# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 4 Experiments and Results
### Enhancing Model Performance: A Comparative Analysis with Augmented Dataset  
The analysis of the enriched dataset reveals significant improvements in performance metrics across all categories, including ML, DL, and Transformer models. These enhancements are detailed in Table 2 and visually represented in Figure 6. Furthermore, Figures 7-9 show a detailed evaluation of the improvement of accuracy in terms of the performance of the ROC curve. Starting with ML models, the Decision Tree reaches near-perfect Precision (0.997), Recall (0.993), and the F1-Score (0.995), leading to notable Accuracy (0.999) and ROC AUC. The Random Forest model delivers perfect performance on all evaluation metrics, demonstrating its excellence with Accuracy and an ROC AUC score of (1.000). Support Vector Machine (SVM) also demonstrates strong performance, with high Precision (0.989), Recall (0.914), and an F1-Score (0.949), maintaining perfect discriminative power (ROC AUC 1.000). Naive Bayes, while achieving high Precision (0.992), continues to lag in Recall (0.287), reflected in a low F1-Score (0.440) despite an overall Accuracy (0.922) and high ROC AUC (0.994). Logistic regression shows notable improvement with high Precision (0.992), Recall (0.837), and an F1-Score (0.908), achieving an Accuracy of 0.982 and an almost perfect ROC AUC of 0.999. The Artificial Neural Network follows closely, aligning its Precision (0.994) and Recall (0.843) to yield a strong F1-Score (0.912) and high Accuracy (0.982) with an ROC AUC of 0.993. In the DL category, models like CNN, LSTM, GRU, and BiLSTM achieve perfect scores on all metrics, boasting (1.000) for Precision, Recall, F1-Score, Precision, and ROC AUC. This indicates that these models handle enriched data exceptionally well, maximizing classification efficiency and discriminative capability without any apparent weaknesses. Figures 7-9 show the performance evaluation. The Transformer models also exhibit notable performance. BERT and RoBERTa exhibit strong performance with Precision scores of 0.974 and 0.942, Recall scores of 0.931 and 0.956, and F1-scores of 0.952 and 0.949, respectively. Both models achieve high Accuracy (0.990 and 0.989) and near-perfect ROC AUC values of 0.999. DISTILBERT slightly outperforms these models with Precision, Recall, and an F1-Score, all at (0.975), accompanied by excellent Accuracy (0.995) and the same ROC AUC (0.999), showcasing its efficiency despite being a lighter model. XLNET follows with Precision (0.965) and Recall (0.946), achieving an F1-Score of 0.955 and maintaining high Accuracy (0.991) and ROC AUC (0.999). ALBERT, \begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Category** & **Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **ROC AUC** \\ \hline \multirow{8}{*}{**ML**} & DT & 0.997 & 0.993 & 0.995 & 0.999 & 0.996 \\  & RF & 1.000 & 0.997 & 0.998 & 1.000 & 1.000 \\  & SVM & 0.989 & 0.914 & 0.949 & 0.989 & 1.000 \\  & NB & 0.992 & 0.287 & 0.440 & 0.922 & 0.994 \\  & LR & 0.992 & 0.837 & 0.908 & 0.982 & 0.999 \\  & ANN & 0.994 & 0.843 & 0.912 & 0.982 & 0.993 \\ \hline \multirow{8}{*}{**DL**} & CNN & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\  & LSTM & 1.000 & 0.998 & 0.999 & 1.000 & 1.000 \\  & GRU & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\  & BiLSTM & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \multirow{8}{*}{**Transformer**} & BERT & 0.974 & 0.931 & 0.952 & 0.990 & 0.999 \\  & ROBERTA & 0.942 & 0.956 & 0.949 & 0.989 & 0.999 \\ \cline{1-1}  & DISTILBERT & 0.975 & 0.975 & 0.975 & 0.995 & 0.999 \\ \cline{1-1}  & XLNET & 0.965 & 0.946 & 0.955 & 0.991 & 0.999 \\ \cline{1-1}  & ALBERT & 0.926 & 0.931 & 0.929 & 0.985 & 0.999 \\ \cline{1-1}  & ELECTRA & 0.970 & 0.961 & 0.965 & 0.993 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy metrics for different models after enriching dataset across ML, DL, and Transformer categories. while not surpassing most models, still performs solidly with Precision (0.926), Recall (0.931), and an F1-Score of 0.929, combined with Accuracy (0.985) and a perfect ROC AUC (0.999).