## 4 Experimental Design and Result Analysis
### Results and Discussion
ERNIE-RCNN: After the input text is vectorized and represented by the ERNIE word embedding model, it is input to the RCNN model to extract features, and finally, the output is externally connected to the _softmax_ function for sentiment classification. ERNIE-CNN: After the input text is vectorized and represented by the ERNIE word embedding model, it is input to the TextCNN model to extract features, and finally, the output is externally connected to the _softmax_ function for sentiment classification. EBLA: After the input text is vectorized and represented by the ERNIE word embedding model, it is input to the BiLSTM-Att model to extract features, and finally, the output is externally connected to the _softmax_ function for sentiment classification. From the experimental results, all the metrics of TextCNN are optimal for the neural network using traditional static word embedding. This is attributed to the TextCNN model having a simple structure, which only contains a convolution layer and a maximum pooling layer. Therefore, it has a good classification effect on the small corpus. The introduction of the maximum pooling layer of TextCNN reduces the risk of overfitting, reduces parameters, speeds up the calculation speed, and obtains better experimental results. The worst effect is the BiLSTM model, which can be explained by analyzing the principle of its model structure. Its structure contains two LSTM layers with several model parameters, and the corresponding amount of data required is relatively large. The amount of data collected in this paper is small, so the effect is poor. However, after adding the attention mechanism (BiLSTM-Att), the classification effect significantly improved, indicating that the attention mechanism enables the model to learn more important features, thereby improving the performance of the neural network. Additionally, compared with the neural network using the traditional static word embedding, the neural network built with the ERNIE \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R** & **_P_** & **_F_1** \\ \hline BiLSTM & 74.72 & 75.73 & 74.44 \\ TextCNN & 83.21 & 83.59 & 83.33 \\ RCNN & 81.29 & 82.09 & 81.35 \\ BiLSTM-Att & 81.51 & 82.05 & 81.58 \\ ERNIE & 86.37 & 86.77 & 86.51 \\ ERNIE-BiLSTM & 86.56 & 86.64 & 86.52 \\ ERNIE-CNN & 86.37 & 86.77 & 86.36 \\ ERNIE-RCNN & 86.81 & 85.77 & 85.01 \\ EBLA & 87.87 & 87.55 & 87.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of different sentiment analysis models %. word embedding model greatly improved its classification effect, with an average increase of 6.7% in precision, 5.82% in recall, and 6.17% in \(F1\) value. Furthermore, it shows that the ERNIE word embedding model has a strong semantic representation ability, and achieved good results in dealing with the dimension mapping and polysemy problems in the Chinese e-commerce product review. The combination of ERNIE and BiLSTM-Att effectively improved the performance of model sentiment classification. The EBLA model used in this paper achieved the best results in the sentiment analysis experiment of the Chinese e-commerce product reviews. Compared with the BiLSTM, TextCNN, RCNN, BiLSTM-Att, ERNIE, ERNIE-BiLSTM, ERNIE-CNN, and ERNIE-RCNN models, the \(F1\) value of the EBLA model increased by 13.04%, 4.15%, 6.13%, 5.9%, 0.97%, 0.96%, 1.12%, and 2.47%, respectively. The EBLA model, too, achieves more than 0.87 in precision, recall, and \(F1\) values, and it can be seen that this model has an excellent performance on the sentiment analysis task of the Chinese e-commerce product reviews.