# Sentiment Analysis of Online New Energy Vehicle Reviews
## 2 Related Works  
### Sentiment Analysis  
Sentiment analysis plays a crucial role in understanding consumer opinions, satisfaction, and needs, which, in turn, can facilitate product optimization and service improvement [20; 21; 22]. Additionally, sentiment analysis helps businesses gain insights into market feedback, competitive trends, brand reputation, loyalty, and identifying strengths and areas for improvement [23]. Promptly addressing negative public sentiment allows businesses to safeguard their brand image and reputation.  
Recent research indicates that deep learning methods, leveraging large datasets to learn feature representations automatically, outperform traditional machine learning techniques. [PERSON] et al. introduced a novel complementary fusion model for the text classification of food comments, combining Bidirectional Encoder Representations from Transformers (BERT), Convolutional Neural Networks, Bidirectional Long Short-Term Memory (BIL-STM), and attention mechanisms, leading to significant improvements in classification accuracy [24]. [PERSON] et al. proposed a sentiment classification model based on Sliced Bidirectional Gated Recurrent Unit (Bi-GRU), multi-head self-attention mechanisms, and BERT embeddings, addressing issues such as word vector ambiguity, the inability to train recurrent neural networks in parallel, and low classification accuracy. The experimental results demonstrated high classification accuracy on the Yelp 2015 dataset and Amazon dataset, along with faster training compared to other models, validating its effectiveness [25]. [PERSON] et al. [26] introduced ServeNet, a deep neural network model that combines BI-LSTM and stacked CNN for automatic service classification, demonstrating a strong performance on the dataset. Traditional methods (e.g., CNN, RNN, and LSTMs) employ context-independent word embedding methods (e.g., Word2 Vec and GloVe) for encoding web service data, which has limitations due to fixed embedding vector positions. In contrast, the BERT model, based on Transformer, utilizes attention mechanisms to extract contextual text features, offering advantages in service classification. Traditional methods overlook word context in the text, while BERT considers it, further enhancing its performance. [PERSON] et al. [27] proposed an extended BERT-DCNN model for sentiment analysis on social media. This model utilizes pretrained BERT language models to generate word embeddings and enhances the capture of local text features through an extended DCNN (deep Convolutional Neural Network) layer and global average pooling layer. The model's simple structure enables efficient parallel processing, holding promise in the field of sentiment analysis on social media data. However, accurately distinguishing different sentiment tendencies in comments on new energy vehicles remains challenging due to the complexity of specialized vocabulary, incomplete context information, and the high volume of comments generated within short periods. To address this issue, deep learning techniques are applied to construct more detailed semantic correlations, aiming to overcome these challenges.