Sentiment Analysis for Streams of Web Data: A Case Study of Brazilian Financial Markets

Abstract

With the rise of Web 2.0 applications, most people started consuming information and sharing opinions and ideas about most aspects of their lives on a variety of social media platforms, creating massive and continuous streams of valuable data. While this opened the door for information extraction and mining techniques that can help us understand different aspects of society, extracting useful information from such streams of Web data is far from trivial. In this setting, sentiment analysis techniques can be convenient as they are capable of summarizing general feeling about entities people care about, such as products and companies. Therefore, they can be quite applicable in scenarios like the stock market, which also has tremendous impact on society. This paper describes and evaluates two different techniques for sentiment analysis applied to the Brazilian stock market data: lexicon-based and machine learning based, considering a wide range of text pre-processing and feature selection approaches.