all considered datasets. To compare the average results in the test sets of our experiments, we assess the statistical significance of our results by means of a paired t-test with 95% confidence. We just consider statistically significant, results whose the value of $p$ is less than 0.05 and any stated claim of superiority is based on these tests. Finally, we adapted the original outputs values of base methods to our corresponding polarities. In particular, an output equals to zero was considered as a neutral or “absence of opinion”.

Experimental Results
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Here, we discuss some decisions taken during the development of 10SENT and start some investigation on issues related to transfer learning for sentiment analysis, showing the potential of this technique to improve our results.

Choice of The Classifier
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10SENT is an unsupervised machine learning method as it does not exploit manually labeled data, only the agreement among the base methods. Given that the bootstrapping process adds a set of instances with high confidence into a training set, it is possible to perform a learning step exploiting such data in the the usual format training/validation. Because of this, there is a need to investigate which classifier fits better this 