## Materials and Methods
### Specific Structure  
The Sac-BiLSTM algorithm proposed in this paper consists of six specific layers. (1) Embedding Layer: The data were loaded using the pandas library and stopwords were eliminated based on a predefined list. Tokenization was performed on the data, resulting in word-level and character-level representations. In the word-level tokenization, words associated with personal names, place names, organization names, and other proper nouns were excluded from the tokenization output. Lastly, the word2 vec.Word2 Vec function was utilized to create and train both word embedding and character embedding models.The pretrained word embeddings and character embeddings from Word2 Vec were used as the initial weights for the embedding layer. This allowed the embedding layer the loading of the pretrained embedding models and representation of the text in vector form. In the pretrained models, Word2 Vec was used to obtain word vector representations, while a character embedding model was used to obtain character vector representations. By using these pretrained word and character embeddings as the initial weights of the embedding layer, the network possesses some level of semantic understanding during the initial training phase. This approach improves the performance and generalization ability of the model while reducing training time and data requirements. The preprocessed dataset after this stage is shown in Figure 2 (where larger characters or words represent more frequent parts in the dataset). (2) BiLSTM layer. For the feature vectors in the training corpus, they are first encoded through the BiLSTM layer. In other words, for each word (or character) in the product reviews, the BiLSTM layer utilizes the current input and its previous hidden state to calculate the next hidden state. This enables each time step's output to incorporate not only the feature vector information of the current vocabulary but also the feature information of all preceding and succeeding vocabularies. Consequently, the feature vectors at each time step can contain more comprehensive information. The structure of the BiLSTM layer is illustrated in Figure 3.  
Figure 2: Product Reviews Word Cloud Display of Segmented Text. The vectors obtained from the forward LSTM ( \(h_{L0}\)) and the backward LSTM ( \(h_{R0}\)) are merged and input into the subsequent CNN layer. (3) Convolutional Layer. In a CNN, a convolutional kernel function can generate a feature sequence. By using convolutional operations with kernels of different sizes on input word and character vectors, more feature sequences can be generated to capture a more comprehensive context of textual information. Assuming \(h\) is the kernel size, \(b\) is the bias term, and \(W_{h}\) is the weight matrix for different granularities of convolutional kernels, \(W_{h}\in R^{(h\times k)}\). In this article, one-dimensional convolution operation with stride \(s=1\) is used, and weight matrix \(W_{h}\) is dot multiplied with the feature matrix composed of word vectors to obtain a sequence of features consisting of \(n-h+1\) outputs. We let the output results obtained by different convolution kernels be \(C_{(h,i)}\), that is, the \(i\) output of the convolution kernel with kernel size h is calculated by the following Formula (1):  
\[C_{h,i}=f(W_{h}X_{i;i+h-1}+b). \tag{1}\]  
In the word vector channel, the formula for using dilated convolution is as follows (2), where \(C_{h,i}\) is the receptive field size of this layer, \(k_{n}\) is the kernel size of this layer \(s_{i}\), and is the stride of the ith layer:  
\[C_{h,i}=C_{h-1,i}+(k_{n}-1)\prod_{n=1}^{n-1}s_{i}. \tag{2}\]  
As the convolution kernel moves, we obtain a lot of feature sequences \(C_{(h,i)}\). The final generated feature graph set is \(C\). The calculation formula is as follows (3):  
\[C=C_{(h,1)},C_{(h,2)},\cdots,C_{(h,n+h-1)}. \tag{3}\]  
To extract higher-level textual features, it is necessary to stack more convolutional layers.