# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 4 Experiments and Results
### Baseline Performance Assessment: Evaluating on Original Dataset  
When evaluating the performance of machine-learning (ML), deep-learning (DL), and Transformer models on the dataset, notable differences emerge across key metrics: Precision, Recall, F1-Score, Accuracy, and ROC AUC. These findings are summarized in Table 1 and visually represented in Figure 2. These metrics collectively provide a comprehensive assessment of each model's capabilities. In the ML category, Decision Tree achieved moderate Precision (0.572) and Recall (0.562), resulting in a balanced F1-Score (0.566) and fairly high Accuracy (0.907), although the ROC AUC (0.747) reflects only moderate discriminative ability. Random Forest demonstrates high Precision (0.980) but suffers from low Recall (0.420), leading to a moderate F1-Score (0.587), yet it boasts one of the highest Accuracy (0.936) and ROC AUC (0.889) values, suggesting strong overall performance. Support Vector Machine (SVM) also displays high Precision (0.989) but a low Recall (0.342), indicating confident predictions but a significant number of missed true positives, which lowers the F1-Score (0.506). Naive Bayes, while perfect in Precision (1.000), fails dramatically in Recall (0.023), leading to a misleadingly high Accuracy (0.894) and the lowest ROC AUC (0.630) in the ML group. Logistic regression shows good Precision (0.907) but struggles with Recall (0.194), reflected in a low F1-Score (0.312) despite a decent Accuracy (0.910) and ROC AUC (0.878). The Artificial Neural Network (ANN) provides a more balanced performance with better Recall (0.489) than most ML models, translating to a respectable F1-Score (0.552), Accuracy (0.915), and ROC AUC (0.827). In the DL category, models show varying strengths. The Convolutional Neural Network (CNN) has moderate Precision (0.745) and low Recall (0.340), resulting in an average F1-Score (0.449) and solid Accuracy (0.915) but slightly lower ROC AUC (0.845). LSTM models improve on this with higher Recall (0.345), enhancing their F1-Score (0.461) and maintaining strong Accuracy (0.919), though their ROC AUC remains moderate (0.811). GRU underperforms compared to LSTM, showing the lowest metrics among DL models with Precision (0.719), Recall (0.319), F1-Score (0.375), Accuracy (0.904), and ROC AUC (0.806). BiLSTM offers improved Recall (0.428) and a better F-score (0.518), positioning it as a competitive option in the DL domain with an Accuracy of 0.915 and ROC AUC of 0.820. It does not, however, outperform the most popular Transformer models. Transformer models have well-balanced metrics and outperform machine-learning (ML) and deep-learning (DL) models in many ways, as shown in Figure 2. BERT, in particular, has superior overall performance, as demonstrated by a high F1-Score (0.689), Accuracy (0.940), and a significant ROC AUC (0.922), as well as good Precision (0.733) and Recall (0.650). In contrast, ROBERTA has higher Precision (0.830) but lower Recall (0.409) while maintaining noteworthy overall metrics. DISTILBERT, a more compact Transformer model, delivers high performance by properly balancing Precision (0.895) and Recall (0.463). With adequate Recall (0.369) and slightly lower Precision (0.773), XLNET performs well, achieving a substantial F1-Score (0.500) and one of the highest ROC AUC values (0.907), demonstrating its capacity to handle complicated patterns. In contrast, despite its high Precision (0.843), ALBERT has somewhat limited performance metrics due to its poorer Recall (0.557). While ELECTRA excels at classification tasks with high Precision (0.912) and Recall (0.409), resulting in high F-scores (0.565), Accuracy (0.935), and ROC AUC (0.919). The performance evaluation is shown in Figures 3-5. The analysis underscores the trade-offs between Precision and Recall, with models like SVM, Naive Bayes, and Logistic Regression focusing on high Precision at the cost of low Recall, which may hinder their practical application when balanced detection is necessary.