# Sentiment Analysis of Online New Energy Vehicle Reviews
## 5 Experimental Results
### Comprehensive Comparison of Experimental Evaluation Metrics  
To further validate the efficacy of our model in sentiment classification tasks, we compare the accuracy (Acc), precision (Pre), recall (Rec), and F1 score of various emotions exhibited by the baseline models with our enhanced EDC model. The comparative results are presented in Table 3.  
According to the table, the EDC model achieves an accuracy of 97.39%. In terms of precision, it demonstrates values of 0.9728 and 0.9749 for the NEG and POS categories, respectively. The recall rates for NEG and POS are 0.9753 and 0.9724, respectively. The corresponding F1 scores are 0.9740 and 0.9737. Comparing these results with the other baseline models, our proposed EDC model consistently outperforms them in all the evaluation metrics. This indicates that our model excels in accurately identifying sample labels and maintains a high recognition ability, especially for positive samples. The experimental findings highlight the superior classification performance and generalization ability of our model. Additionally, Figures 13 and 14 present the accuracy (acc) and loss rates (loss) of the EDC model on the training and validation sets. The model achieves an impressive accuracy of 98.82% on the training set and 97.45% on the validation set. As training progresses, the loss values steadily decrease, reaching 0.02661 for the training set and 0.08762 for the validation set. These values significantly outperform the other baseline models, indicating the effectiveness and efficiency of our proposed model. Overall, our model surpasses other models across various evaluation metrics, showcasing its ability to capture essential data features and enhance the effectiveness of automotive reviews. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **Accuracy** & **Precision** & **Precision** & **Recall** & **Recall** & **F1 Score** & **F1 Score** \\ \hline  & & **NEG** & **POS** & **NEG** & **POS** & **NEG** & **POS** \\ \hline FastText & 86.63\% & 0.8675 & 0.8651 & 0.8665 & 0.8661 & 0.8670 & 0.8656 \\ TextCNN & 91.60\% & 0.9088 & 0.9236 & 0.9259 & 0.9060 & 0.9173 & 0.9147 \\ TextRCNN & 89.70\% & 0.8959 & 0.8981 & 0.8997 & 0.8943 & 0.8978 & 0.8962 \\ TextRNN & 84.77\% & 0.8822 & 0.8185 & 0.8045 & 0.8914 & 0.8416 & 0.8534 \\ TextRNN\_Att & 89.19\% & 0.8830 & 0.9013 & 0.9048 & 0.8787 & 0.8938 & 0.8899 \\ Transformer & 87.79\% & 0.8984 & 0.8591 & 0.8537 & 0.9023 & 0.8755 & 0.8802 \\ DPCNN & 93.57\% & 0.9314 & 0.9402 & 0.9415 & 0.9299 & 0.9364 & 0.9350 \\ BERT\_CNN & 94.27\% & 0.9392 & 0.9464 & 0.9474 & 0.9379 & 0.9433 & 0.9421 \\ BERT\_RNN & 94.36\% & 0.9400 & 0.9472 & 0.9483 & 0.9388 & 0.9441 & 0.9430 \\ BERT\_RCNN & 94.63\% & 0.9408 & 0.9520 & 0.9531 & 0.9394 & 0.9469 & 0.9456 \\ BERT\_DPCNN & 94.49\% & 0.9399 & 0.9500 & 0.9511 & 0.9385 & 0.9455 & 0.9442 \\ EDC & 97.39\% & 0.9728 & 0.9749 & 0.9753 & 0.9724 & 0.9740 & 0.9737 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Acc, Pre, Recall, and F1 scores for different emotion polarities among the models. Table 4 provides an overview of the confusion matrix for the EDC model, showcasing the quantities of true positives, true negatives, false positives, and false negatives. Specifically, the model achieved accurate classification for 3384 out of 3480 negative emotions and 3433 out of 3450 positive emotions, demonstrating its high precision in identifying positive and negative sentiments within the dataset.