Offensive Language Identification in Greek
Table 4: Results for offensive language detection with TF/IDF unigram features, POS and dependency relation tags. For each model, Precision (P), Recall (R), and F1 are reported on all classes, and weighted averages. Macro-F1 is also listed (best in bold).
['[BOLD] Model', '[BOLD] Not Offensive  [BOLD] P', '[BOLD] Not Offensive  [BOLD] R', '[BOLD] Not Offensive  [BOLD] F1', '[BOLD] Offensive  [BOLD] P', '[BOLD] Offensive  [BOLD] R', '[BOLD] Offensive  [BOLD] F1', '[BOLD] Weighted Average  [BOLD] P', '[BOLD] Weighted Average  [BOLD] R', '[BOLD] Weighted Average  [BOLD] F1', '[BOLD] F1 Macro']
Experiments with linguistic features were conducted, to inspect their efficiency for this task. For these experiments, the RBF SVM was not used due to data handling problems by the model in the scikit-learn library. In the first experiment, TF/IDF unigram features were combined with POS and dependency relation tags. While the Linear SVM model improved the recall score on the previous model trained with bigrams, the other models show a significant drop in their performance.