be used very carefully, especially for unknown datasets. More importantly, it suggests that novel sentiment analysis methods should not only be superior to existing ones in terms of predictive performance, but they should also be stable, that is, its relative prediction performance should vary minimally when used in many different datasets and contexts.

Accordingly, in this article, we propose 10SENT, an unsupervised learning approach for sentence-level sentiment classification that tells if a given piece of text (i.e. a tweet) is positive, negative, or neutral. In order to obtain better results than existing methods and guarantee stability across datasets, our approach exploits the combination of their classification outputs in a smart way. Our strategy relies on using a bootstrapped learning classifier that creates a training set based on a combination of answers provided by existing unsupervised methods. The intuition is that if the majority of the methods label an instance as positive, it is likely that it is positive, and it could be used to learn a classifier. This self-learning step provides to our method a level of adaptability to the current (textual) context, reducing prediction performance instability, a key aspect of an unsupervised approach.

We test our proposed approach by 