describe the method in Algorithm \[alg:bootstrapping\]. Suppose we have access to a set of sentences $S = \{s_{tr0}, s_{tr1}, s_{tr3},...,s_{tr_n}\}$, which are candidates of being part of our training data. Our goal is to use the unlabeled data $S$ in order to produce a training set $train$ and, then, apply it to unseen sentences for which we want to predict (here represented as $test = \{s_{tst0}, s_{tst1}, s_{tst3},...,s_{tst_m}\}$), generating the set of predictions $P$. The training data $train$ is represented by a set of pairs $(c,s)$ where $c$ is the class representing a sentiment (positive, negative or neutral) obtained by using the information of each sentiment analysis method described in Section \[sec:methods\] and $s$ is a sentence represented by a set of features which, in our case, corresponds to the off-the-shelf sentiment methods’ outputs.

Minimum of Agreement $A$ Minimum of Confidence $C$ The set of $n$ sentences $S = s_{tr0}, s_{tr1}, s_{tr3},...,s_{tr_n}$, candidates of being part of our training data The set of $m$ sentences which we want to predict: $test = \{s_{tst0}, s_{tst1}, s_{tst3},...,s_{tst_m,}\}$

Let $train$ = our training set represented by $(c,s)$ which $c$ is the target class and $s$ is the sentence

Let $P$ = our result 