# Sentiment Analysis of Online New Energy Vehicle Reviews
## 3 The Proposed Model EDC
### EDC Model  
The characteristics of sentiment analysis on text data for new energy vehicles include incomplete context, concise text length, and the presence of specialized vocabulary. To better capture the features of text regions, a region embedding layer can be utilized. This approach learns semantic information from different regions of automotive text, such as car model, price, brand, appearance, etc., thereby enhancing the accuracy of the sentiment analysis. To reduce the computational complexity, a fixed number of feature maps can be employed for downsampling. Given that text features often lack a clear hierarchical structure, this method satisfies the \"semantic substitution\" property, enabling the representation of high-dimensional semantics with lower dimensions, thus improving the model efficiency while preserving crucial semantic information. For text sentiment analysis, equal-length convolution [39] can be employed to gather contextual information for each word. This technique compresses the contextual information of each word by considering its neighboring words, thereby enriching the semantic understanding of individual words. Additionally, a channel attention mechanism can be applied to the feature matrix generated by the convolutional layers. By utilizing channel attention, the network can automatically learn the importance of different channels, thereby enhancing the model's ability to capture and model input data. Moreover, residual connections can address issues related to gradient vanishing and exploding, leading to improved training speed and accuracy. Residual connections allow gradients to bypass the impact of convolutional layer weights and flow directly through each block and the output layer without loss, resulting in better model training and improved accuracy in the sentiment analysis. The channel attention mechanism can be applied to the feature maps generated by the convolutional layers. In this study, the dataset used is a sentiment dataset of new energy vehicle reviews, characterized by high noise, limited dimensional information, and unclear topics. The ERNIE pretrained model utilizes a large volume of Chinese data for unsupervised learning [40,41], thereby acquiring high-quality feature word vectors. Consequently, replacing the original word vectors with those generated by ERNIE can enhance the performance of the deep CNN model on the automotive review sentiment dataset, improve the model's ability to capture text semantics, and mitigate collinearity issues associated with similar words. The EDC model is a neural network used to identify and classify events in text data. To accomplish this task, the model utilizes the ERNIE algorithm, which generates unique word vectors for each word present in the input text. At the input layer, the EDC model feeds the input text through ERNIE to generate word vectors for each word in the text. These word vectors are then concatenated to form a matrix X, which serves as the input to the neural network. \[X_{1:n}=x_{1}\oplus x_{2}\oplus\ldots\oplus x_{n} \tag{12}\]  
The symbol \(\oplus\) represents the operator used for concatenating word vectors. For a given sentence, Xi represents the word vector for the \(i\)-th word. If we have a sequence of word vectors from \(Xi\) to \(X_{i+j}\), we can use the symbol \(X_{i:i+j}\) to denote this sequence. Therefore, \(X_{i:i+j}\) refers to the collection of word vectors that includes \(X_{i}\), \(X_{i+1}\), \(X_{i+2}\), \(\ldots\), \(X_{i+j}\). The convolution process involves using a convolutional kernel W of size h to generate a feature \(C_{i}\) through the operation of equal-length convolution. The feature Ci is obtained by convolving the kernel \(W\) with the word vector sequence \(X_{i:i+j}\), and it can be represented using the following formula:  
\[C_{i}=f(W\cdot X_{i:i+h-1}+b) \tag{13}\]  
To further process the features \(C_{i}\) generated from the convolution operation, a bias term b is added, and a nonlinear activation function f is applied.