Offensive Language Identification in Greek
Table 6: Results for offensive language detection with TF/IDF unigram features 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']
The experiment with linguistic features was the combination of dependency relation tags with TF/IDF unigrams. This experimented yielded the same F1-score of 80% as the other Linear SVM classifiers, performing almost identically with the previous model trained with POS tags, only bested in precision for the Offensive class. While the recall score for Offensive instances improves on the first model trained only on TF/IDF unigrams by 0.01%, the recall score for Not Offensive instances drops by the same amount. The recall score for the Not Offensive class was already high, so this increase in recall score could slightly facilitate the offensive language detection task. Without improving upon the first SGDC presented, the SGDC rised in performance overall and as for the Naïve Bayes representatives, the both the Multinomial and Bernoulli approaches performed better than in the second experiment.