## 3 Design of HCOA-SACDC Model
### SAE-Based Classification  
The SAE model is utilized for the recognition and categorization of sarcasm [19; 20]. Normally, SAE is a type of unsupervised deep learning (DL) method that is organized by a dissimilar autoencoder (AE). The AE comprises a decoder and an encoder. Initially, the encoder layer is useful to translate the input \(x\) to a hidden illustration \(h\), viz., defined by \(h=f(wx+b)\), where \(f\), \(w\), and \(b\) define the activation function, the weighting matrices, and the bias of the existing encoder layer, respectively. Next, the decoding layer is useful to reconstruct \(x\) from \(h\), viz., represented by \(x^{\prime}=g(w^{\prime}h+b^{\prime})\), where \(x^{\prime}\), \(g\), \(w^{\prime}\), and \(b^{\prime}\) signify the weighting matrix, the simulation outcome, the bias of the existing decoder layer, and the activation function, respectively. Moreover, the wide-ranging training technique of the AE comprises a pretraining stage and a finetuning stage [17]. Firstly, AE minimizes the cost function as follows:  
\[L(x)=\frac{1}{2m}\sum_{i=1}^{m}\ \|x_{i}-x_{i}^{{}^{\prime}}\|^{2} \tag{1}\]  
where \(x_{i}\) describes the AE input, which characterizes the \(i\)th samples, and m denotes the sample count. \(x_{i}^{{}^{\prime}}\) indicates the AE outcomes, and the regeneration of the \(i\)th sample and \(m\) determines the quantity of samples. SAE can be recognized by adding hidden states.  
Consequently, the implementation of a hidden encoder of an existing AE is assumed as the input of the upcoming AE. When the pretraining layer of SAE is completed, the decoding AE is released. Then, the encoder weight of the SAE is interconnected and finetuned using softmax regression. Figure 2 depicts the framework of SAE.  
Figure 1: The overall process of HCOA-SACDC technique.  
Figure 2: Framework of SAE.  
Furthermore, a restricted term is involved in the loss function of the AE. The prolonged loss function is as follows:  
\[L(x)=\frac{1}{2m}\sum_{i=1}^{m}\|x_{i}-x_{i}^{{}^{\prime}}\|^{2}+\lambda\|w\|_{1} \tag{2}\]  
where \(w\) represents the weight matrix, and \(\lambda\) is the balance factor. When the quantity of hidden states is high in comparison with the input layer from the input neuron, it usually increases Kullback-Leibler (KL) divergence to the loss function of the AE. Consequently, the adopted loss function is as follows:  
\[L(x)=\frac{1}{2m}\sum_{i=1}^{m}\ \|x_{i}-x_{i}^{{}^{\prime}}\|^{2}+\lambda\sum_{j= 1}^{H}\ KL\big{(}\rho\big{|}\big{|}\hat{\rho}_{j}\big{)} \tag{3}\]  
where \(H\) indicates the quantity of concealed states, and KL shows the KL discrepancy. \(\rho\) describes the sparsity variable quantity, and \(\hat{\rho}_{j}\) defines the activation of the input instance of the \(j\)th concealed neuron. Obvious guiding normalization, applicable to some functions, includes the cross-entropy loss function, which is suggested to be included in the loss function of the AE. Thus, the adopted loss function can be expressed as follows:  
\[L(x)=\frac{1}{2m}\sum_{i=1}^{m}\ \|x_{i}-x_{i}^{{}^{\prime}}\|^{2}+\frac{ \lambda}{m}\sum_{i=1}^{m}\ \sum_{j=1}^{c}\ label(i,j)\cdot log\ pred(i,j) \tag{4}\]  
where \(C\) indicates the quantity of classes. \(label(i,j)\) represents the correct possibility from the \(j\)th class of the \(i\)th sample. \(log\ pred(i,j)\) represents the surveyed probability from the \(j\)th class of the \(i\)th sample. At that moment, a dissimilarity of SAE is applied in the newly designed technique. Now, the weight and bias values of the SAE are carefully chosen using the suggested technique.