What Do Guests Really Think? A Multi-Scenario ABSA Framework in Brazilian Amazon Tourism
Resumo
O turismo é uma atividade socioeconômica relevante, contribuindo com 11,6 trilhões de dólares para o PIB global em 2025, e plataformas online como o Booking tornaram-se ricas fontes de conteúdo gerado pelo usuário, refletindo a percepção de turistas em larga escala. Contudo, há uma lacuna significativa de estudos sobre o turismo na Amazônia brasileira. Dessa forma, este trabalho realiza a coleta, o processamento e a análise de avaliações do Booking de três destinos de dimensões distintas, Belém, Santarém e Alter do Chão (Pará), combinando análise exploratória e análise de sentimentos baseada em aspectos em português. Foram analisadas cerca de 103 mil avaliações, com três variações do BERTimbau-Large ajustadas ao corpus ABSAPT-2024. Os resultados evidenciam predominância de sentimento positivo, picos sazonais no segundo semestre e contrastes entre as cidades nos aspectos mais notáveis, fornecendo subsídios a gestores de hospedagem e a políticas públicas de turismo na Amazônia.Referências
Aleixo, M. J. A., de Sousa Araujo, G., do Carmo, F. A., Junior, A. F. L. J., and Lobato, F. M. F. (2023). Análise comparativa de plataformas de mídias sociais turísticas: um estudo de caso de booking e tripadvisor para o município de santarém, pará. Anais do Computer on the Beach, 14:070–077.
Buhalis, D. and Volchek, K. (2022). Bridging marketing theory and big data analytics: The taxonomy of marketing attribution. International Journal of Information Management, 56:102253.
chakki-works (2024). seqeval: A python framework for sequence labeling evaluation. GitHub. Acesso em: 05 de junho de 2026.
Cheng, L.-C., Huang, H.-Y., and Huang, Y.-W. (2025). Multi-task chinese aspect-based sentiment analysis framework for service improvement: a case study on bnb reviews. Electronic Commerce Research, 25(6):4765–4798.
Chu, S.-C., Kim, J., and Chen, H. (2022). The power of social media in destination choice: Exploring the roles of online reviews and user-generated content in tourism. Journal of Travel & Tourism Marketing, 39(2):214–234.
de Souza, A. C., Batista, K. R. M., and Gomes, S. C. (2020). Hospedagens para turistas a negócios: quais atributos importam? RITUR-Revista Iberoamericana de Turismo, 10(1):202–220.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 4171–4186.
Egger, R. and Gokce, E. (2022). Natural language processing (nlp): An introduction: making sense of textual data. In Applied data science in tourism: Interdisciplinary approaches, methodologies, and applications, pages 307–334. Springer.
Jakob, N. and Gurevych, I. (2010). Extracting opinion targets in a single and cross-domain setting with conditional random fields. In Proceedings of the 2010 conference on empirical methods in natural language processing, pages 1035–1045.
Junior, L. F., Junior, J. S., and Lobato, F. (2020). Um olhar sobre turismo gastronômico: Um caso no tripadvisor. In Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pages 519–530. SBC.
Li, X., Bing, L., Li, P., and Lam, W. (2019). A unified model for opinion target extraction and target sentiment prediction. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 6714–6721.
Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Kull, M., Lachiche, N., Ramírez-Quintana, M. J., and Flach, P. (2019). Crisp-dm twenty years later: From data mining processes to data science trajectories. IEEE transactions on knowledge and data engineering, 33(8):3048–3061.
Pereira, F. N., de Freitas, L. A., and Corrêa, U. B. (2026). Análise de sentimento baseada em aspectos no domínio de acomodações utilizando o modelo bertimbau. In Procee dings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026)-Vol. 1, pages 830–837.
Pereira, G., Barbosa, L., Moreira, J., Melo, T., and Silva, A. (2024). Enhancing aspect-based sentiment analysis for portuguese using instruction tuning. In Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pages 990–1001. SBC.
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., and Manandhar, S. (2014). SemEval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 27–35, Dublin, Ireland. Association for Computational Linguistics.
Ralph, P. and Baltes, S. (2022). Paving the way for mature secondary research: the seven types of literature review. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 1632–1636.
Ramshaw, L. and Marcus, M. (1995). Text chunking using transformation-based learning. In Third workshop on very large corpora.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Sun, C., Huang, L., and Qiu, X. (2019). Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 380–385.
Thommandru, A., Espinoza-Maguiña, M., Ramirez-Asis, E., Ray, S., Naved, M., and Guzman-Avalos, M. (2023). Role of tourism and hospitality business in economic development. Materials Today: Proceedings, 80:2901–2904.
