Technologies in 2017. The PhD thesis "Mining Social Media Sentiment to Forecast Stock Market Behavior" analyzes the predictive value of sentiment and attention indicators extracted from social media data for various stock market variables (e.g., returns, volatility, trading volume) of different types of stocks (e.g., indexes, portfolios, aggregate market) by applying a robust methodology (e.g., large datasets, different sentiment sources (e.g., Twitter, StockTwits, surveys), diverse Machine Learning methods, statistical test of predictive accuracy). The most relevant experiments executed during this PhD were published in two journal papers, three conference papers and one book chapter.His main research interests are: Machine Learning, Deep Learning, Natural Language Processing, Time Series Prediction, Geographical Forecasting, Social Network Analysis, Behavioral Finance