressatul Islam University, Pakistan
Keywords: LSTM, student feedback, sentiment analysis, performance analysis


This paper focuses on the performance analysis of hyperparameters of the Sentiment Analysis (SA) model of a course evaluation dataset. The performance was analyzed regarding hyperparameters such as activation, optimization, and regularization. In this paper, the activation functions used were adam, adagrad, nadam, adamax, and hard_sigmoid, the optimization functions were softmax, softplus, sigmoid, and relu, and the dropout values were 0.1, 0.2, 0.3, and 0.4. The results indicate that parameters adam and softmax with dropout value 2.0 are effective when compared to other combinations of the SA model. The experimental results reveal that the proposed model outperforms the state-of-the-art deep learning classifiers.


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