# Sentiment Analysis of Online New Energy Vehicle Reviews
## 1 Introduction
Through sentiment analysis, deeper insights into the sentiment polarity embedded within textual descriptions can be gained, enabling more accurate evaluations of consumers' perceptions of vehicle comfort. With the widespread popularity of social media and mobile devices, people have become accustomed to sharing their views and opinions on social media platforms. User-generated content, such as online reviews, has emerged as a more authentic reflection of users' thoughts compared to traditional survey methods, providing valuable insights [15]. Recent studies have demonstrated the use of sentiment analysis based on comments to determine evaluation criteria for electric vehicles, further emphasizing the significant potential of textual comments and sentiment analysis in the consumer goods domain [16]. Therefore, it is foreseeable that comments and sentiment analysis will continue to play a crucial role in providing consumers with more accurate and valuable information as social media and mobile devices continue to prevail. Sentiment analysis methods serve as a vital tool in understanding consumer emotions and feedback within consumer goods reviews. [PERSON] et al. developed a sentiment analysis system for hotel reviews that delivers precise evaluation reports on hotel service quality based on user-specified criteria [17]. [PERSON]. [PERSON] et al. proposed a comprehensive method for detecting fake reviews, utilizing various features related to comments. By combining linguistic features, part-of-speech features, and sentiment analysis features, and employing rule-based classifiers for inference and enhancement, they achieved improved detection performance [18]. [PERSON] et al. introduced a hybrid approach to sentiment analysis, encompassing preprocessing, feature extraction, and sentiment classification. They employed NLP techniques to process comment data, constructing unique hybrid feature vectors. Deep learning classifiers were used for sentiment classification, and experiments conducted on multiple comment datasets yielded positive outcomes [19]. Despite the significant role of sentiment analysis methods in consumer goods reviews, challenges persist in the sentiment analyss of new energy vehicle reviews. Firstly, the context information in comments may be incomplete, leading to ambiguity in sentiment analysis results. Secondly, sudden events, such as the recent BMW Mini controversy, have generated a surge of comments and discussions regarding product quality, safety, and brand image. Additionally, the field of new energy vehicles encompasses numerous specialized terms that necessitate domain-specific knowledge for a better understanding of their meanings. To address these challenges, this study presents the Emotion Detection for Electric Vehicle Comments method called EDC, which brings several innovative contributions:  
1. A comprehensive dataset of comments specifically focused on new energy vehicles is curated. Accurate polarity labels for each comment are obtained through meticulous pre-processing steps, including data cleaning, tokenization, and sentiment category labeling. 2. Multiple layers of Convolutional Neural Networks are leveraged by our deep Convolutional Neural Network model to extract localized features from sentences. The vanishing gradient problem is mitigated by introducing residual connections, and diverse channel representations are effectively fused using channel attention to adeptly handle cases of ambiguous sentiment tendencies in comments. Furthermore, contextual information, especially in the face of abrupt and unexpected comments, is captured excellently by our model. 3. The novel hybrid sentiment analysis model is enhanced by combining the strengths of ERNIE and deep CNN. ERNIE, which is based on the Transformer deep learning architecture, demonstrates remarkable performance in pretraining Chinese language models.