## 4 Discussion
### Comparison and Analysis with Other Methods
By comparing the results of various methods in Tables 6-8, it can be observed that the proposed Sac-BiLSTM algorithm outperforms the other six methods in all performance metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Feature** & **Acc** & **Precision** & **Recall** & **F1** \\ \hline Static-CNN & Static & 0.9188 & 0.9007 & 0.9390 & 0.9195 \\ CNN-character & Static & 0.9209 & 0.9006 & 0.9442 & 0.9219 \\  & Single-channel & & & & \\ S-Non-CNN & Non-static & 0.9336 & 0.9449 & 0.9190 & 0.9318 \\  & Dual-channel & & & & \\ DCCNN & Dual-channel & 0.9372 & 0.9376 & 0.9352 & 0.9364 \\ BiLSTM-CNN & Non-static & & & & \\ BiLSTM-CNN & Non-static & & & & \\  & Dual-channel & & & \\ Sac-BiLSTM & Non-static & 0.9350 & 0.9413 & 0.9262 & 0.9337 \\  & Non-static & & & & \\  & Non-static & & & & \\  & Dual-channel & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Text classification results of different algorithms on the Online Shopping Review dataset test set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Feature** & **Acc** & **Precision** & **Recall** & **F1** \\ \hline Static-CNN & Static & 0.8376 & 0.8602 & 0.8432 & 0.8560 \\ CNN-character & Static & & & & \\ Single-channel & 0.8464 & 0.8457 &  
Analyzing the results of the comparative experiments, we can understand the impact of different feature fusion and algorithm combinations on model performance in sentiment analysis tasks. From the results in Tables 6-8, the following can be concluded. (1) Compared to Static-CNN, the Character-level Convolutional Neural Network (CNN-character) utilizes pre-trained character embeddings as input, validating that using character embeddings as the original features is superior to using word embeddings. (2) The multi-channel algorithm can improve the accuracy of the model to some extent. The S-Non-CNN multi-channel algorithm trains with both static and non-static word embeddings in two separate channels, resulting in higher Accuracy, Recall, and F1 score values compared to single-channel algorithms. (3) BiLSTM can improve the accuracy of the model. Adding a BiLSTM layer in the network architecture allows each time step's feature vector more information. Experimental results show that adding BiLSTM to the convolutional neural network can achieve higher accuracy. Comparing the effects of BiLSTM-CNN concatenation and BiLSTM-CNN parallelization in sentiment classification, it is found that adding BiLSTM in series with CNN performs better than adding them in parallel. Additionally, the dual-channel algorithm using character/word embeddings can better utilize the ability of BiLSTM to extract global feature vectors, resulting in better performance compared to that of algorithms that combine BiLSTM and CNN in series or in parallel. The proposed Sac-BiLSTM introduces a novel dual-channel architecture, where features are separately extracted from character and word vectors and then fused before classification, allowing the model the learning of semantic features of different sizes. Furthermore, the proposed method incorporates Self-Attention units into the dual-channel network structure, making it easier for the network to capture long-distance dependent features in sentences. The experimental results on the online_shopping_10_cats dataset demonstrate that the Sac-BiLSTM achieves an Accuracy of 0.9463, Recall of 0.9409, Precision of 0.9469, and F1-score of 0.9404, which are the highest among all the algorithms.In the food delivery dataset, the Accuracy is 0.8776, Precision is 0.86363, Recall is 0.8980, and the F1-score is 0.8804. In the Weibo comment dataset, the Accuracy is 0.9775, Precision is 0.9940, Recall is 0.9592, and the F1-score is 0.9763. These metrics indicate that the Sac-BiLSTM model performs the best among all the models. The experimental results show that the performance of the model is influenced by the size of the data, with better results observed with larger datasets. Moreover, the model demonstrates good robustness.