will the sentiment assigned to $s$. Class $c$ is obtained by using the class which has the majority of sentiment analysis methods assigned to the sentence $s$.

After doing this for all the sentences in $S$, only sentences for which we could not infer a label with enough agreement remain in $S$. Then, in order to increase our training data, we use our training set $train$ to train a classification model and apply it to sentences in $S$, producing the predictions $P$. By doing so, we are able to use $P$ to add more sentences to $train$. In order to avoid noise, we only add sentences for which the learned model produces a confidence higher than a threshold $C$. Finally, we retrain with the new set $train$ and apply it to $test$ in order to produce, for each sentence $s$, a single score $c$ representing its final sentiment score.

As mentioned before, our approach consists of combining popular **“off-the-shelf” sentiment analysis methods** freely available for use. It is important to highlight that the number of methods to be combined is not necessarily restricted to ten. In fact, there is no limit on the number of methods we can include as 