ارائه یک روش جدید تحلیل احساسات مبتنی بر الگوریتم بهینه‌سازی ارشمیدس چندهدفه و یادگیری ماشین

Hosseinalipour, A., et al., A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology. Applied Intelligence, 2021: p. 1-36.

2. Vazan, M. and J. Razmara, Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews. arXiv preprint arXiv:2109.07680, 2021.
3. Hosseinalipour, A., et al., Toward text psychology analysis using social spider optimization algorithm. Concurrency and Computation: Practice and Experience, 2021. n/a(n/a): p. e6325.
4. Hosseinalipour, A. and R. Ghanbarzadeh, A novel approach for spam detection using horse herd optimization algorithm. Neural Computing and Applications, 2022: p. 1-15.
5. Zhang, L., S. Wang, and B. Liu, Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018. 8(4): p. e1253.
6. Young, T., et al., Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine, 2018. 13(3): p. 55-75.
7. Tang, F., et al., Aspect based fine-grained sentiment analysis for online reviews. Information Sciences, 2019. 488: p. 190-204.
8. Pang, B. and L. Lee, Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint cs/0506075, 2005.
9. Ouyang, X., et al. Sentiment analysis using convolutional neural network. in 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing. 2015. IEEE.
10. Nowak, J., A. Taspinar, and R. Scherer. LSTM recurrent neural networks for short text and sentiment classification. in International Conference on Artificial Intelligence and Soft Computing. 2017. Springer.
11. Ma, D., et al., Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893, 2017.
12. Hassan, A. and A. Mahmood. Deep learning approach for sentiment analysis of short texts. in 2017 3rd international conference on control, automation and robotics (ICCAR). 2017. IEEE.
13. Çano, E. and M. Morisio. A deep learning architecture for sentiment analysis. in Proceedings of the International Conference on Geoinformatics and Data Analysis. 2018.
14. Goularas, D. and S. Kamis. Evaluation of deep learning techniques in sentiment analysis from twitter data. in 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). 2019. IEEE.
15. Li, R., et al., A local search algorithm with tabu strategy and perturbation mechanism for generalized vertex cover problem. Neural Computing and Applications, 2017. 28(7): p. 1775-1785.
16. Hashim, F.A., et al., Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 2021. 51(3): p. 1531-1551.
17. Arora, S. and P. Anand, Binary butterfly optimization approaches for feature selection. Expert Systems with Applications, 2019. 116: p. 147-160.
18. Hussien, A.G., et al., S-shaped binary whale optimization algorithm for feature selection, in Recent trends in signal and image processing. 2019, Springer. p. 79-87.
19. McCallum, A., Graphical Models, Lecture2: Bayesian Network Represention. PDF). Retrieved, 2019. 22.