Desenvolvimento de modelo para predição de cotações de ação baseada em análise de sentimentos de tweets
This is an incremental improvement for stock traders using social media data.
The paper tackled stock price prediction for Petrobras by using sentiment features from Twitter posts, achieving a net gain of R$88.82 over 250 days compared to random models.
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.