which is represented by a set of triplet $(i, predicted\_class, confidence)$ which is the instance, the predicted class and its confidence

**Add** the pair $(agreeClass(s),s)$ to $train$ **Remove** $s$ from $S$ **Create** a model $M$ using $train$ **Apply** the model $M$ in $S$ to obtain the predictions $P$

**Add** the pair $(predicted\_class,s)$ to $train$

**Create** a model $M$ using $train$\
**Apply** the model $M$ in $test$ to obtain the predictions $P$

The $test$ is represented by a set of sentences $test = \{s_{tst0}, s_{tst1}, s_{tst3},...,s_{tst_n}\}$ and, the prediction $P$, contains a set of triplets $(s, predicted\_class, confidence)$ representing the sentence, the predicted class and the confidence (i.e. a score representing how confident the machine learning method is in its prediction), respectively.

We use the function $agree(s)$, for each sentence $s$, which computes the Agreement level, in other words, the maximum number of sentiment analysis methods agreeing with each other regarding the sentiment in the sentence $s$. If this number is higher than the threshold $A$, we add the sentence $s$ in the training set $train$, removing it from $S$. Note that, when adding a sentence to $train$ we use the method $agreeClass(s)$ in order to obtain the class $c$ which 