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Performance Analysis of Hyperparameters on a Sentiment Analysis Model
- Source :
- Engineering, Technology & Applied Science Research, Vol 10, Iss 4 (2020)
- Publication Year :
- 2020
- Publisher :
- Zenodo, 2020.
-
Abstract
- 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.
- Subjects :
- Computer science
student feedback
02 engineering and technology
Machine learning
computer.software_genre
Regularization (mathematics)
lcsh:Technology (General)
0202 electrical engineering, electronic engineering, information engineering
performance analysis
Dropout (neural networks)
Hyperparameter
lcsh:T58.5-58.64
business.industry
lcsh:Information technology
Deep learning
Sentiment analysis
020207 software engineering
Sigmoid function
lcsh:TA1-2040
sentiment analysis
Softmax function
lcsh:T1-995
020201 artificial intelligence & image processing
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
LSTM
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Engineering, Technology & Applied Science Research, Vol 10, Iss 4 (2020)
- Accession number :
- edsair.doi.dedup.....5becd17922bf4a075e5f7dd95286906d
- Full Text :
- https://doi.org/10.5281/zenodo.4016212