## 4 Results
To address the class imbalance, the classifier employed automatic class weighting. Figure 6: UMAP projection of clustered complaint embeddings. The figure shows a two-dimensional Uniform Manifold Approximation and Projection (UMAP) visualization of the BERT-based semantic embeddings of complaints. Each point represents a complaint, colored according to its assigned cluster. The spatial organization illustrates the degree of separation and semantic similarity between the identified complaint categories. To empirically justify the selection of the Random Forest classifier, we conducted a comparative performance evaluation against two widely used alternatives: support vector machine (SVM) and XGBoost. All models were trained on the same stratified dataset and evaluated using macro-averaged metrics across the four predefined complaint categories. As shown in Table 4, Random Forest achieved the highest scores in accuracy (0.83), precision (0.81), and F1-score (0.80), along with the best Matthews correlation coefficient (MCC) of 0.71. These results confirm that Random Forest provides a balanced and interpretable performance suitable for deployment in public sector environments. While XGBoost and SVM delivered competitive results, their slightly lower scores and increased computational complexity made them less optimal for the real-time, resource-constrained context of this application. Performance was evaluated using precision, recall, F1-score, AUC, and Matthews correlation coefficient (MCC). Table 5 summarizes the per-class results: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **MCC** \\ \hline Random Forest & 0.83 & 0.81 & 0.79 & 0.80 & 0.71 \\ \hline SVM & 0.78 & 0.76 & 0.74 & 0.74 & 0.64 \\ \hline XGBoost & 0.80 & 0.78 & 0.75 & 0.76 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance of three classifiers (Random Forest, SVM, XGBoost) for semantic complaint classification. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Cluster** & **Description** & **Examples** \\ \hline \multirow{4}{*}{Cluster 0—Startup Errors and Equipment Reassignment} & \multicolumn{1}{l}{Includes critical startup errors} & - \"Remove Dr. profile. Prepare the laptop for the new user...\" \\  & \multicolumn{1}{l}{(e.g., hardware failures with beeps} & - \"The computer is beeping and won’t start the OS.\" (2\(\times\)) \\ \hline \multirow{4}{*}{Cluster 1—Software Reinstallation and Slow Performance} & \multicolumn{1}{l}{Issues related to general system} & - \"Uninstall English Office and \\  & \multicolumn{1}{l}{slowness and requests for installation} & \multicolumn{1}{l}{install Portuguese.\"} \\ \cline{1-1}  & \multicolumn{1}{l}{or reinstallation of base software,} & - \"The PC is very slow.\" \\ \cline{1-1}  & \multicolumn{1}{l}{such as Office versions or the Windows operating system.} & - \"Install Windows XP and Office XP.\" \\ \hline \multirow{4}{*}{Cluster 2—Peripheral Failures and Error Indicators} & \multicolumn{1}{l}{Covers failures in peripherals like monitors} & - \"Monitor failure... request diagnosis.\" \\  & \multicolumn{1}{l}{and printers, as well as physical failure} & - \"PC with intermittent sound and red light.\" \\ \cline{1-1}  & \multicolumn{1}{l}{indicators (e.g., intermittent sounds, red lights) that require technical diagnosis.} & - \"Install multifunction printer.\" \\ \hline \multirow{4}{*}{Cluster 3—Equipment Replacement and Migration} & \multicolumn{1}{l}{Concerns logistical requests such as} & - \"We don’t have a computer for this worker.\" \\  & \multicolumn{1}{l}{assigning new computers, retrieving old equipment, and transferring data} & - \"Replace Dr. computer...\" \\ \cline{1-1}  & \multicolumn{1}{l}{between users, indicating inventory} & - \"Retrieve the old PC... need to copy \\ \cline{1-1}  & \multicolumn{1}{l}{management and continuity of work.} & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the four complaint clusters identified through semantic clustering.