## 2 Materials and Methods
### Model Performance Evaluation  
In this study, the model's performance was evaluated using a confusion matrix, which includes metrics such as accuracy, precision, recall, and F1-score [23]. The confusion matrix provides a comprehensive view of how well the model classifies data into the correct categories by measuring the number of correct (true) and incorrect (false) predictions. A deeper understanding of the model's performance can be gained through the confusion matrix, including whether the model is more effective at detecting one category [11]. Accuracy is an important metric to determine how well the model correctly classifies data. It is calculated by comparing the number of correct predictions (both positive and negative) to the total number of predictions made by the model [17]. The formula for calculating accuracy is expressed in Equation (3) below:  
\[Accuracy=\frac{(TP+TN)}{(TP+FN+TN+FP)} \tag{3}\]  
* TP (True Positives) is the number of correct positive predictions. * TN (True Negatives) is the number of correct negative predictions. * FP (False Positives) is the number of incorrect positive predictions. * FN (False Negatives) is the number of incorrect negative predictions. This precision metric is critical in assessing the model's effectiveness in making accurate positive predictions, ensuring that each positive prediction can be trusted. Precision is key when incorrect positive predictions could significantly impact the analysis results, especially if the model is used to make decisions affecting the student experience. \[Precision=\frac{TP}{(TP+FP)} \tag{4}\]  
where:  
* TP represents the number of positive reviews correctly identified as positive by the model. Indicates the model's accuracy in recognizing genuinely positive sentiments within the dataset, providing valuable insights into aspects that receive positive student feedback. * FP refers to the number of reviews the model predicts as positive but with negative or neutral sentiments. These are instances where the model incorrectly classifies a review as positive, which could lead to overestimating satisfaction or approval in certain \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & \
\begin{tabular}{c} **Aspect** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Sentiment** \\ **F1-Score** \\ \end{tabular} & **Training Time (per Epoch)** \\ \hline Baseline & 0.85 & 0.82 & 12 min \\ \hline Fine-Tuned & 0.97 & 0.95 & 18 min \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Baseline comparison model. areas. Managing false positives is essential to ensure that the model's results accurately reflect student sentiment and do not misrepresent areas that need improvement. The recall is crucial to ensure the model does not miss significant positive reviews, especially those related to service aspects that directly impact students' experiences. The formula for calculating recall can be expressed in Equation (5) as follows:  
\[Recall=\frac{TP}{(TP+FN)} \tag{5}\]  
This formula measures the model's ability to capture all relevant positive instances in the dataset, ensuring that critical positive reviews, particularly those that may influence decision-making, are not overlooked. In the ABSA context, recall indicates how well the model can detect all reviews with positive sentiment towards a specific aspect, such as services. F1-score provides a solution by combining two essential metrics, precision and recall, to give a more balanced picture of model performance. F1-score is the harmonic mean of precision and recall [24], which is very useful when balancing the two. When the model must avoid too many false positives and negatives, the F1-score becomes a more appropriate metric than accuracy alone.