a combination of the learning and lexical algorithms. All techniques were tested separately and combined, but the authors did not obtain significant improvements with the hybrid approaches over the best individual techniques for this particular domain.

[@polly2016offtheshelf] analyzed different datasets and considered supervised machine learning in the context of classifiers’ ensembles. Their methodology also consisted of combining a set of different sentiment analysis method in a “off-the-self” strategy to generate the ensemble method. Their results suggest that it is possible to obtain significant improvements with ensemble techniques depending on the domain. In here, we focus on a unsupervised solution enhanced with an automatic bootstrapping step.

In a more recent effort on the ensemble direction, [@gonccalves2013comparing] exploits the power of the combination of some of the state-of-the-art methods, showing that they can outperform individual methods. Their results show the potential of simple solutions such as majority voting, but the authors did not delve deep in more complex combination strategies.

Some approaches use a limited amount of labeled data (also known as weakly supervised classifier) in order to predict the sentiment in some domains. For example, [@siddiqua2016] proposed a weakly supervised classifier for Twitter sentiment analysis. In this work, Naive-Bayes (NB) is 