Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media

References

• Arias, M., A. Arratia, and R. Xuriguera. 2013. Forecasting with twitter data. ACM Transactions on Intelligent Systems and Technology 5 (1):1–24. doi:10.1145/2542182.
   Web of Science ®Google Scholar
• Assis, C. A. S., E. J. Machado, A. C. M. Pereira, and E. G. Carrano. 2018. Hybrid deep learning approach for financial time series classification. Revista Brasileira De Computação Aplicada 10 (2):54–63. doi:10.5335/rbca.v10i2.7904.
   Web of Science ®Google Scholar
• Atsalakis, G. S., and K. P. Valavanis. 2009. Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications 36 (3):5932–41. doi:10.1016/j.eswa.2008.07.006.
   Web of Science ®Google Scholar
• Avanço, L. V., and M. D. G. Volpe Nunes. 2014. Lexicon-based sentiment analysis for reviews of products in Brazilian Portuguese. Intelligent Systems (BRACIS), 2014 Brazilian Conference on, 277–81. IEEE, São Carlos, SP, Brazil. doi:10.1094/PDIS-04-13-0439-PDN.
   Google Scholar
• Bollen, J., H. Mao, and X. Zeng. 2011. Twitter mood predicts the stock market. Journal of Computational Science 2 (1):1–8. doi:10.1016/j.jocs.2010.12.007.
   Web of Science ®Google Scholar
• Corder, G. W., and D. I. Foreman. 2014. Nonparametric statistics: A step-by-step approach. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Economia, G. 1. G. 2018a. Bovespa bate recorde após eleição de Bolsonaro, mas fecha em queda à espera de detalhes do novo governo. Accessed April 16, 2019. https://g1.globo.com/economia/noticia/2018/10/29/bovespa-29102018.ghtml.
   Google Scholar
• Economia, G. 1. G. 2018b. Bovespa fecha em queda de 2,8% com cenário eleitoral; Eletrobras recua forte. Accessed April 16, 2019. https://g1.globo.com/economia/noticia/2018/10/10/bovespa-10102018.ghtml.
   Google Scholar
• Economia, U. O. L. 2018f. Dólar passa de R$ 4 após pesquisas eleitorais; até onde vai a moeda? Accessed April 16, 2019. https://economia.uol.com.br/cotacoes/noticias/redacao/2018/08/21/cotacao-dolar-alta-eleicoes-pesquisas-limite-maxima.htm.
   Google Scholar
• Econômico, V. 2018. Jogo eleitoral pode levar Ibovespa a 45 mil ou a 170 mil pontos. Accessed April 16, 2019. https://www.valor.com.br/financas/5576153/jogo-eleitoral-pode-levar-ibovespa-45-mil-ou-170-mil-pontos.
   Google Scholar
• Feuerriegel, S., and H. Prendinger. 2016. News-based trading strategies. Decision Support Systems 90:65–74. doi:10.1016/j.dss.2016.06.020.
   Web of Science ®Google Scholar
• Francis, J. C., and E. Kirzner. 1991. Investments: Analysis and management. New York, NY: McGraw-Hill.
   Google Scholar
• Globo, O. 2018. Após 1° turno, Bolsa registra maior volume financeiro da história. Accessed April 16, 2019. https://oglobo.globo.com/economia/apos-1-turno-bolsa-registra-maior-volume-financeiro-da-historia-23139170.
   Google Scholar
• Hájek, P. 2018. Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Neural Computing and Applications 29 (7):343–58. doi:10.1007/s00521-017-3194-2.
   Web of Science ®Google Scholar
• Haykin, S. 1994. Neural networks: A comprehensive foundation. Upper Saddle River, NJ: Prentice Hall PTR.
   Google Scholar
• Hussein, D. M. E.-D. M. 2018. A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences 30 (4):330–38. doi:10.1016/j.jksues.2016.04.002.
   Google Scholar
• Kibriya, A. M., E. Frank, B. Pfahringer, and G. Holmes. 2004. Multinomial naive bayes for text categorization revisited. Australasian Joint Conference on Artificial Intelligence, 488–99. Berlin, Heidelberg: Springer.
   Google Scholar
• Kraus, M., and S. Feuerriegel. 2017. Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems 104:38–48. doi:10.1016/j.dss.2017.10.001.
   Web of Science ®Google Scholar
• Li, Q., T. Wang, L. Ping, L. Liu, Q. Gong, and Y. Chen. 2014. The effect of news and public mood on stock movements. Information Sciences 278:826–40. doi:10.1016/j.ins.2014.03.096.
