## 3 Proposed Method
### Sentiment Classification  
The proposed spider Taylor-ChOA-based RMDL is adapted to classify the sentiments from reviews considering the feature vector. Here, the spider Taylor-ChOA-based RMDL is obtained by combining spider Taylor-ChOA in the RMDL model for selecting optimal weights contained in RMDL [25]. The proposed spider Taylor-ChOA is adapted to optimize RMDL by selecting the optimal weights. The structure of RMDL and the training process are given below. **Architecture of RMDL:** Here, the feature vector is adapted as an input of RMDL. RMDL [25] is a method that is used for any database to perform effective classification. The RMDL consists of various deep convolutional neural networks (CNN), deep neural networks (DNN), and deep recurrent neural networks (RNN). The count of nodes and layers of all deep learning multi-models are generated arbitrarily. Figure 2 displays the structure of the RMDL model. The RMDL comprises three fundamental models of deep learning, namely CNN, RNN, and DNN, in a parallel manner. Here, each model is defined individually. The RMDL consists of \(n\) DNN, \(o\) RNN, and \(p\) CNN models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cap** & **Emo** & **Hash** & **Elong** & **Positive** & **Negative** & **Punj** & **Numerical** & **No. of Sent.** \\ \hline  
& 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of feature extraction. **(i) DNN:** DNN is developed to learn various layer connections in which each layer receives a connection with a prior parameter and provides the connection to a consequent layer of the hidden part. The input indicates the association of features with the first hidden of all random models. The output layer determines various classes to perform multi-class classification and acquires one input for binary classification. In DNN, each learning unit is generated randomly, and it uses the standard backpropagation method with sigmoid, ReLU, and an activation function. Here, the activation function is expressed as  
\[f(g)=\frac{1}{1+e^{-g}}\in(0,1) \tag{9}\]  
\[f(g)=\max(0,g) \tag{10}\]  
Here, the sigmoid function is expressed as  
\[\sigma(h)_{i}=\frac{e^{h_{i}}}{\sum_{l=1}^{Y}e^{2l}} \tag{11}\]  
Figure 2: An illustration of the RMDL model. where \(h_{i}\) represents a data point, and \(t\) signifies a random number such that \(1\leq t\leq Y\). \[\forall i\in\{1\dots Y\} \tag{12}\]  
where \(Y\) refers to an arbitrary number. _(ii) RNN:_ Another neural model that contributes in RMDL is RNN, which assigns more weights before a data point's series. Hence, this technique is effective in classifying series data. In RNN, the neural network adapts the data of prior nodes for enhanced semantic analysis of the database and is expressed as  
\[g_{v}=F(g_{v-1},\ell_{v},\theta) \tag{13}\]  
where \(g_{v-1}\) signifies the state at time \(v-1\), \(\theta\) represents the mapping string of feature space, and \(\ell_{v}\) symbolizes the input at step \(v\). In general, the weights are used for modeling the above formula given as  
\[g_{v}=W_{rec}\sigma(g_{v-1})+W_{in}l_{v}+\eta \tag{14}\]  
where \(W_{rec}\) symbolizes the recurrent matrix weight, \(W_{in}\) expresses the input weight, \(\eta\) is the bias, and \(\sigma\) expresses the element-wise function. **LSTM:** LSTM is an exceptional type of RNN that preserves large dependencies in a more effective way in contrast to fundamental RNN. It is beneficial in overwhelming vanishing gradient problems. However, the LSTM represents a chain-like model that uses various gates to regulate the amount of data, which are permitted to each of the node state. Here, the input gate is given by  
\[H_{v}=\sigma(\omega_{H}\mid g_{v},R_{v-1})+\eta_{H} \tag{15}\]  
where \(\eta\) represents the bias vector, \(\omega\) symbolizes the weight matrices, \(g_{v}\) is the memory cell input at time \(v\), \(H\) signifies the input, and \(R\) refers to the hidden layer.