Tweet and News Sentiment Indicators and the Behavior of the Brazilian Stock Market

In this work, we explore machine learning to ob-tain sentiment indicators from financial market text messages collected from Twitter and news in Portuguese. A statistical analysis was carried out with Sperman’s correlation coefficient between sentiment indicators and actual variables in the Brazilian financial market. For sentiment analysis, the MaxEnt and CNN models provided F1-scores of 85% and 96%, for tweets and news, respectively. A result shown a moderate correlation (Cohen’s scale) between some variables such as, amount of tweets and sentiment of the news; market variation and tweet sentiment; amount of retweets published and sentiment of the news; trading volume and tweet sentiment. Moreover, a very large correlation was identified between the amount of negative tweets and retweets, leading to the belief that pessimism is often propagated.