## 4 Experimental Results
Support Vector Machine (SVM) consistently achieves strong performance across feature combinations, maintaining F1-weighted scores of 0.89. The addition of VADER sentiment features improves model performance, particularly for SVM and Random Forest classifiers. However, readability metrics (FKRA/FRE) show minimal impact on offensive language detection performance, suggesting their limited utility. Focusing on balanced model performance, the combination of TF-IDF and VADER features yields optimal results across classifiers. SVM and RF achieve F1-weighted scores of 0.89, demonstrating strong precision-recall balance, while LR shows comparable performance with an F1-weighted score of 0.88. We evaluated different combinations of GloVe word embeddings (dimensions: 50d, 100d, 200d, and 300d) with three neural network architectures (BiLSTM, CNN, MLP) on the Davidson dataset, as shown in Table 2. The 50d GloVe embeddings resulted in F1 scores between 0.84 and 0.87 across architectures, where BiLSTM and CNN performed better than MLP. Similarly, 100d embeddings produced F1 scores of 0.84-0.88, with BiLSTM and CNN again outperforming MLP. Using 200d GloVe embeddings, the CNN architecture reached an F1 score of 0.89, higher than both BiLSTM and MLP (F1: 0.84-0.85). The CNN model with 300d embeddings produced an F1 score of 0.88, with BiLSTM and MLP reaching F1 scores between 0.86 and 0.87. The CNN model with 200d GloVe embeddings achieved the highest F1 score (0.89) with balanced precision-recall, and the BiLSTM model reached its peak performance with 100d embeddings (F1: 0.87). As per the results in Table 3, \"bert encased preprocess/1 + small bert/L4H512A8/1 + neural network layers\" has the highest Macro F1 of 0.72. The \"bert encased preprocess/1 + small bert/L4H512A8/1 + neural network layers\" and \"bert encased preprocess/1 + electro small/2 + cnn\" have the highest weighted average F1 of 0.90. The \"bert encased preprocess/1 + small bert/L4H512A8/1 + neural network layers\" model appears to perform \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Embeddings** & **Model** & \multicolumn{3}{c}{**Macro**} & \multicolumn{3}{c}{**Weighted Avg.**} \\ \hline  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multicolumn{8}{c}{**Results on Davidson Dataset**} \\ \hline Glove 50d & BiLSTM & 0.86 & 0.86 & 0.85 & 0.87 & 0.88 & 0.87 \\ Glove 50d & CNN & 0.87 & 0.86 & 0.86 & 0.87 & 0.87 & 0.87 \\ Glove 50d & MLP & 0.85 & 0.84 & 0.84 & 0.84 & 0.84 & 0.84 \\ Glove 100d & BiLSTM & 0.87 & 0.87 & 0.86 & 0.88 & 0.86 & 0.87 \\ Glove 100d & CNN & 0.87 & 0.87 & 0.86 & 0.88 & 0.87 & 0.88 \\ Glove 100d & MLP & 0.85 & 0.86 & 0.84 & 0.86 & 0.86 & 0.86 \\ Glove 200d & BiLSTM & 0.85 & 0.85 & 0.84 & 0.85 & 0.85 & 0.84 \\ Glove 200d & CNN & 0.89 & 0.87 & 0.87 & 0.89 & 0.89 & 0.89 \\ Glove 200d & MLP & 0.85 & 0.84 & 0.83 & 0.85 & 0.85 & 0.85 \\ Glove 300d & BiLSTM & 0.88 & 0.87 & 0.87 & 0.88 & 0.87 & 0.87 \\ Glove 300d & CNN & 0.88 & 0.86 & 0.86 & 0.88 & 0.87 & 0.88 \\ Glove 300d & MLP & 0.86 & 0.85 & 0.85 & 0.86 & 0.86 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the GloVe embedding with DL model. well in terms of both macro and weighted average F1, indicating a good balance between precision and recall for both macro-average and weighted-average cases. The \"albert en base/2 + alert encoder + cnn\" model has a lower macro F1 (0.50), suggesting that it have imbalanced performance in terms of precision and recall for different classes. The \"bert encased preprocess/1 + small bert/L4H512A8/1 + cnn\" demonstrated consistent performance in terms of both macro and weighted average F1. The \"bert encased preprocess/1 + small bert/L4H512A8/1 + neural network layers\" provided a balanced performance across both metrics. We have analyzed both existing research results and our findings as shown in Table 4. To determine the best model for OLD, we prioritized balanced performance.