## 4 Discussion  
### Comparison and Analysis with Other Methods  
The proposed Sac-BiLSTM algorithm in this study achieves efficient text sentiment analysis, with the model rapidly converging within the first five iterations and maintaining a classification accuracy above 0.94. To validate the effectiveness and superiority of the proposed algorithm, it is compared with other mainstream text classification algorithms, including Static-CNN, Non-Static-CNN, S-Non-CNN (Multi-channel Convolutional Neural Network), CNN-character (Character-level Convolutional Neural Network), DCCNN (Dual-channel Convolutional Neural Network), BiLSTM-CNN Concatenation, and BiLSTM-CNN Parallel. In this section, we provide a brief introduction to different comparative models, all of which use the Word2 Vec model as their pre-trained model. 1. WordCNN-static: This model uses pre-trained word embeddings as the embedding layer. Multiple one-dimensional convolutions with different sizes are applied to the output of the embedding layer, followed by batch normalization, activation, and pooling. The outputs of the pooling layers are concatenated, flattened, and passed through a Dropout layer and a fully connected layer for classification. 2. CNN-character: This model uses pre-trained character embeddings as the embedding layer. Different-sized convolutional kernels are applied to the input, followed by Max pooling to reduce the dimensionality of each convolutional kernel's output, resulting in a set of feature vectors. The pooling results of all convolutional kernels are then concatenated and further processed by a fully connected layer for feature extraction and classification. 3. S-Non-CNN: This model defines two different word embedding layers that load pre-trained word embeddings. Next, different-sized convolutional kernels are applied to each channel independently, followed by non-linear transformations. The outputs of each convolutional kernel are then reduced in dimensionality through Max pooling, resulting in two sets of feature vectors. These feature vectors are flattened and concatenated to form a global feature vector. The global feature vector is further processed by a fully connected layer for feature extraction and classification. 4. DCCNN: This model defines two different word embedding layers that load different pre-trained word and character embeddings. Different-sized convolutional kernels are applied to the input, followed by non-linear transformations and dimensionality reduction, resulting in two sets of feature vectors. These feature vectors are flattened and concatenated to form a global feature vector. The global feature vector is further processed by a fully connected layer for feature extraction and classification. 5. BiLSTM-CNN Concatenation: This model loads pre-trained word embeddings and applies a BiLSTM layer for sequence modeling. Then, different-sized convolutional kernels are applied to the output of the BiLSTM layer, followed by non-linear transformations and dimensionality reduction, resulting in a set of feature vectors. These feature vectors are flattened and further processed by a fully connected layer for feature extraction and classification. 6. BiLSTM-CNN Parallel: This model uses two input channels. The model applies a BiLSTM layer for sequence modeling on inputA, resulting in a sequence output (outputA). Simultaneously, different-sized convolutional kernels (three, five, and seven) are applied to inputB, followed by non-linear transformations and dimensionality reduction, resulting in a set of feature vectors (outputB). These feature vectors are flattened and merged with outputA, resulting in a comprehensive feature vector. The comprehensive feature vector is further processed by a fully connected layer for feature extraction and classification. The text classification results of this algorithm compared to the other six algorithms on various test datasets are shown in Tables 6-8.