highlights the importance of our bootstrapped strategy to improve the effectiveness of the sentiment classification task. It is important to stress that the number of methods to be combined is not necessarily restricted to ten. Our self-learning approach is very independent of the base methods, which means that it is highly extensible to incorporate any new additional method that can be created in the future.

To summarize, the main contribution of our work is an easily deployable and stable method that can produce results as good or better than the best single method for most datasets (the performance of the base methods can vary a lot) in a completely unsupervised manner, being much superior than other unsupervised solutions such as majority voting and, in some cases, close to the best supervised ones. As far as we know, this is the first time non-trivial unsupervised learning is used along with “state-of-the-practice” sentiment analysis methods to solve important issues in the field such as stability, generality, and improved effectiveness, all at the same time.

Finally, as a second contribution, we start an investigation into a important question of our research: whether we can “transfer” some knowledge to our method from a dataset 