# Sentiment Analysis of Online New Energy Vehicle Reviews
## 6 Conclusions  
In this study, an algorithm for sentiment analysis is proposed, utilizing a combination of the ERNIE and deep CNN models. The approach involves extracting sentiment word-level feature vectors from new energy vehicle reviews using the enhanced ERNIE model. Subsequently, a deep CNN network is employed to hierarchically extract features from these vectors. The deep CNN architecture utilizes alternating convolutional layers and downsampling layers to transform discrete text into continuous representations, thereby minimizing the data and computational requirements at each layer. To enhance word-level embeddings and compress the sequence length, parallel convolutional layers are introduced, and max pooling is applied after each convolution. Channel attention mechanisms are also incorporated to allocate processing resources to informative parts of the input data, enabling the model to capture the relevance of comments and identify global semantic information in the sentiment analysis. To address class imbalance, the Focal Loss function is utilized, which downweighs easily classifiable samples, allowing the model to focus more on feature word samples with ambiguous sentiment expressions and the sentiments of automotive review features. The performance of the improved model in the sentiment analysis for electric vehicles is evaluated through experiments conducted on a curated dataset comprising a large number of new energy vehicle reviews. The dataset is partitioned into training, validation, and testing sets, with the training set used for model parameter training, the validation set for hyperparameter selection and model optimization, and the testing set for evaluating the model's performance. The experimental results demonstrate that the improved model achieves outstanding performance in the sentiment analysis for electric vehicles, as evidenced by key metrics such as accuracy, precision, and the F1 score. High accuracy is observed on the testing set, indicating the model's capacity to accurately predict the sentiment orientation expressed in electric vehicle reviews. Exceptional precision is demonstrated, with the accurate identification of positive, negative, and neutral sentiments in electric vehicle reviews. The F1 score, which combines precision and recall, further highlights the model's strong performance in identifying sentiment orientation. Overall, the experimental findings support the conclusion that the improved algorithm represents a significant advancement in sentiment analysis for electric vehicles. It exhibits enhanced capabilities in accurately analyzing and predicting sentiment orientation in new energy vehicle reviews, showcasing commendable performances in sentiment analysis tasks. These findings provide compelling evidence of the effectiveness and potential of the algorithm in real-world applications. While significant progress has been made in this research, there are still opportunities for improvement. Future work will explore advanced model fusion methods at a deeper level and simplify model parameters to reduce complexity. The applicability of the approach in different domains will be investigated to validate its generalization performance on diverse datasets, including multilingual datasets. Additionally, techniques for identifying and eliminating fraudulent reviews will be researched to enhance data quality. The goal is to improve the accuracy of the model by exploring additional sentiment categories and incorporating relevant concepts, expanding the scope of the research on the applicability of sentiment analysis models. Conceptualization, M.W. and H.Y. ; data curation, M.W. and X.S. ; formal analysis, M.W., H.Y., X.S. and Z.W. ; methodology, M.W. ; project administration, H.M.; resources, M.W. and H.M.; software, M.W. ; supervision, H.M.; validation, M.W., H.Y., X.S. and Z.W. ; visualization, M.W. ; writing--original draft, M.W. ; and writing--review and editing, H.M. and M.W. All authors have read and agreed to the published version of the manuscript.