Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification
Table 2: F1-scores of compared methods on the test sets. The highest scores among the three proposed models are highlighted in bold face. ML-CNN and FML-CNN are two variants of the method presented in Section 5.1.
['[BOLD] Method', '[BOLD] Language  [BOLD] English', '[BOLD] Language  [BOLD] French', '[BOLD] Language  [BOLD] German', '[BOLD] Language  [BOLD] Italian']
The fully-trained SL-CNNs significantly outperforms the other methods in all four languages. The best F1-score was achieved for Italian (67.79%), followed by German (65.09%) and French (64.79%), while the system for English reached only 62.26%. The proposed SL-CNNs outperform the corresponding baselines from literature and RF.