Desenvolvimento de modelo para predição de cotações de
  ação baseada em análise de sentimentos de tweets
Mario Mitsuo Akita Everton Josue da Silva
Training machine learning models for predicting stock market share prices is
an active area of research since the automatization of trading such papers was
available in real time. While most of the work in this field of research is
done by training Neural networks based on past prices of stock shares, in this
work, we use iFeel 2.0 platform to extract 19 sentiment features from posts
obtained from microblog platform Twitter that mention the company Petrobras.
Then, we used those features to train XBoot models to predict future stock
prices for the referred company. Later, we simulated the trading of Petrobras'
shares based on the model's outputs and determined the gain of R$88,82 (net) in
a 250-day period when compared to a 100 random models' average performance.