exploits their strengths while overcome their limitations. The idea of combining different sentiment analysis strategies, however, has been only recently explored, but most of the existing literature on the combination of sentiment analysis involves a learning component.

For instance, [@prabowo2009sentiment] proposed a new hybrid classification method based on the combination of different strategies. This work combines a rule-based classification and other supervised learning strategies into a new hybrid sentiment classifier. [@DangZC10] combined machine learning and semantic-orientation that consider words expressing positive or negative sentiments.

[@zhang] explored an entity-level sentiment analysis method specific to the Twitter data. In that work, the authors combined lexicon and learning-based methods in order to increase the recall rate of individual methods. Differently from our work, this method was proposed for the entity-level, while ours focus on a sentence-level granularity. Similarly, [@Mudinas2012] proposed *pSenti*, a method for sentiment analysis developed as a combination of lexicon and learning approaches for a different granularity level, the concept-level (semantic analysis of texts by means of web ontologies or semantic networks).

[@MoraesVPDAG13] investigated approaches to detect the polarity of FourSquare tips using supervised (SVM, Maximum Entropy and Na[ï]{}ve Bayes) and unsupervised (SentiWordNet) learning. They also investigate hybrid approaches, developed as 