Análise de Sentimentos em Avaliações de Livros Utilizando a API Gemini para Recomendação Personalizada
Resumo
Este trabalho investiga o uso da API Gemini na análise de sentimentos em avaliações de livros, com foco em resenhas categorizadas por gênero. A metodologia envolveu a extração de emoções predominantes para aplicação em sistemas de recomendação personalizados. Os resultados mostraram eficácia na detecção de sentimentos positivos, especialmente em obras de ficção. As recomendações baseadas nas emoções apresentaram alta correspondência com as preferências dos usuários, o que mostra como a análise de sentimentos pode melhorar a personalização em sistemas de recomendação de livros. Também foram identificados desafios em gêneros menos populares, indicando a necessidade de abordagens adaptativas.
Palavras-chave:
Análise de Sentimentos, Recomendação de Livros, Computação Afetiva, Processamento de Linguagem Natural, API Gemini
Referências
Ashish, V., Noam, S., Niki, P., Jakob, U., Llion, J., Aidan, N., Łukasz, K., and Illia, P. (2017). "attention is all you need", advances in neural information processing systems. NeurIPS Proceedings.
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Chen, Y. (2015). Convolutional neural network for sentence classification. Master’s thesis, University of Waterloo.
Computation, N. (2016). Long short-term memory. Neural Comput, 9:1735–1780.
Devika, P. and Milton, A. (2025). Book recommendation using sentiment analysis and ensembling hybrid deep learning models. Knowledge and Information Systems, 67(2):1131–1168.
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, volume 1 (long and short papers), pages 4171–4186.
Dos Santos, C. and Gatti, M. (2014). Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pages 69–78.
Gogula, S. D., Rahouti, M., Gogula, S. K., Jalamuri, A., and Jagatheesaperumal, S. K. (2023). An emotion-based rating system for books using sentiment analysis and machine learning in the cloud. Applied Sciences, 13(2):773.
Graves, A., Mohamed, A.-r., and Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 6645–6649. Ieee.
Kheiri, K. and Karimi, H. (2023). Sentimentgpt: Exploiting gpt for advanced sentiment analysis and its departure from current machine learning. arXiv preprint arXiv:2307.10234.
Liu, Z., Lin, W., Shi, Y., and Zhao, J. (2021). A robustly optimized bert pre-training approach with post-training. In China national conference on Chinese computational linguistics, pages 471–484. Springer.
Mounika, A. and Saraswathi, S. (2021). Design of book recommendation system using sentiment analysis. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020, pages 95–101. Springer.
Sahoo, C., Wankhade, M., and Singh, B. K. (2023). Sentiment analysis using deep learning techniques: a comprehensive review. International Journal of Multimedia Information Retrieval, 12(2):41.
Severyn, A. and Moschitti, A. (2015). Twitter sentiment analysis with deep convolutional neural networks. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pages 959–962.
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1631–1642.
Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
Chen, Y. (2015). Convolutional neural network for sentence classification. Master’s thesis, University of Waterloo.
Computation, N. (2016). Long short-term memory. Neural Comput, 9:1735–1780.
Devika, P. and Milton, A. (2025). Book recommendation using sentiment analysis and ensembling hybrid deep learning models. Knowledge and Information Systems, 67(2):1131–1168.
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, volume 1 (long and short papers), pages 4171–4186.
Dos Santos, C. and Gatti, M. (2014). Deep convolutional neural networks for sentiment analysis of short texts. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pages 69–78.
Gogula, S. D., Rahouti, M., Gogula, S. K., Jalamuri, A., and Jagatheesaperumal, S. K. (2023). An emotion-based rating system for books using sentiment analysis and machine learning in the cloud. Applied Sciences, 13(2):773.
Graves, A., Mohamed, A.-r., and Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 6645–6649. Ieee.
Kheiri, K. and Karimi, H. (2023). Sentimentgpt: Exploiting gpt for advanced sentiment analysis and its departure from current machine learning. arXiv preprint arXiv:2307.10234.
Liu, Z., Lin, W., Shi, Y., and Zhao, J. (2021). A robustly optimized bert pre-training approach with post-training. In China national conference on Chinese computational linguistics, pages 471–484. Springer.
Mounika, A. and Saraswathi, S. (2021). Design of book recommendation system using sentiment analysis. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020, pages 95–101. Springer.
Sahoo, C., Wankhade, M., and Singh, B. K. (2023). Sentiment analysis using deep learning techniques: a comprehensive review. International Journal of Multimedia Information Retrieval, 12(2):41.
Severyn, A. and Moschitti, A. (2015). Twitter sentiment analysis with deep convolutional neural networks. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pages 959–962.
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1631–1642.
Publicado
29/09/2025
Como Citar
SILVA, Fagner; ALVES FILHO, Sebastião; PARADEDA, Raul; SILVA, Lenardo.
Análise de Sentimentos em Avaliações de Livros Utilizando a API Gemini para Recomendação Personalizada. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 40. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 671-684.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2025.247308.
