Análise de Sentimento em Avaliações de Jogos da Steam: Um Estudo Comparativo entre RoBERTa, SVM e Naive Bayes
Resumo
A análise de sentimentos tem como objetivo a categorização de textos quanto à polaridade expressa, geralmente classificando-os como positivos ou negativos. Neste trabalho, o domínio de aplicação está relacionado a jogos digitais. Foram utilizados o modelo RoBERTa (Robustly Optimized BERT Pretraining Approach) e abordagens tradicionais, como Support Vector Machines (SVM) e Naive Bayes. O objetivo é avaliar o desempenho de cada abordagem dentro do domínio de aplicação, bem como a influência do volume de dados e das estratégias de pré-processamento, de modo a auxiliar na escolha do modelo e do cenário mais adequados.Referências
Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2018). BERT: pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805.
Eke, C. I., Norman, A. A., and Shuib, L. (2021). Context-based feature technique for sarcasm identification in benchmark datasets using deep learning and bert model. IEEE Access, 9:48501–48518.
Garrido-Merchan, E. C., Gozalo-Brizuela, R., and Gonzalez-Carvajal, S. (2023). Comparing bert against traditional machine learning models in text classification. Journal of Computational and Cognitive Engineering, 2(4):352–356.
Jurafsky, D. and Martin, J. H. (2024). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. [s.n.], 3. ed. draft edition. Online manuscript released August 20, 2024.
Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., and He, L. (2022). A survey on text classification: From traditional to deep learning. ACM Transactions on Intelligent Systems and Technology (TIST), 13(2):1–41.
Liao, W., Zeng, B., Yin, X., and Wei, P. (2021). An improved aspect-category sentiment analysis model for text sentiment analysis based on roberta. Applied Intelligence, 51(6):3522–3533.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael, CA.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach.
Moreira, L. S., Lunardi, G. M., de Oliveira Ribeiro, M., Silva, W., and Basso, F. P. (2023). A study of algorithm-based detection of fake news in brazilian election: Is bert the best. IEEE Latin America Transactions, 21(8):897–903.
Sabiri, B., Khtira, A., El Asri, B., and Rhanoui, M. (2023). Analyzing bert’s performance compared to traditional text classification models. In ICEIS (1), pages 572–582.
Siino, M., Tinnirello, I., and La Cascia, M. (2024). Is text preprocessing still worth the time? a comparative survey on the influence of popular preprocessing methods on transformers and traditional classifiers. Information Systems, 121:102342.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Wahba, Y., Madhavji, N., and Steinbacher, J. (2022). A comparison of svm against pre-trained language models (plms) for text classification tasks. In International Conference on Machine Learning, Optimization, and Data Science, pages 304–313. Springer.
Zhao, T. (2024). Valve corporation’s evolution from a game developer to the gaming titan. Transactions on Social Science, Education and Humanities Research, 11:502–508.
Eke, C. I., Norman, A. A., and Shuib, L. (2021). Context-based feature technique for sarcasm identification in benchmark datasets using deep learning and bert model. IEEE Access, 9:48501–48518.
Garrido-Merchan, E. C., Gozalo-Brizuela, R., and Gonzalez-Carvajal, S. (2023). Comparing bert against traditional machine learning models in text classification. Journal of Computational and Cognitive Engineering, 2(4):352–356.
Jurafsky, D. and Martin, J. H. (2024). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. [s.n.], 3. ed. draft edition. Online manuscript released August 20, 2024.
Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., and He, L. (2022). A survey on text classification: From traditional to deep learning. ACM Transactions on Intelligent Systems and Technology (TIST), 13(2):1–41.
Liao, W., Zeng, B., Yin, X., and Wei, P. (2021). An improved aspect-category sentiment analysis model for text sentiment analysis based on roberta. Applied Intelligence, 51(6):3522–3533.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael, CA.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach.
Moreira, L. S., Lunardi, G. M., de Oliveira Ribeiro, M., Silva, W., and Basso, F. P. (2023). A study of algorithm-based detection of fake news in brazilian election: Is bert the best. IEEE Latin America Transactions, 21(8):897–903.
Sabiri, B., Khtira, A., El Asri, B., and Rhanoui, M. (2023). Analyzing bert’s performance compared to traditional text classification models. In ICEIS (1), pages 572–582.
Siino, M., Tinnirello, I., and La Cascia, M. (2024). Is text preprocessing still worth the time? a comparative survey on the influence of popular preprocessing methods on transformers and traditional classifiers. Information Systems, 121:102342.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Wahba, Y., Madhavji, N., and Steinbacher, J. (2022). A comparison of svm against pre-trained language models (plms) for text classification tasks. In International Conference on Machine Learning, Optimization, and Data Science, pages 304–313. Springer.
Zhao, T. (2024). Valve corporation’s evolution from a game developer to the gaming titan. Transactions on Social Science, Education and Humanities Research, 11:502–508.
Publicado
22/04/2026
Como Citar
FERREIRA, Alex Benhard; LICHTNOW, Daniel.
Análise de Sentimento em Avaliações de Jogos da Steam: Um Estudo Comparativo entre RoBERTa, SVM e Naive Bayes. In: ESCOLA REGIONAL DE BANCO DE DADOS (ERBD), 21. , 2026, Dois Vizinhos/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1-10.
ISSN 2595-413X.
DOI: https://doi.org/10.5753/erbd.2026.20241.
