# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 4 Experiments and Results
### Evaluation Metrics  
The evaluation metrics employed to assess the performance of the models--Precision, Recall, F1-Score, Accuracy, and ROC AUC--are crucial for assessing different aspects of model performance [45], particularly in complex and imbalanced datasets. These are calculated as shown in the following:  
Precision measures the ratio of true-positive predictions to the total number of positive predictions (true positives plus false positives). Mathematically, Precision is defined as:  
\[\text{Precision}=\frac{TP}{TP+FP}\]  
This metric is essential for understanding how exact a model is when predicting positive outcomes, making it particularly important in applications where false positives have significant consequences.  
Recall, or sensitivity, is another vital metric that reflects the model's ability to identify all actual positive cases, calculated as:  
\[\text{Recall}=\frac{TP}{TP+FN}\]  
High Recall indicates that the model effectively captures positive instances, which is critical in fields such as medical diagnosis, where missing true positives (false negatives) can be highly detrimental. However, a model optimized for high Recall may suffer from lower Precision, as it may capture more false positives.  
The F1-Score balances Precision and Recall, providing a single measure that incorporates both. It is represented by the harmonic mean:  
\[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}\]  
This metric is particularly useful in scenarios where a balance between Precision and Recall is needed, especially when dealing with imbalanced datasets where Accuracy alone might be misleading.  
Accuracy is defined as:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}\]  
It is a straightforward measure representing the overall proportion of correctly predicted instances. While it provides a general sense of a model's effectiveness, it can be inadequate for imbalanced data where Precision, Recall, and the F1-Score give deeper insights into the model's performance.  
Finally, the ROC AUC (Receiver Operating Characteristic-Area Under the Curve) evaluates the model's ability to differentiate between classes across different thresholds. The ROC AUC is mathematically represented by:  
\[\text{ROC AUC}=\int_{0}^{1}TPR(FPR)\,d(FPR)\]  
Where  
- True Positive (TP): An instance that is positive and is classified correctly as positive.  
- True Negative (TN): An instance that is negative and is classified correctly as negative.  
- False Positive (FP): An instance that is negative but is classified wrongly as positive.  
- False Negative (FN): An instance that is positive but is classified wrongly as negative.  
These evaluation metrics were selected for their ability to provide a comprehensive understanding of model performance. Precision, Recall, and the F1-Score ensure that both the positive predictive power and coverage are assessed, while Accuracy offers a general performance snapshot, and ROC AUC provides insight into how well the model separates classes under varying conditions.