## 4 Experiments, Numerical Results and Discussion
### Evaluation and Analysis of the Well-Performing Models
By evaluating _Dataset2_ (anxiety dataset), the accuracy is about 86.35% which means that many anxious tweets were classified as non-depressive. Likewise, by evaluating _Dataset1_ (depressive dataset) using _Model_3_, the accuracy is about 62.96%; this means that most of depressive tweets were classified as non-anxious. The conclusions we draw from these results can be summarized as follows:  
1. The source of the improved accuracy of the studied models comes from the way the data were collected by relying on both common and non-common symptoms instead of only using keywords related to common symptoms between depressive and anxiety disorders. 2. Our multi-class models seem to be more effective than the corresponding binary class models as they can resolve ambiguities. Indeed, as depressive and anxiety disorders present certain intersections, binary models most likely classify them as positive \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Training Dataset** & **Type of Classification** & **Prediction** & **Evaluation Dataset** & **Accuracy (\%)** \\ \hline \multirow{2}{*}{Model\_1} & \multirow{2}{*}{Train\_Dataset} & \multirow{2}{*}{Multi-class} & \multirow{2}{*}{Normal, Depressed and Anxiety} & Eval\_dataset & 92.75 \\ \cline{5-5}  & & & & Shen\_dataset & 62.96 \\ \hline \multirow{2}{*}{Model\_2} & \multirow{2}{*}{Dataset1} & \multirow{2}{*}{Binary-class} & \multirow{2}{*}{Normal and Depressed} & Dataset2 & 86.35 \\ \cline{5-5}  & & & & [PERSON]\_dataset & 95.34 \\ \hline \multirow{2}{*}{Model\_3} & \multirow{2}{*}{Dataset2} & \multirow{2}{*}{Binary-class} & \multirow{2}{*}{Normal and Anxiety} & Dataset1 & 69.97 \\ \cline{5-5}  & & & & [PERSON]\_dataset & 94.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The CNN-BiGRU classifiers to predict normal cases and, depression and anxiety disorders using different datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**N\({}^{\circ}\)**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Fixed Learning Rate**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**F1-Score**} \\  & & & & & **Class 0 (\%)** & & **Class 1 (\%)** & **Class 2 (\%)** \\ \hline
13 & CNN\_RNN\_gs & 0.0002 & 73.17 & 72.00 & 73.00 & 75.00 \\
14 & CNN\_LSTM\_gs & 0.0008 & 55.74 & 33.00 & 55.00 & 75.00 \\
15 & CNN\_GRU\_gs & 0.0006 & 88.24 & 89.00 & 84.00 & 90.00 \\
16 & CNN\_BiRNN\_gs & 0.0001 & 88.51 & 92.00 & 86.00 & 87.00 \\
17 & CNN\_BiLSTM\_gs & 0.0007 & 92.20 & 95.00 & 90.00 & 92.00 \\
18 & CNN\_BiGRU\_gs & 0.0006 & 92.75 & 96.00 & 91.00 & 92.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The evaluation of our models on the evaluation dataset (_Eval_dataset_), by using grid search optimizer to determine the learning rate value for Adam optimizer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**N\({}^{\circ}\)**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Predict Class 0**} & \multirow{2}{*}{**Predict Class 1**} & \multirow{2}{*}{**Predict Class 2**} & \multirow{2}{*}{**Correct**} & \multirow{2}{*}{**Convergence**} \\  & & & & & **(Tweets)** & & **(Tweets)** & **Prediction** & **Ratio (\%)** \\ \hline
15 & **CNN\_GRU\_gs** & **88.24** & **2241** & **4778** & **1261** & **6832** & **82.51** \\
16 & CNN\_BiRNN\_gs & 88.51 & 1740 & 2771 & 3769 & 4259 & 51.44 \\
17 & CNN\_BiLSTM\_gs & 92.20 & 1884 & 3630 & 2766 & 5410 & 65.34 \\
18 & CNN\_BiGRU\_gs & 92.75 & 1474 & 4002 & 2804 & 5213 & 62.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Prediction of tweets from _Shen_dataset_ using our well-performing models. tweets (i.e., either depressive or anxious tweets) regardless of the model used (see for instance the results of using Model_2). It should be noted that the conclusions drawn concern only the context of our work and can in no way be generalized.