of them rely on monitoring what people think or talk about places, companies, brands, celebrities or politicians [@Hu:2004:MSC:1014052.1014073; @conf/epia/OliveiraCA13; @DBLP:journals/corr/abs-1010-3003].

Due to the enormous interest and applicability, many methods have been proposed in the last few years (e.g., SentiStrength [@thelwall2013heart], VADER [@hutto2014vader], Umigon[@levallois2013umigon], SO-CAL[@taboada2011lexicon]). In common, these methods are unsupervised[^1] tools and have been applied to identify sentiments (i.e. positive, negative, and neutral) of short pieces of text such as tweets, in which the subject discussed in the text is known *a priori*. The importance of being unsupervised is that, in a real application of sentiment analysis, it can be very hard to get previous labeled data to train a classifier.

These tools are all currently acceptable by the research community as the state-of-the-art is not well established yet. However, a recent effort [@ribeiro2015benchmark] has shown that the prediction performance of these methods varies considerably from one dataset to another. For instance, in that study, Umigon was ranked in the first position in five datasets containing tweets and was among the worst in a dataset of news comments. Even among similar datasets, existing methods showed **low stability** in terms of their ranked positions. This suggests that existing unsupervised approaches should 