# Sentiment Analysis of Online New Energy Vehicle Reviews
## 3 The Proposed Model EDC
### Deep CNN Model  
CNN has significant advantages in sentiment analyses of text comments about new energy vehicles, especially when dealing with short texts and limited information. This is because CNN does not require maintaining state information in the feature sequence and can directly perform convolution operations on the text, resulting in more accurate feature representation. Additionally, in the domain of new energy vehicles, comments often involve a large number of specialized vocabularies, requiring domain-specific knowledge to understand the meanings of these terms. CNNs can leverage convolution operations to extract these key words from the text, further improving the accuracy of feature representation and enhancing the overall accuracy. The proposed deep CNN module consists of a two-dimensional convolutional neural network (CNN), which includes convolutional layers (CONV), pooling layers (POOL), fully connected layers, and channel attention layers [38]. The convolutional layer analyzes the data by sliding windows and alters the data dimensions. The pooling layer is primarily used to select the optimal features, reducing the computation and improving the model's generalization ability. The fully connected layer transforms the pooled two-dimensional features into one-dimensional feature vectors, and through iterative training, it performs classification from the input to output layers. The model also incorporates a channel attention mechanism, enabling the model to more flexibly model and adjust features, thereby enhancing the model's expressive power and performance. The deep CNN architecture transforms discrete text into continuous representations through alternating convolutional and downsampling layers, minimizing the data and computational requirements at each layer. Furthermore, the deep CNN utilizes four parallel convolutional layers and performs max pooling after each convolution to improve word-level embeddings and compress the sequence length. This approach enables the model to capture the relevance of comments and identify global semantic information in sentiment analyses of new energy vehicle comments. The design of the deep CNN structure is illustrated in Figure 5.  
As the network deepens, its performance may degrade, resulting in lower training accuracy and the possibility of gradient vanishing. Research has shown that residual connections can effectively mitigate these issues. Figure 5: Schematic diagram of deep CNN architecture. In the deep CNN module, a residual network (ResNet) is employed to define the network structure within the _block method. The _block method constructs a deep Convolutional Neural Network by stacking multiple convolutional layers and incorporating residual connections. Specifically, the residual connection in the _block method is achieved by adding the input x to the output after the convolution operation. This residual connection enables more efficient gradient propagation, alleviating the problem of gradient vanishing in deep networks and enhancing the model's training performance and representational capacity. Furthermore, considering the incomplete contextual information and ambiguous sentiment tendencies often present in new energy vehicle reviews, which can lead to inaccurate analysis results, we introduce a text processing channel attention mechanism in the deep CNN module. The attention mechanism is generally viewed as a weight allocation mechanism that shows the available processing resources are biased towards the informative portions of the input data. In recent years, researchers have successfully applied attention mechanisms in various deep learning networks, achieving significant breakthroughs in domains such as image classification and natural language processing. Specifically, in our model, the channel attention module takes the feature map x generated by the convolutional layers as the input, with the dimensions [batch_size, num_filters, seq_len, 1].