## 4 Experiments, Numerical Results and Discussion
### Evaluation and Analysis of the Well-Performing Models  
In this section, we evaluate our approach regarding the quality of the data collected and the models built. The objective is twofold: (1) verify the effectiveness of the assumptions used to collect data and (2) show the effectiveness of using multi-class models rather than binary class models. To this end, we leverage the dataset used in [36] to perform an evaluation using binary class models for depression and anxiety detection. Thus, we have randomly selected 12,982 tweets from Depression Dataset D1 and 2658 tweets from Non-Depression Dataset D2. After preprocessing these data, we obtained 5955 tweets labeled by '1' and 2325 tweets labeled by '0'; the resulting dataset is denoted as _Shen_dataset_. These data are then tested by considering the well-performing models discussed in Tables 7 and 8. The results are given on Table 9.  
Figure 4: _Cont._  
According to Table 9, one observes that the prediction accuracy of _Shen_dataset_ is average and thus does not show very good results. This is because many depressive tweets were classified as anxious tweets by our models. Indeed, as mentioned in Table 1, there are some common symptoms between depressive and anxiety disorders which consequently may lead to committing classification errors. By knowing that the tweets of _Shen_dataset_ were collected by using some keywords that overlap with anxiety disorders (e.g., \"I am depressed and **anxious**\", \"I am too **tired**\", \"I am so **sad**\" and \"I have depression **anxiety suicidal** thoughts\"), our models most likely classify them as anxiety tweets instead of depressive ones. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{N}^{c}\)} & \multirow{2}{*}{**Models**} & **Fixed** & \multirow{2}{*}{**Accuracy (\%)**} & **F1-Score** & **F1-Score** & **F1-Score** \\  & & & & **Class 0 (\%)** & **Class 1 (\%)** & **Class 2 (\%)** \\ \hline
1 & CNN\_RNN [33] & 0.0001 & 36.07 & 69.00 & 19.00 & 2.00 \\
2 & CNN\_LSTM [33] & 0.0001 & 72.76 & 62.00 & 67.00 & 88.00 \\
3 & CNN\_GRU [33] & 0.0001 & 80.17 & 85.00 & 79.00 & 77.00 \\
4 & CNN\_BiRNN [33] & 0.0001 & 87.27 & 92.00 & 84.00 & 86.00 \\
5 & CNN\_BiLSTM [33] & 0.0001 & 88.93 & 86.00 & 90.00 & 91.00 \\
6 & CNN\_BiGRU [33] & 0.0001 & 87.94 & 85.00 & 87.00 & 92.00 \\
7 & CNN\_RNN & 0.001 & 35.42 & 0.00 & 0.00 & 52.00 \\
8 & CNN\_LSTM & 0.001 & 57.02 & 49.00 & 56.00 & 64.00 \\
9 & CNN\_GRU & 0.001 & 78.22 & 77.00 & 73.00 & 83.00 \\
10 & CNN\_BiRNN & 0.001 & 89.65 & 93.00 & 87.00 & 89.00 \\
11 & CNN\_BiLSTM & 0.001 & 91.82 & 92.00 & 91.00 & 93.00 \\
12 & CNN\_BiGRU & 0.001 & 93.38 & 96.00 & 91.00 & 93.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The evaluation of our models on the evaluation dataset (_Eval_dataset_), based on fixed learning rate values for Adam optimizer. Figure 4: Comparison between training and test for accuracy and loss of hybrid models; (**a**) learning rate 0.001; (**b**) learning rate 0.0001. To check this issue, we have reused our dataset to build two binary class models for predicting depression and anxiety separately while keeping the same parameters values. These models are based on the hybridization of CNN and Bi-GRU. Hence, _Main-dataset_ was divided into two datasets denoted as _Dataset1_ and _Dataset2_. _Dataset1_ contains only normal and depressive tweets labeled, respectively, with '0' and '1' while _Dataset2_ contains only normal and anxiety tweets labeled, respectively, with '0' and '1'. Once these models are built, we test datasets _Eval_dataset_, _Shen_dataset_, _Dataset1_ and _Dataset2_ to make comparisons and thus draw conclusions. The results are given on Table 10.  
According to Table 10, both binary class models classify depressive tweets from _Shen_dataset_ as depressive and anxiety tweets with very high accuracy. Regarding our datasets, the obtained results are much better. For instance, _Model_2_ was trained to classify depressive tweets.