## 4 Discussion
### Feature Analysis of the Proposed Dual-Channel Structure  
In this paper, a novel dual-channel structure, BiLSTM-Dilated Convolution combined with CNN, is designed for text sentiment analysis. It was found that when the BiLSTM module is connected to CNN through a single channel, it fails to understand deep semantic correlations and faces difficulties in simultaneously capturing local and global information. Consequently, the accuracy decreases compared to using only CNN, as shown in Table 6, where both the serial and parallel BiLSTM-CNN methods have lower accuracy than DCCNN.  
The DCCNN model, in the aspect of text sentiment analysis, computes faster than current mainstream text sentiment classification methods and can achieve high accuracy in a very short number of iterations. This ability to quickly capture features makes it more effective in extracting and modeling local features. However, for global models, it may overlook longer dependency relationships. On the other hand, algorithms like BiLSTM-CNN serial and BiLSTM-CNN parallel can learn deeper contextual information in text sentiment analysis, enabling simultaneous modeling of local features and global context information, resulting in higher precision values. However, these methods struggle with feature fusion and suffer from reduced accuracy due to repeated features.  
The proposed dual-channel approach effectively addresses these issues. In this study, a new dual-channel connection method is designed to connect the BiLSTM module separately with CNN and Dilated Convolution. This involves setting up channels for word vectors and character vectors, combining word embedding channels with dilated convolutions, and character embedding channels with convolutions. The word embedding channel captures word-level global information and semantic associations using dilated convolutions, capturing a broader range of contextual information. Simultaneously, the character embedding channel utilizes convolutions to capture local information and semantic correlations between adjacent characters, learning features from different window sizes. The combination of features from both channels provides a richer representation, effectively addressing the problem of understanding deep semantic correlations.  
Experimental verification shows that when the convolutional kernel sizes are set to three, five, and seven with a quantity of 128, the Sac-BiLSTM algorithm achieves the best performance.  
In conclusion, the proposed Sac-BiLSTM algorithm accurately extracts features from Chinese product review corpus for sentiment analysis. This algorithm utilizes fine-grained  
Figure 8: Accuracy Comparison of Different Algorithms on the Test Set of the Online Shopping Review Dataset at Different Iterations.  
character vectors to assist in extracting semantic information from word vectors, constructs a non-static dual-channel, and combines long short-term memory models with convolutional neural networks and dilated convolutions to avoid semantic extraction biases. It effectively extracts feature information from text, thereby improving the accuracy of sentiment analysis. Additionally, the Sac-BiLSTM algorithm proposed in this paper incorporates the Self-Attention mechanism to better capture key information in the text, enhancing the model's understanding of text relationships and contextual information, further improving the accuracy of sentiment analysis. Experimental results demonstrate the higher precision of the proposed Sac-BiLSTM algorithm.