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English Information

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Authors
# Name
1 Mariana Silva(mariana.santos)
2 Clarisse Scofield(clarissescofield@dcc.ufmg.br)
3 Mirella Moro(mirella@dcc.ufmg.br)

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Reference
# Reference
1 Ahmad, J., Duraisamy, P., Yousef, A., and Buckles, B. (2017). Movie success prediction using data mining. In Int’l Conf. on Computing, Communication and Networking Technologies (ICCCNT), pages 1–4. doi:10.1109/ICCCNT.2017.8204173
2 Champagne, A. (2020). What Is A Reader? How Readers on Goodreads are Changing the Canon in the Twenty-First Century. In 15th Annual International Conference of the Alliance of Digital Humanities Organizations, Conference Abstracts.
3 de Araujo, P. H. L., de Campos, T. E., de Oliveira, R. R., Stauffer, M., Couto, S., and Bermejo, P. (2018). Lener-br: a dataset for named entity recognition in brazilian legal text. In Int’l Conf. on Computational Processing of the Portuguese Language, pages 313–323. Springer
4 Lebrun, T. and Audet, R. (2020). Artificial Intelligence and the Book Industry. White Paper. Zenodo. doi:10.5281/zenodo.4036258
5 Lozano, L. C. and Planells, S. C. (2020). Best books ever dataset. Zenodo. doi:10.5281/zenodo.4265096
6 Maharjan, S., Kar, S., Montes, M., Gonz´alez, F. A., and Solorio, T. (2018). Letting emotions flow: Success prediction by modeling the flow of emotions in books. In Procs. Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 259–265. doi:10.18653/v1/N18-2042
7 Maity, S. K., Panigrahi, A., and Mukherjee, A. (2019). Analyzing Social Book Reading Behavior on Goodreads and How It Predicts Amazon Best Sellers, pages 211–235. Springer International Publishing, Cham.
8 Mart´ın-Gutie´rrez, D., Herna´ndez Pen˜aloza, G., Belmonte-Herna´ndez, A., and A´ lvarez Garc´ıa, F. (2020). A multimodal end-to-end deep learning architecture for music popularity prediction. IEEE Access, 8:39361–39374. doi:10.1109/ACCESS.2020.2976033.
9 Ni, J., Li, J., and McAuley, J. (2019). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Procs. Conf. on Empirical Methods in Natural Language Processing and Int’l Joint Conf. on Natural Language Processing (EMNLPIJCNLP), pages 188–197.
10 Rigau, P. and Tienda, A. (2020). 100 bestselller books during covid-19 in spain. Zenodo. doi:10.5281/zenodo.3820050.
11 Sabri, N. and Weber, I. (2021). A global book reading dataset. Data, 6(8):83. doi:10.3390/data6080083
12 Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Comput. Surv., 34(1):1–47. doi:10.1145/505282.505283
13 Silva, M., Scofield, C., Oliveira, G., Seufitelli, D., and Moro, M. (2021a). Exploring Brazilian Cultural Identity Through Reading Preferences. In Anais do X Brazilian Workshop on Social Network Analysis and Mining, pages 115–126. SBC. doi:10.5753/brasnam.2021.16130
14 Silva, M. O., Scofield, C., and Moro, M. M. (2021b). PPORTAL: Public domain Portuguese-language literature Dataset. Zenodo. doi:10.5281/zenodo.5178063
15 Silva, M. O., Scofield, C., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2021c). BraCID: Brazilian Cultural Identity Information Through Reading Preferences. Zenodo. doi:10.5281/zenodo.4890048
16 Soares, F., Yamashita, G. H., and Anzanello, M. J. (2018). A parallel corpus of theses and dissertations abstracts. In International Conference on Computational Processing of the Portuguese Language, pages 345–352. Springer
17 Sousa, A. W. and Fabro, M. D. D. (2019). Iudicium textum dataset uma base de textos jur´ıdicos para nlp. In XXXIV Simp´osio Brasileiro de Banco de Dados: Dataset Showcase Workshop, SBBD 2019 Companion. SBC
18 Wagner Filho, J. A., Wilkens, R., Idiart, M., and Villavicencio, A. (2018). The brwac corpus: A new open resource for brazilian portuguese. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
19 Wan, M., Misra, R., Nakashole, N., and McAuley, J. J. (2019). Fine-grained spoiler detection from large-scale review corpora. In Procs. Conf. of the Association for Computational Linguistics (ACL), pages 2605–2610. doi:10.18653/v1/p19-1
20 Wang, X., Yucesoy, B., Varol, O., Eliassi-Rad, T., and Barabasi, A.-L. (2019). Success in books: predicting book sales before publication. EPJ Data Science, 8(31). doi:10.1140/epjds/s13688-019-0208-6
21 Yadollahi, A., Shahraki, A. G., and Zaiane, O. R. (2017). Current state of text sentiment analysis from opinion to emotion mining. ACM Comput. Surv., 50(2). doi:10.1145/3057270