References
Al Amrani, Y., Lazaar, M., & El Kadiri, K. E. (2018). Random forest and support vector machine based hybrid approach to sentiment analysis. Procedia Computer Science, 127, 511–520. Alamoudi, E. S., & Alghamdi, N. S. (2021). Sentiment classification and aspect-based sentiment analysis on Yelp reviews using deep learning and word embeddings. Journal of Decision Systems, 30(2–3), 259–281. Alqaryouti, O., Siyam, N., Abdel Monem, A., & Shaalan, K. (2024). Aspect-based sentiment analysis using smart government review data. Applied Computing and Informatics, 20(1– 2), 142–161. An, S., Kim, D., & Kim, S. (2018). Recurrent neural network training with dark knowledge transfer. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 4254–4263. Asthana, A. (2022). Hybrid Recommendation System: A Review. International Journal of Computer Applications, 184(32), 1–7. Drolet, M.-J., Rose-Derouin, E., Leblanc, J.-C., Ruest, M., & Williams-Jones, B. (2023). Ethical issues in research: Perceptions of researchers, research ethics board members, and research ethics experts. Journal of Academic Ethics, 21(2), 269–292. Hao, Y., Zhang, Y., & Li, X. (2023). Sentiment recognition and analysis method of official document text based on BERT-SVM model. Journal of Physics: Conference Series, 2318(1), Heguerte, L. B., Bugeau, A., & Lannelongue, L. (2023). How to estimate carbon footprint when training deep learning models? A guide and review. Environmental Research Communications, 5(8), 085001 Javed, A., Rafi, M., & Baig, A. R. (2021). A Survey on Content-Based Filtering Recommender Systems. Journal of Information Science and Engineering, 37(4), 1011–1031. Kulkarni, S., & Rodd, S. F. (2020). Context Aware Recommendation Systems: A review of the state of the art techniques. Computer Science Review, 37, 100255 Kulkarni, S., & Rodd, S. F. (2020). Context Aware Recommendation Systems: A review of the state of the art techniques. Computer Science Review, 37, 100255. Lengkeek, S., van der Lee, C., & Hogenboom, F. (2023). Leveraging hierarchical language models for aspect-based sentiment analysis on financial data. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1234-1245. Li, Z., Zhang, Y., & Chen, L. (2022). Personalized recommendation model of electronic commerce in new media environment. Frontiers in Psychology, 13, 952622. Li, Z., Zhang, Y., & Chen, L. (2022). Personalized recommendation model of electronic commerce in new media environment. Frontiers in Psychology, 13, 952622. Logesh, R., Subramaniyaswamy, V., & Vijayakumar, V. (2019). A Hybrid Personalized Recommender System Using Clustering and Association Rule Mining. Journal of Ambient Intelligence and Humanized Computing, 10(5), 1955–1965. Mutinda, J., Mwangi, W., & Okeyo, G. (2023). Sentiment analysis of text reviews using lexicon- enhanced BERT embedding (LeBERT) model with convolutional neural network. Applied Sciences, 13(3), 1445 Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1 Osman, T., Noah, S. A. M., & Darwich, M. (2019). A Hybrid Recommender System Based on Context Awareness and Sequential Behavior. IEEE Access, 7, 186264–186278. Pagano, T. P., Loureiro, R. B., Lisboa, F. V. N., Peixoto, R. M., Guimarães, G. A. S., Cruz, G. O. R., Araujo, M. M., Santos, L. L., Cruz, M. A. S., Oliveira, E. L. S., & others. (2023). Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods. Big Data and Cognitive Computing, 7(1), 15. Roy, S., & Dutta, P. (2022). A Hybrid Recommender System Using Collaborative Filtering and Content-Based Filtering for Improved Recommendation. Procedia Computer Science, 198, 85–91. Shakhovska, N., Medykovskyy, M., & Syerov, Y. (2020). Logistic regression for star rating prediction. Advances in Intelligent Systems and Computing, 1080, 697–707. Suresh, A., & MJ, C. M. B. (2020). A Comprehensive Study of Hybrid Recommendation Systems for E-Commerce Applications. International Journal of Advanced Science and Technology, 29(3), 4089–4101. Suresh, A., & MJ, C. M. B. (2020). A Comprehensive Study of Hybrid Recommendation Systems for E-Commerce Applications. International Journal of Advanced Science and Technology, 29(3), 4089–4101. Walek, B., & Fajmon, P. (2023). A Hybrid Recommender System for E-commerce Based on Customer Reviews and Product Features. Applied Sciences, 13(1), 123. Wang, J., Zhang, Y., Yuan, S., & Zeng, D. D. (2020). A Systematic Study on the Recommender Systems in the E-Commerce. IEEE Access, 8, 45459–45470. Wang, J., Zhang, Y., Yuan, S., & Zeng, D. D. (2020). A Systematic Study on the Recommender Systems in the E-Commerce. IEEE Access, 8, 45459–45470.
