Agradecimentos: The authors thank Espaço da Escrita - Pró-Reitoria de Pesquisa - UNICAMP - for the language services provided. We also thank MSc. Renato Martins (CEFET-MG), PhD Adriano Pereira (UFMG), and PhD Fabrício Benevenuto (UFMG) for providing the databases used for the experiments of this...

Agradecimentos: The authors thank Espaço da Escrita - Pró-Reitoria de Pesquisa - UNICAMP - for the language services provided. We also thank MSc. Renato Martins (CEFET-MG), PhD Adriano Pereira (UFMG), and PhD Fabrício Benevenuto (UFMG) for providing the databases used for the experiments of this study

Abstract: According to the Efficient Market Hypothesis, financial market movements are dependent on news and external events that have a significant impact on the market value of companies. Thus, a great amount of applications has arisen to explore this knowledge through automatic sentiment and...

Abstract: According to the Efficient Market Hypothesis, financial market movements are dependent on news and external events that have a significant impact on the market value of companies. Thus, a great amount of applications has arisen to explore this knowledge through automatic sentiment and opinion extraction. The technique known as Sentiment Analysis (SA) aims to analyze opinions, sentiments, and emotions present in unstructured data, leading many papers to address the impact of news and social media publications on the financial market. However, the literature lacks works considering the effects of sentiment available on social media and its impacts on the Brazilian stock market. This work aims to conduct a study of the Brazilian stock market movement through SA in Twitter considering tree perspectives: (i) absolute number of tweet sentiments; (ii) tweets sentiments weighted by favorites; and (iii) tweets sentiments weighted by retweets. The analyzed period was the Brazilian electoral period of 2018 (01-Oct-2018 to 31-Dec-2018). In this paper, we first developed a comparison study with SA Machine Learning techniques (Naive Bayes, Support Vector Machines, Maximum Entropy, and Multilayer Perceptron) and then applied the best algorithm to establish the relations between sentiments and the Brazilian stock market movement considering different time frames (windows sizes). Results indicate that Multilayer Perceptron was the best technique to perform SA in Portuguese. In addition, we observed that the predominant sentiment in social media relates to the stock market movement, improving accuracy as long as windows sizes are increased

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