Offensive Language Identification in Greek
Table 2: Results for offensive language detection with TF/IDF unigram features. 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']
We can see that both linear classifiers (SVM and SGDC) outperform the other classifiers in terms of macro-F1, which does not take label imbalance into account. The Linear SVM and SGDC perform almost identically, with the Linear SVM performing slightly better in recall score for the Not Offensive class and SGDC in recall score for the Offensive class. Bernoulli Naïve Bayes performs better than all classifiers in recall score for the Offensive class but yields the lowest precision score of all classifiers. While the RBF SVM and Multinomial Naïve Bayes yield better recall score for the Not Offensive class, their recall scores for the Offensive class are really low. For a binary text classification task like offensive language detection, a high recall score for both classes, especially for the Offensive class, is important for a model to be considered successful. Thus, the Linear SVM can be considered the marginally best model trained with OGTD, as its weighted average precision and recall scores are higher.