Sentiment Analysis Applied to News from the Brazilian Stock Market



Sentiment Analysis, Text Mining, Stock Market


Investments in the stock market have grown in Brazil in recent years, especially considering the individual number of investors. According to data from April 2020, the Brazilian stock market reached the historic mark of 2.38 million active investors, and with this scenario, there is an increasing need to study the Brazilian financial market, seeking to better understand its fluctuations. Recent work in the literature indicates that a company’s stock values can be influenced by published news. Therefore, this work contributes to the automatic sentiment analysis applied to news written in Portuguese and related to the Brazilian stock market. For this, we performed three sentiment analysis strategies: two based on machine learning, using the Naive Bayes classifier and a Multilayer Perceptron neural network; and the other based on the lexical approach. Also, we proposed two dictionaries, focused on the financial domain and adapted to Portuguese. Our results show that the Naive Bayes classifier and the Multilayer Perceptron overcomes the best lexical approach. It is worth mentioning that the accuracy achieved by the best lexical approach was with the adapted dictionary proposed here.


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Author Biographies

Brenda A. Januário, University of Campinas (UNICAMP)

Brenda Alexsandra Januário is graduating in Information Systems at the School of Technology of the University of Campinas - UNICAMP. She is currently a Data Engineer at Itaú Unibanco and her areas of interest are cloud computing, data mining and machine learning.

Arthur Emanuel de O. Carosia, University of Campinas (UNICAMP) and Federal Institute of São Paulo (IFSP)

Arthur Emanuel de Oliveira Carosia is a Ph.D. student at the School of Technology of the University of Campinas, Brazil. He is a Professor at the Instituto Federal de São Paulo (IFSP), Brazil. His research interests are Data Science and Computational Intelligence, with recent work on machine learning and stock market prediction.

Ana Estela A. da Silva, University of Campinas (UNICAMP)

Ana Estela Antunes da Silva has an undergraduation degree in Computer Science from the University of Campinas – UNICAMP, a Master degree in Computer Science from Massey University and a Ph.D. in Computer Engineering from the University of Campinas. She is currently a teacher in the School of Technology at University of Campinas. Has experience in the area of Computer Science, with emphasis on: text mining, data mining and intelligent systems for decision making.

Guilherme P. Coelho, University of Campinas (UNICAMP)

Guilherme Palermo Coelho is a Computer Engineer (University of Campinas - UNICAMP), with M.Sc. and Ph.D. degrees in Electrical Engineering (also from UNICAMP). He is an IEEE Senior Member and currently an Assistant Professor at the School of Technology of the University of Campinas, Brazil. His research interests are associated with Computational Intelligence in general, with recent work on metaheuristics for optimization (single and multi-objective), data mining, and machine learning.


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How to Cite

Januário, B. A., Carosia, A. E. de O., Silva, A. E. A. da, & Coelho, G. P. (2021). Sentiment Analysis Applied to News from the Brazilian Stock Market. IEEE Latin America Transactions, 20(3), 512–518. Retrieved from