## 3 Methodology of Research
### Cnn-Lstm  
Suppose \(X_{i}\in R^{k}\) represents the K-dimensional vector which is equal to the \(ith\) token in a user review with total size or length of \(n\), which is denoted as a string of its word vectors, mathematically shown in Equation (1). If the length of the sentence is less than \(n\), then zero padding is applied. \[X_{1:\;n}=X_{1}+X_{2}+X_{3}.\ldots\ldots\ldots\ldots+X_{n} \tag{1}\]  
The \(+\) operator denotes a concatenation operation in Equation (1). Similarly, suppose the concatenation of the words \(X_{i}\), \(X_{i+1}\), \(X_{i+2}\), \(\ldots\ldots\ldots\ldots X_{i+j}\) is equivalent to \(X_{i:\;i+j}\). Let W\(\in R^{lk}\) denote the convolutional filters, applied in an \(nX\)-dimensional matrix of a sentence with a window or gap of h words, to produce a new feature matrix. The basic element \(X_{i:\;i+j}\) denotes the local feature matrix from the \(ith\) to the \((I+J)th\) line of the present sentence vector. Equation (2) can be used to produce a feature \(C_{if}\) from a window of words \(X_{i:\;i+h-1}\). \[C_{i}=\;f(W\cdot X_{i:\;i+h-1}+b) \tag{2}\]  
where \(b\) denotes bias and belongs to the set of real numbers, and \(f\) is an activation function such as hyperbolic tangent and sigmoid. The convolutional filter is convoluted on each window of words to generate a feature map by applying Equation (3). \[C=[C_{1},C_{2},C_{3},C_{n-h+1}] \tag{3}\]  
where \(C\) belongs to \(R^{n-h+1}\). The above is the mathematical technique for constructing a single feature map from a single convolutional filter. A convolutional layer with multiple \(m\) filters will produce \(m(n-h+1)\) features in the same way. Because feature selection can disrupt long-term dependencies early in the LSTM layers, the max pooling layer is not employed on features maps. The features are directly transferred into the LSTM layer before the fully linked layer, to capture long-term dependencies. LSTM can capture long-term relationships in sentences of unknown length and can be effectively utilized to govern information by avoiding a vanishing gradient. The basic architecture of the LSTM model is depicted in Figure 4. In an LSTM, the memory cell is utilized to save the selected data for longer without decaying. To execute the current input data, LSTM used recursive execution of the present memory cell using the current input \(X_{t}\) and the previous hidden state \(h_{t-1}\), where \(t\) denotes the present time and \(t-1\) denotes the previous time. Additionally, LSTM has an input gate \(i_{t}\), output gates \(O_{t}\), and forget gates \(f_{t}\), where \(C_{t}^{-}\) represents the present memory cell at time \(t\). The following mathematical equations explain the operational structure of LSTM. Equations (4) and (5) are used to calculate the values of the input gate and current memory cell states at time \(t\). \[i_{t}=\sigma(W_{i}\cdot X_{t}+U_{i}\cdot h_{t-1}+b_{i}) \tag{4}\]  
\[C^{-}=\;\tanh(W_{c}\cdot X_{t}+U_{c}\cdot h_{t-1}+b_{c}) \tag{5}\]  
The value of the forget gate at time \(t\) is calculated using Equation (6). \[f_{t}=\;\sigma(W_{f}\cdot X_{t}+U_{f}\cdot h_{(t-1)}+b_{f}) \tag{6}\]Similarly, Equation (7) is used to calculate the value of the new state of the memory cell at time \(t\). \[C_{t}=i_{t}\cdot C^{-}+f_{t}+C_{t-} \tag{7}\]  
The values of the output gate are calculated using Equations (8) and (9). \[O_{t}=\ \sigma(W_{o}\cdot X_{t}+U_{o}\cdot h_{t}(t-1)+b_{o}+V_{o}\cdot C_{t}) \tag{8}\]  
\[h_{t}=O_{t}\cdot\tanh C_{t} \tag{9}\]  
The input of the memory cell is denoted by \(X_{t}\) at time \(t\), where \(W_{f}\),\(W_{i}\), \(W_{c}\), \(W_{o}\), \(V_{o}\), \(U_{o}\), \(U_{i}\), \(U_{c}\), and \(U_{f}\) all are weight matrices, \(\sigma\) represents a sigmoid function and \(b_{c}\), \(b_{f}\), \(b_{i}\), and \(b_{o}\) are bias vectors. Throughout training, our supposed model learns the values of \(U_{i}\) and \(W_{i}\). The values of the forget gate, input gate, and output gate are in the range [0,1].