## 4 Experimentation
### Results  
To begin with, we evaluate the \(M_{LF}\) model to explore which specific features lead to better performance. An initial experiment was conducted using all the features of each set. Features exhibiting a strong correlation (greater than 0.95) with other features were removed, and all values were scaled using Scikit-learn MinMaxScaler. The results of this experiment are shown in Table 2.  
We can see that the best performance for both datasets was achieved using the combination of all feature sets, resulting in an F1-score of 71.12% and 60.80% for the DsD and DepSign datasets. This indicates that incorporating diverse lexicon-based features allows the model to capture various aspects of depression-related linguistic patterns, leading to more informed and accurate depression detection. However, it is reasonable to consider that not all extracted features are equally valuable for depression detection and that some may even negatively impact the accuracy of the classification. A feature selection process was conducted to investigate and improve our model. Thus, we have used recursive feature elimination (RFE) with cross-validation to identify the most relevant features among all the feature sets. The results improved slightly using this technique, reaching a 71.89% and 61.29% F1-score for each dataset. A significant reduction in the dimensionality of the features accompanies this performance improvement. Of the 376 features in the proposed framework, only 61 and 114 were selected for the DsD and DepSign datasets, respectively. The selection of a more significant number of features in the DepSign dataset is consistent with the conclusions drawn from the information gain analysis described in Section 3.2. That analysis showed a more significant number of features with a high information gain in this dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**DsD**} & \multicolumn{3}{c}{**DepSign**} \\ \cline{3-8}
**Model** & **Feature Set** & **F1-Score** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** \\ \hline \multirow{6}{*}{\(M_{LF}\)} & Affective & 0.6873 & 0.6763 & 0.7053 & 0.5661 & 0.5648 & 0.5817 \\  & Social & 0.6673 & 0.6552 & 0.6842 & 0.5477 & 0.5544 & 0.5758 \\ \cline{1-1}  & Syntactic & 0.6765 &  
Upon examining the performance of different types of characteristics, we can observe that affective and syntactic features yield the best results (**RQ2**). On the contrary, social characteristics exhibited the lowest performance in both datasets. Further information on this matter is depicted in Figure 4. These figures illustrate the percentage of features that were selected from a specific category (Figure 4a) and the distribution of the feature sets among the specified features (Figure 4b) for each dataset. We can see that for both datasets, affective features are consistently among the top-selected categories. Figure 4a shows that approximately 22% to 24% of the total set are selected for each dataset, while Figure 4b shows that this set is the one with more representation in the DsD dataset and the second one in the DepSign dataset. This suggests that emotional expressions and sentiment-related content are crucial in detecting depression. Furthermore, the syntactic features are consistently represented in both datasets. In contrast, social characteristics are less represented among the selected features. It is worth mentioning, however, that this is the set with a smaller size. After obtaining the results for the \(M_{LF}\) model, our focus shifts to evaluating the distributional representation model (\(M_{DR}\)). To conduct this evaluation, we experimented using various word embedding techniques, including FastText and transformer-based embeddings. For the latter, we specifically used two different pre-trained models using the SentenceTransform-Python framework [68]: all-mpnet-base-v2 and all-distilroberta-v1 [71].