combining the top (best) ranked methods, according to a recent benchmark study [@ribeiro2015benchmark]. We evaluate 10SENT with thirteen gold standard datasets containing social media data from different sources and contexts. Those datasets consist of different sets of labeled data annotated for positive, negative and neutral texts from social networks messages and from comments of news articles, videos, websites, and blogs. Our approach showed to be statistically superior to (or at least ties with) the existing individual methods in most datasets. As a consequence, our approach obtains the best mean rank position considering all datasets. Thus, our experimental results demonstrate that our combined method not only improves significantly the overall effectiveness in many datasets but its cross-dataset performance variability is minimal (maximum stability). In practical terms, this means that one can use our approach in any situation in which the base methods can be exploited, without any extra cost (since it is unsupervised) and without the need to discover the best method for a given context, and still obtain top-notch effectiveness in most situations.

We also show that 10SENT is superior to basic baseline combinations, such as a majority voting approaches, with gains of up to 17% against such baselines. This 