SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Jose Fagner(fagnersilva009@gmail.com)
2 Sebastião Emidio(sebastiaoalves@uern.br)
3 Raul Benites(raulparadeda@uern.br)
4 Lenardo Chaves(lenardo@ufersa.edu.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 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.
2 Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
3 Chen, Y. (2015). Convolutional neural network for sentence classification. Master’s thesis, University of Waterloo.
4 Computation, N. (2016). Long short-term memory. Neural Comput, 9:1735–1780.
5 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.
6 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 con- ference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 4171–4186.
7 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 confe- rence on computational linguistics: technical papers, pages 69–78.
8 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 ma- chine learning in the cloud. Applied Sciences, 13(2):773.
9 Graves, A., Mohamed, A.-r., and Hinton, G. (2013). Speech recognition with deep recur- rent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 6645–6649. Ieee.
10 Kheiri, K. and Karimi, H. (2023). Sentimentgpt: Exploiting gpt for advanced senti- ment analysis and its departure from current machine learning. arXiv preprint ar- Xiv:2307.10234.
11 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.
12 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.
13 Sahoo, C., Wankhade, M., and Singh, B. K. (2023). Sentiment analysis using deep lear- ning techniques: a comprehensive review. International Journal of Multimedia Infor- mation Retrieval, 12(2):41.
14 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.
15 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.