## 4 Experimentation
### Results
This library exploits an approach using siamese and triplet networks to generate sentence embeddings for each sentence. According to the results obtained, shown in Table 3, the \(M_{DR}\) model performs slightly better than the \(M_{LF}\) model. Regarding comparing word embedding techniques, we can observe that transformer-based embeddings yield better performance. Specifically, the best results are obtained with all-mpnet-base-v2 embeddings in the DsD dataset and all-distilroberta-v1 in the DepSign dataset. As described in Section 2, this can be explained by the fact that transformer-based embeddings can capture intricate relationships between words, contextual information, and semantic meanings, resulting in more comprehensive and contextually rich representations of textual data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dataset**} & \multicolumn{3}{c}{**DsD**} & \multicolumn{3}{c}{**DepSign**} \\ \cline{3-8}
**Model** & **Embeddings** & **F** & **P** & **R** & **F** & **P** & **R** \\ \hline \multirow{3}{*}{\(M_{DR}\)} & FastText & 0.7041 & 0.6767 & 0.7079 & 0.6040 & 0.6034 & 0.6224 \\  & all-mpnet-base-v2 & **0.7238** & 0.7221 & **0.7256** & 0.6322 & 0.6289 & **0.6397** \\ \cline{1-1}  & all-distilroberta-v1 & 0.7211 & **0.7244** & 0.7230 & **0.6325** & **0.6399** & 0.6335 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best performances (F1-score, precision, and recall) attained by the \(M_{DR}\) model for each word embedding method and dataset. Best results are indicated by bold numbers. Figure 4: Feature set representation after running the Recursive feature elimination (RFE) method for each dataset. (**a**) shows the percentage of features that were selected from a certain category. (**b**) shows the distribution of features from each category between the selected features. After separately analyzing the results obtained from the distributional representation and the models based on lexicons, our focus shifted to examining their combination, represented by the model \(M_{E}\). To conduct this experiment, we selected the features that demonstrated the most promising performance for each dataset and integrated them with word embeddings. The better results obtained with context-aware embeddings motivated us to select it as a word embedding technique in the \(M_{E}\) model. Specifically, we selected all-distilroberta-v1, as it obtained a higher result than all-mpnet-base-v2 in the DepSign dataset, is very similar in DsD, and also has a smaller size, which reduces its computational expense. Thus, we constructed the \(M_{E}\) model combining the best-performing characteristics selected from \(M_{LF}\) with all-distilroberta-v1 embeddings. In addition, we aim to compare the results obtained with the methods proposed in this paper with those obtained using transformer-based models. For this purpose, we utilized two widely used pre-trained transformer models: BERT and MentalBERT. BERT is a powerful language representation model that has shown effectiveness in numerous NLP applications. MentalBERT is a specialized adaptation of BERT fine-tuned specifically for mental health-related tasks. It incorporates domain-specific knowledge and context, making it particularly suitable for detecting depression in the textual data. Table 4 presents the best results achieved with three different models: \(M_{LF}\), \(M_{DR}\), and \(M_{E}\), along with the results obtained with BERT and MentalBERT [20; 32]. The results show that the \(M_{E}\) model achieved the highest F1-score in both datasets: 73.35% for DsD and 63.81% for DepSign. These results suggest that combining linguistic cues and emotional expressions from lexicon-based features with contextual information and semantic relationships captured from word embeddings leads to improved performance and more informative depression detection models. Furthermore, such results affirm that the proposed approach for detecting depression severity signs in the textual data exhibits substantial performance across different datasets (**RQ1**).