  The increase in the number of Internet users and the strong interaction
brought by Web 2.0 made the Opinion Mining an important task in the area of
natural language processing. Although several methods are capable of performing
this task, few use multi-label classification, where there is a group of true
labels for each example. This type of classification is useful for situations
where the opinions are analyzed from the perspective of the reader, this
happens because each person can have different interpretations and opinions on
the same subject. This paper discuss the efficiency of problem transformation
methods combined with different classification algorithms for the task of
multi-label classification of reactions in news texts. To do that, extensive
tests were carried out on two news corpora written in Brazilian Portuguese
annotated with reactions. A new corpus called BFRC-PT is presented. In the
tests performed, the highest number of correct predictions was obtained with
the Classifier Chains method combined with the Random Forest algorithm. When
considering the class distribution, the best results were obtained with the
Binary Relevance method combined with the LSTM and Random Forest algorithms.
