## 4 Experiments, Numerical Results and Discussion
### Assessment of Our Proposal  
Finally, we objectively assess our proposal against related works. Table 11 provides a comparison between our proposal and some other related works within the same context (i.e., those dealing with depression and/or anxiety disorders based on Twitter data), according to the following criteria:  
1. **Mental disorder**: this refers to the mental disorder studied, which can be either depression (denoted as Dep) or anxiety (denoted as Anx) disorders. 2. **Data collection**: this refers to whether the training data were collected using keywords (e.g., symptoms, usernames, etc.) or reused from other datasets. 3. **Dataset size**: this refers to the total number of tweets used to train the models. 4. **Type of learning model**: this refers to whether the well-performing classifier adopts simple variants (denoted as S) or hybridization (denoted as H) of models. 5. **Type of classification**: this refers to whether the well-performing classifier is a binary (denoted as B) or a multi-class (denoted as M) model. 6. **Accuracy achieved**: this refers to the accuracy achieved by the well-performing classifier (measured as a percentage). Figure 5: _Cont._  
learning rate determined by grid search = 0.0006  
Figure 5: _Cont._  
learning rate determined by grid search = 0.0007  
Figure 5: _Cont._  
In view of the foregoing, the main potential advantage of our study is that it can be viewed as a complementary work to existing research focused on the detection of depression and anxiety disorders, as  
1. In contrast to many related works that rely on binary classification, our approach is based on multi-class models;
2. Our study showed that multi-classification may be more efficient than binary class models as it could better resolve ambiguities issues, although this cannot be generalized;
3. The data were collected based on assumptions involving both common and non-common symptoms between depression and anxiety disorders. Our approach also shows some drawbacks which are discussed in the following while trying to propose solutions. It should be noted that these limitations do not only concern our approach but much research working within the same context. 1. Although the data were generated according to a well-defined process, we still lack for more efficient methods for collecting data and labelling them (tweets). This still remains a big challenge for large volumes of data, in contrast to small volumes of data that can be processed and annotated within a reasonable time. As an ongoing work, we are currently studying the use of semantics to help collect and label the data through _ontology-computing_ while considering emoji, emoticons and related contents. 2. In fact, many researchers have embarked on a frantic race to design/improve classification models for the detection of mental disorders through the Twitter platform. Undoubtedly, this is very important, but it should not be an end in itself because what is more important is to leverage these models in order to perform useful tasks. In this line of thinking, we are currently working to deploy our models within a syndromic surveillance system, in order to improve public health systems. At this level, our  
Figure 5: Comparison between confusion matrix of the hybrid model; (**a**) CNN_RNN; (**b**) CNN_LSTM; (**c**) CNN_GRU; (**d**) CNN_BiRNN; (**e**) CNN_BiLSTM; (**f**) CNN_BiGRU. classification models are only used to classify the tweets as potentially positive toward depression and anxiety mental disorders or not. If so, the concerned users will be taken into account to study and monitor their behaviors on social media platforms through the syndromic surveillance system that further processes user data (tweets) in order to make some decisions and thus to perform the required actions. Indeed, it is far from easy to decide whether a given user is affected by a mental disorder by analyzing only one or a few tweets.