Machine learning based sentiment analysis on Spanish financial tweets

Abstract

Nowadays, financial data on social networks play an important role to predict the stock market. However, the exponential growth of financial information on social networks such as Twitter has led to a need for new technologies that automatically collect and categorise large volumes of information in a fast and easy manner. The Natural Language Processing (NLP) and sentiment analysis areas can solve this problem. In this respect, we propose a supervised machine learning method to detect the polarity of financial tweets. The method employs a set of lexico-morphological and semantic features, which were extracted with UMTextStats tool. Furthermore, we have conducted a comparison of the performance of three classification algorithms (J48, BayesNet, and SMO). The results showed that SMO provides better results than BayesNet and J48 algorithms, obtaining an F-measure of 73.2%.