Studying the Effects of Text Preprocessing and Ensemble Methods on Sentiment Analysis of Brazilian Portuguese Tweets

The analysis of social media posts can provide useful feedback regarding user experience for people and organizations. This task requires the use of computational tools due to the massive amount of content and the speed at which it is generated. In this article we study the effects of text preprocessing heuristics and ensembles of machine learning algorithms on the accuracy and polarity bias of classifiers when performing sentiment analysis on short text messages. The results of an experimental evaluation performed on a Brazilian Portuguese tweets dataset have shown that these strategies have significant impact on increasing classification accuracy, particularly when the ensembles include a deep neural net, but not always on reducing polarity bias.