## 3 Method
### SCS  
Unlike previous dichotomous sentiment analysis tasks, we optimize the output of the sentiment score ratings to fit the scores, ranging from 0 to 5, to fit the corresponding score ratings. Our proposed recommendation system defines \(U\) as the set of users and \(I\) as the set of items, with \(U\) having \(m\) elements and \(I\) having \(n\) elements. There are \(m\) users and \(n\) items in the user-item matrix \(R\in\mathbb{R}^{n\times m}\). \(r_{ui}\) denotes the assessment of item \(i\) by user \(u\), while \(V\) stands for the complete review text. The set of vectors \(V_{s}\) is the output from the SCS when \(V\) is the input. Our proposal includes four hidden layers within the SCS model. The first of these is the embedding layer, which seeks to translate user comments \(V\) into a dense array of  
Figure 1: SCTD framework. feature vectors. The comments of user \(u,v_{u}\in V\) are arranged into a matrix \(R_{s}\in\mathbb{R}^{l\times u_{s}}\). Each embedding layer is represented as:  
\[S^{1}:V\to R_{s}\to R^{l\times q_{s}\times k} \tag{1}\]  
In this context, \(q_{s}\) denotes the count of items evaluated by user \(u\), \(l\) signifies the review's length, and \(k\) indicates the latent dimension of the embedded word. Each word in a review is transformed into a \(K\)-dimensional vector, and the underlying depiction of user \(u\)'s reviews is as follows:  
\[LD_{u}=d_{1}\odot d_{1}\odot d_{2}\odot...\odot d_{q_{s}} \tag{2}\]  
where \(d_{i}\) is an \(I\)-dimensional vector and the term of the inner word is a \(K\)-dimensional potential vector. \(d_{i}\) represents \(u\)'s review of \(i\). \(\odot\) represents the concatenation operator that combines these vectors into a matrix \(LD_{u}\) with dimensions \(q_{s}\times l\times k\) for description. We then input \(LD_{u}\) into the second layer, the convolution layer. Attention theory helps us to adjust the weights for each word and each review in the review description matrix differently. \(g\) represents the \(K\)-dimensional embedding of each word. \(W_{g}\) and \(W^{d}\) represent the attention vector for each word in the review and the attention vector for each review in the review matrix. First, we denote each word in the review by \(g\) and embed an attention factor \(W_{g}\) into \(g\):  
\[W_{g}d_{i}=w_{g1}g_{1}\odot w_{g2}g_{2}\odot w_{g3}g_{3}\cdots\odot w_{g1}g_{1},W_{g}=\{w_{g}|\sum_{i=1}^{I}w_{gi}=1\} \tag{3}\]  
where \(w_{g1}g_{1}\) represents the element-wise production of \(W_{g1}\) and \(g_{1}\). The other attention vector \(W_{d}\) focuses on each review in the review matrix:  
\[W^{d}D_{u}=w_{1}^{d}d_{1}\odot w_{2}^{d}d_{2}\odot w_{3}^{d}d_{3}\cdots\odot w _{q_{s}}^{d}d_{q_{s}},W^{d}=\left\{w^{d}\mid\sum_{i=1}^{q_{s}}w_{i}^{d}=1\right\} \tag{4}\]  
Subsequently, we feed the output vector from the previous step into the convolution layer to capture contextual features among words. This process can be described as  
\[S^{2}:C_{u}=S^{2}(W^{2}(W^{d}(W_{g}d))+b^{2}) \tag{5}\]  
where \(W^{2}\) and \(b^{2}\) are the weights of the filter function in the CNN. A nonlinear activation function is represented by \(S^{2}\). The third layer, known as the maximum pooling layer, captures the most crucial information from a variety of contextual features. The max pooling function can be expressed in this way:  
\[S^{3}:C_{u}^{m}=max(C_{u1},C_{u2},...,C_{upll}) \tag{6}\]  
The above vectors are resolved to the final layer, the fully connected layer:  
\[S^{4}:v_{s}=S^{4}(W^{4}C_{u}^{m}+b^{4}) \tag{7}\]  
The weights in the fully connected neural network are denoted by \(W^{4}\) and \(b^{4}\), and \(S^{4}\) can serve as either a nonlinear or linear activation function. Since our goal is to train the network using ratings instead of binary sentiments, we employ softmax as the output function. The SCS process can be outlined as follows:  
\[v_{s}=SCS(V|\alpha) \tag{8}\]where \(\alpha\) consists of \((W^{g},W^{d},W^{2},W^{4},b^{2},b^{4})\). We use user ratings to train the CNN. The output of the SCS is designed into six classes.