Thurow Bender, A., Gomes, G. A., Lopes, E. P., Araujo, R. M., de Freitas, L. A., and Corrêa, U. B. (2024). Overview of absapt at iberlef 2024: Overview of the task on aspect-based sentiment analysis in portuguese. Procesamiento del lenguaje natural, 73:315–322.
Tripathy, G. and Sharaff, A. (2026). Traversing the landscape of aspect-based sentiment analysis: Delving deeper into techniques, trends, and future directions. Computer Science Review, 60:100885.
Yang, H., Zhang, C., and Li, K. (2023). Pyabsa: A modularized framework for reproducible aspect-based sentiment analysis. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, pages 5117–5122. ACM.
Buhalis, D. and Volchek, K. (2022). Bridging marketing theory and big data analytics: The taxonomy of marketing attribution. International Journal of Information Management, 56:102253.
chakki-works (2024). seqeval: A python framework for sequence labeling evaluation. GitHub. Acesso em: 05 de junho de 2026.
Cheng, L.-C., Huang, H.-Y., and Huang, Y.-W. (2025). Multi-task chinese aspect-based sentiment analysis framework for service improvement: a case study on bnb reviews. Electronic Commerce Research, 25(6):4765–4798.
Chu, S.-C., Kim, J., and Chen, H. (2022). The power of social media in destination choice: Exploring the roles of online reviews and user-generated content in tourism. Journal of Travel & Tourism Marketing, 39(2):214–234.
de Souza, A. C., Batista, K. R. M., and Gomes, S. C. (2020). Hospedagens para turistas a negócios: quais atributos importam? RITUR-Revista Iberoamericana de Turismo, 10(1):202–220.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 4171–4186.
Egger, R. and Gokce, E. (2022). Natural language processing (nlp): An introduction: making sense of textual data. In Applied data science in tourism: Interdisciplinary approaches, methodologies, and applications, pages 307–334. Springer.
Jakob, N. and Gurevych, I. (2010). Extracting opinion targets in a single and cross-domain setting with conditional random fields. In Proceedings of the 2010 conference on empirical methods in natural language processing, pages 1035–1045.
Junior, L. F., Junior, J. S., and Lobato, F. (2020). Um olhar sobre turismo gastronômico: Um caso no tripadvisor. In Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pages 519–530. SBC.
Li, X., Bing, L., Li, P., and Lam, W. (2019). A unified model for opinion target extraction and target sentiment prediction. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 6714–6721.
Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Kull, M., Lachiche, N., Ramírez-Quintana, M. J., and Flach, P. (2019). Crisp-dm twenty years later: From data mining processes to data science trajectories. IEEE transactions on knowledge and data engineering, 33(8):3048–3061.
Pereira, F. N., de Freitas, L. A., and Corrêa, U. B. (2026). Análise de sentimento baseada em aspectos no domínio de acomodações utilizando o modelo bertimbau. In Procee dings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026)-Vol. 1, pages 830–837.
Pereira, G., Barbosa, L., Moreira, J., Melo, T., and Silva, A. (2024). Enhancing aspect-based sentiment analysis for portuguese using instruction tuning. In Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pages 990–1001. SBC.
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., and Manandhar, S. (2014). SemEval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 27–35, Dublin, Ireland. Association for Computational Linguistics.
Ralph, P. and Baltes, S. (2022). Paving the way for mature secondary research: the seven types of literature review. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 1632–1636.
Ramshaw, L. and Marcus, M. (1995). Text chunking using transformation-based learning. In Third workshop on very large corpora.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Sun, C., Huang, L., and Qiu, X. (2019). Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 380–385.
Thommandru, A., Espinoza-Maguiña, M., Ramirez-Asis, E., Ray, S., Naved, M., and Guzman-Avalos, M. (2023). Role of tourism and hospitality business in economic development. Materials Today: Proceedings, 80:2901–2904.
Thurow Bender, A., Gomes, G. A., Lopes, E. P., Araujo, R. M., de Freitas, L. A., and Corrêa, U. B. (2024). Overview of absapt at iberlef 2024: Overview of the task on aspect-based sentiment analysis in portuguese. Procesamiento del lenguaje natural, 73:315–322.
Tripathy, G. and Sharaff, A. (2026). Traversing the landscape of aspect-based sentiment analysis: Delving deeper into techniques, trends, and future directions. Computer Science Review, 60:100885.
Yang, H., Zhang, C., and Li, K. (2023). Pyabsa: A modularized framework for reproducible aspect-based sentiment analysis. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, pages 5117–5122. ACM.
Publicado
19/10/2026
Como Citar
SILVEIRA, Victor I. S.; SILVA, Cecília A.; CORTÉS, Omar A. C.; J. JÚNIOR, Antonio F. L.; LOBATO, Fábio M. F..
What Do Guests Really Think? A Multi-Scenario ABSA Framework in Brazilian Amazon Tourism. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1744-1755.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.31354.
