Offensive Language Identification in Greek
Table 5: Results for offensive language detection with TF/IDF unigram features and POS 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']
In the next experiment, POS tags were used in conjunction with TF/IDF unigram features. Surprisingly, the addition of POS tags in the Linear SVM yields the same F1-score as the first model trained on TF/IDF unigram features, yielding lower precision scores for both classes, while the recall score for the Offensive class improved marginally. The Naïve Bayes models show a marginal decrease in their performance. On the other hand, the performance of SGDC significantly decreases with POS tags only and, interestingly enough, its recall score for the Offensive class is the worst among classifiers.