   Web of Science ®Google Scholar
• Lima, M. L., T. P. Nascimento, S. Labidi, N. S. Timbó, M. V. L. Batista, G. N. Neto, E. A. M. Costa, and S. R. S. Sousa. 2016. Using sentiment analysis for stock exchange prediction. International Journal of Artificial Intelligence & Applications (IJAIA) 7 (1): 59–67.
   Google Scholar
• Long, D., J. Bradford, A. Shleifer, L. H. Summers, and R. J. Waldmann. 1990. Noise trader risk in financial markets. Journal of Political Economy 98 (4):703–38. doi:10.1086/261703.
   Web of Science ®Google Scholar
• Loughran, T., and B. Mcdonald. 2011. Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Journal of Finance 66 (1):35–65. doi:10.1111/j.1540-6261.2010.01625.x.
   Web of Science ®Google Scholar
• Makrehchi, M., S. Shah, and W. Liao. 2013. Stock prediction using event-based sentiment analysis. 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 337–42. IEEE, Atlanta, GA, USA, November.
   Google Scholar
• Malkiel, B. G. 2003. The efficient market hypothesis and its critics. Journal of Economic Perspectives 17 (1):59–82. doi:10.1257/089533003321164958.
   Web of Science ®Google Scholar
• Malkiel, B. G., and E. F. Fama. 1970. Efficient capital markets: A review of theory and empirical work. The Journal of Finance 25 (2):383–417. doi:10.1111/j.1540-6261.1970.tb00518.x.
   Web of Science ®Google Scholar
• Martins, R. F., A. Pereira, and F. Benevenuto. 2015. An approach to sentiment analysis of web applications in portuguese. Proceedings of the 21st Brazilian Symposium on Multimedia and the Web, 105–12. Manaus, AM: ACM.
   Google Scholar
• Mocherla, S., A. Danehy, and C. Impey. 2017. Evaluation of naive bayes and support vector machines for wikipedia. Applied Artificial Intelligence 31 (9–10):733–44. doi:10.1080/08839514.2018.1440907.
   Web of Science ®Google Scholar
• Nofer, M., and O. Hinz. 2015. Using Twitter to predict the stock market. Business & Information Systems Engineering 57 (4):229–42. doi:10.1007/s12599-015-0390-4.
   Web of Science ®Google Scholar
• Pang, B., L. Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2 (1–2):1–135. doi:10.1561/1500000011.
   Google Scholar
• Pang, B., L. Lee, and S. Vaithyanathan. 2002. Thumbs up?: Sentiment classification using machine learning techniques. Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, EMNLP ‘02, Stroudsburg, PA, USA, 79–86. Association for Computational Linguistics.
   Google Scholar
• Ravi, K., and V. Ravi. 2015. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems 89:14–46. doi:10.1016/j.knosys.2015.06.015.
   Web of Science ®Google Scholar
• Santos, H. S., A. H. Laender, and A. C. Pereira. 2015. A twitter view of the Brazilian stock exchange market. Lecture Notes in Business Information Processing 239:112–23.
   Google Scholar
• Schumaker, R. P., Y. Zhang, C.-N. Huang, and H. Chen. 2012. Evaluating sentiment in financial news articles. Decision Support Systems 53 (3):458–64. doi:10.1016/j.dss.2012.03.001.
   Web of Science ®Google Scholar
• Sun, S., C. Luo, and J. Chen. 2017. A review of natural language processing techniques for opinion mining systems. Information Fusion 36:10–25. doi:10.1016/j.inffus.2016.10.004.
   Web of Science ®Google Scholar
• Taboada, M., J. Brooke, M. Tofiloski, K. Voll, and M. Stede. 2011. Lexicon-based methods for sentiment analysis. Technical Report 2.
   Google Scholar
• Vargas, M. R., B. S. L. P. de Lima, and A. G. Evsukoff. 2017. Deep learning for stock market prediction from financial news articles. 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 60–65. IEEE, Annecy, France, June.
   Google Scholar
• Yan, D., G. Zhou, X. Zhao, Y. Tian, and F. Yang. 2016. Predicting stock using microblog moods. China Communications 13 (8):244–57. doi:10.1109/CC.2016.7563727.
   Web of Science ®Google Scholar
• Yoshihara, A., K. Fujikawa, K. Seki, and K. Uehara. 2014. Predicting stock market trends by recurrent deep neural networks. In Springer International Publishing, ed. by D. N. Pham and S. P. Park, 759–69. Cham: Springer.
   Google Scholar
• Zhao, B., H. Yongji, C. Yuan, and Y. Huang. 2016. Stock market prediction exploiting microblog sentiment analysis. 2016 International Joint Conference on Neural Networks (IJCNN), 4482–88. IEEE, Vancouver, BC, Canada, July.
   Google Scholar