## 5 Discussion  
In Section 1, we outlined three key research questions that guided our work. The primary focus of the first question (**RQ1**) was to investigate the effectiveness of traditional machine learning methods in achieving robust performance and generalizability across multiple datasets for depression severity detection. The prior research has showcased the efficacy of more complex models, such as deep neural networks or transformer-based architectures. However, these often demand significant computational resources, suffer from prolonged inference times, and lack interpretability [74]. Our approach, in contrast, strikes a compelling balance between performance and computational demands since it is based on simple features. We have evaluated two public English datasets and observed favorable performance metrics on both, outperforming more complex models such as BERT or MentalBERT. In addition, the comparison with the previous works shows that the proposed method remains competitive with the existing state-of-the-art methods. To our knowledge, this study is the first to propose a traditional machine-learning-based method that achieves commendable results comparable to those obtained by novel approaches. Furthermore, our approach demonstrates promising results and offers a more practical and interpretable alternative to the computationally intensive methods commonly used in this domain. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{DsD} & [PERSON] et al. [20] & 0.7374 & 0.7323 & 0.7316 \\  & \(M_{E}\) & **0.7512** & **0.7388** & **0.7438** \\ \hline \multirow{2}{*}{DepSign} & [PERSON] et al. [32] & **0.663** & **0.621** & **0.637** \\  & \(M_{E}\) & 0.5870 & 0.6138 & 0.5867 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best performances achieved with the \(M_{E}\) model and comparisons of these results with the best-performing approaches found in the literature. Best results are indicated by bold numbers. The second research question (**RQ2**) revolved around comparing various types of lexicon-based features in terms of their performance in depression detection. To address this, we introduced a feature framework leveraging multiple established lexicons to identify and categorize features into four distinct sets: affective, topic, social, and syntactic. The outcomes of our investigation indicate that affective features exhibit superior performance in text classification for depression detection. Nevertheless, including social, syntactic, and topic-related features enhances the performance. Finally, in our final research question (**RQ3**), we delved into determining the most effective method for detecting the severity of depression signs. To this end, we presented and evaluated three distinct classification models: a lexicon-based features model, a distributional representation model, and an ensemble model that combines lexicon-based features with distributional representations. The evaluation in terms of the F1-score revealed that the ensemble approach consistently outperforms the other models in both datasets, exhibiting remarkable performance. The enhancement in performance is more significant in the DsD dataset. In this dataset, the F1-score achieved by the \(M_{E}\) model represents an increase of 2 percentage points compared to \(M_{DR}\) and nearly 3 points compared to \(M_{LF}\). This 3-point improvement between the \(M_{E}\) model and the \(M_{LF}\) model is also revealed in the DepSign dataset. However, the enhancement achieved by \(M_{E}\) compared to \(M_{DR}\) is less pronounced. The Friedman test confirmed the statistical significance of these results. Furthermore, our analysis expanded to explore the impact of different word embedding techniques on depression detection performance, including a traditional context-independent technique such as FastText and more novel context-aware solutions based on transformers (all-distilroberta-v1 and all-mpnet-base-v2).