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English Information

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Authors
# Name
1 Emanuelle Marreira(erm.eng22@uea.edu.br)
2 Tiago de Melo(tmelo@uea.edu.br)

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Reference
# Reference
1 Almeida Neto, J. and de Melo, T. (2023). Exploring supervised learning models for multi-label text classification in brazilian restaurant reviews. Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pages 126–140.
2 Antonio, N., de Almeida, A. M., Nunes, L., Batista, F., and Ribeiro, R. (2018). Hotel online reviews: creating a multi-source aggregated index. International Journal of Contemporary Hospitality Management, 30(12):3574–3591.
3 Chambua, J. and Niu, Z. (2021). Review text based rating prediction approaches: preference knowledge learning, representation and utilization. Artificial Intelligence Review, 54:1171–1200.
4 Cunha, W., Rocha, L., and Gonçalves, M. A. (2025). A thorough benchmark of automatic text classification: From traditional approaches to large language models. arXiv preprint arXiv:2504.01930.
5 de Melo, T. (2022). Sentilexbr: An automatic methodology of building sentiment lexicons for the portuguese language. Journal of Information and Data Management, 13(3).
6 de Melo, T., da Silva, A. S., de Moura, E. S., and Calado, P. (2019). Opinionlink: Leveraging user opinions for product catalog enrichment. Information Processing & Management, 56(3):823–843.
7 Eleyan, D., Othman, A., and Eleyan, A. (2020). Enhancing software comments readability using flesch reading ease score. Information, 11(9):430.
8 Gardazi, N. M., Daud, A., Malik, M. K., Bukhari, A., Alsahfi, T., and Alshemaimri, B. (2025). Bert applications in natural language processing: a review. Artificial Intelligence Review, 58(6):1–49.
9 Hanić, S., Bagić Babac, M., Gledec, G., and Horvat, M. (2024). Comparing machine learning models for sentiment analysis and rating prediction of vegan and vegetarian restaurant reviews. Computers, 13(10):248.
10 Hossain, M. I. et al. (2021). Rating prediction of product reviews in bangla using machine learning. In Proc. Int. Conf. on AI and Mechatronics Systems (AIMS), pages 1–6. IEEE.
11 Kang, W.-C., Ni, J., Mehta, N., Sathiamoorthy, M., Hong, L., Chi, E., and Cheng, D. Z. (2023). Do llms understand user preferences? evaluating llms on user rating prediction. arXiv preprint arXiv:2305.06474.
12 Kettunen, K. (2014). Can type-token ratio be used to show morphological complexity of languages? Journal of Quantitative Linguistics, 21(3):223–245.
13 Khan, R. A., Mannan, A., and Aslam, N. (2022). Prediction of product rating based on polarized reviews using supervised machine learning. VFAST Transactions on Software Engineering, 10(4):01–09.
14 Li, J., Wang, Y., and Tao, Z. (2022a). A rating prediction recommendation model combined with the optimizing allocation for information granularity of attributes. Information, 13(1):21.
15 Li, S., Liu, F., Zhang, Y., Zhu, B., Zhu, H., and Yu, Z. (2022b). Text mining of user-generated content (ugc) for business applications in e-commerce: A systematic review. Mathematics, 10(19):3554.
16 Pak, A., Ziyaden, A., Saparov, T., Akhmetov, I., and Gelbukh, A. (2024). Word embeddings: A comprehensive survey. Computación y Sistemas, 28(4):2005–2029.
17 Pereira, D. A. (2021). A survey of sentiment analysis in the portuguese language. Artificial Intelligence Review, 54(2):1087–1115.
18 Shi, W., Wang, L., and Qin, J. (2020). Extracting user influence from ratings and trust for rating prediction in recommendations. Scientific Reports, 10(1):13592.
19 Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.