## 4 Experimental Design and Result Analysis
### Results and Discussion  
To evaluate the effectiveness of the EBLA model for sentiment analysis of e-commerce product reviews, we conducted two control experiments. The first group compares the sentiment analysis performance of models using different word embeddings, and the second group compares the sentiment analysis performance of different deep learning models. (1) Comparative Experiment for Different Word Embedding Models  
To verify that the ERNIE word embedding model has better semantic representation ability, we compared its performance with that of Word2 Vec and BERT word embedding models. ERNIE used the ERNIE1.0 Chinese pre-training model published by Baidu with the word vector dimension of 768. BERT used the BERT-base network structure launched by Google with the word vector dimension of 768. Word2 Vec used the Chinese word vector \"Chinese Word Vector\" constructed by [PERSON] [24] with the word vector dimension of 300. In this experiment, we divided the word embedding model into two combined deep learning models: TextCNN and BiLSTM-Att. The experimental results are shown in Table 4.  
The experimental results show that compared with the traditional static word embedding model Word2 Vec, dynamic word embedding models, such as BERT and ERNIE, can dynamically adjust the different semantic representations of a word using the text context information, thereby effectively solving the polysemy problem of the static word vector. (2) Comparative Experiment of Different Sentiment Analysis Model  
To verify the effectiveness of this model in sentiment analysis of e-commerce product reviews, the sentiment classification performance of different models was compared in the same experimental environment. ERNIE adopted the ERNIE1.0 Chinese pre-training model released by Baidu, and other traditional deep learning models adopted Word2 Vec constructs word vectors. Table 5 shows the experimental results of the experiment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R** & **\(P\)** & **\(F1\)** \\ \hline Word2 Vec-CNN & 83.21 & 83.59 & 83.33 \\ BERT-CNN & 85.59 & 85.50 & 85.50 \\ ERNIE-CNN & 86.37 & 86.77 & 86.36 \\ Word2 Vec-BiLSTM-Att & 81.51 & 82.05 & 81.58 \\ BERT-BiLSTM-Att & 84.80 & 85.14 & 84.77 \\ EBLA & 87.87 & 87.55 & 87.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative results using different word embedding models %. BiLSTM: Bidirectional long short-term memory network, the forward hidden layer vector, and the backward hidden layer vector sum were obtained using the forward LSTM and backward LSTM modules, respectively, then both were spliced to obtain the hidden layer vector. Finally, the output is connected to the _softmax_ function for sentiment classification. TextCNN: This is a convolutional neural network for text classification, including convolutional and maximum pooling layers, and finally, the output is connected to a _softmax_ function for sentiment classification. RCNN: Here, the convolution layer in the general convolutional neural network is replaced with a BiLSTM network, and finally, the maximum pooling layer is taken and the output is connected to the _softmax_ function for sentiment classification. BiLSTM-Att: The attention mechanism is introduced using the BiLSTM network to obtain features highly relevant to the training task, and finally, the output is connected to an external _softmax_ function for sentiment classification. ERNIE: The ERNIE pre-training model adds [CLS] symbols and uses the corresponding output as the semantic representation of the text. Finally, the output is connected to a _softmax_ function for sentiment classification. ERNIE-BiLSTM: After the input text is vectorized and represented by the word embedding model ERNIE, it is input to the BiLSTM model to extract features, and finally, the output is externally connected to the _softmax_ function for sentiment classification.