# Sentiment Analysis of Online New Energy Vehicle Reviews
## 5 Experimental Results
### Comparison of Accuracy Experimental Results  
In sentiment analysis tasks, accuracy (Acc) plays a vital role as a performance metric, playing a key role in evaluating the effectiveness and usability of sentiment analysis models. Acc helps assess the model's ability to accurately predict sentiment polarity, thereby providing valuable insights into its performance. Higher Acc values indicate that the model can precisely classify sentiment in review texts, demonstrating a better understanding of the text's emotional nuances. Figure 9 presents a comparative analysis of the accuracy results between the EDC model and the word2 vec-based and BERT pretrained models, allowing for a comprehensive assessment of their accuracy performances.  
Figure 8: Comparison of loss values between EDC and different models. (**a**) Comparison with word2 vec word embedding model. (**b**) Comparison with BERT word embedding model.  
Figure 9: Comparison of accuracy (Acc) values between EDC and different models. (**a**) Comparison with word2 vec word embedding model. (**b**) Comparison with BERT word embedding model.  
Based on the accuracy plot shown in Figure 9, the following conclusions can be drawn:  
When comparing the accuracy of the EDC model with models using word2 vec as the pretraining method, it is evident that the TextCNN, DPCNN, and EDC models outperform the other models in terms of accuracy. These models exhibit higher and more stable accuracy growth. Notably, the EDC model demonstrates exceptional stability at the final data points, consistently maintaining a high accuracy level. This suggests that these models are adept at capturing the semantics and features of the text, resulting in improved accuracy. On the other hand, the TextRNN and Transformer models show relatively average performances. Their accuracy growth is more gradual, indicating the need for further optimization and additional training data to fully unlock their potential. This implies that these models may benefit from more extensive learning and adjustments to enhance the accuracy in text classification tasks. As for the TextRCNN and TextRNN_Att models, they fall in the middle, displaying moderate performances. Their accuracy performances are relatively average compared to the other models. However, the TextRNN_Att model incorporates an attention mechanism, which may offer advantages in handling long text sequences. The attention mechanism allows the model to focus on key words and contextual information, thereby enhancing the classification performance. Nevertheless, it still falls short of surpassing the EDC model's performance.  
When compared to models using BERT as the pretraining method, the EDC model consistently demonstrates superior accuracy across different training epochs. In the initial epoch (Epoch 1), the EDC model achieves a higher accuracy (97.19%) than the other four BERT-based models (BERT_CNN, BERT_RNN, BERT_RCNN, and BERT_DPCNN). As the training epochs progress, the accuracy of all models gradually improves and tends to converge. However, the EDC model consistently maintains a relatively high and stable accuracy (over 97%) in subsequent epochs, highlighting its competitive advantage in terms of accuracy.  
In conclusion, based on the accuracy results, the EDC model exhibits exceptional performance, particularly in terms of stability and competitiveness, when compared to models using word2 vec and BERT as the pretraining methods.