<hi xmlns="http://www.tei-c.org/ns/1.0" rend="">The dictionary-based SVM classifier reached a macro </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">F1</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend=""> </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="">score of 0.64, while the best performance was obtained by SentiArt (see Fig. 3). Efficiency was again higher for FastText (macro </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">F1 </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="">score = 0.72) and best for BERT (macro </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">F1</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend=""> </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="">score = 0.83). However, the learning curve for BERT shows how there is still room for improvement, with efficiency not fully reaching a plateau (see Fig. 4).</hi>