用双向LSTM分析电动车用户评论情感强度，更精准捕捉细微情绪。
Fine-Grained Sentiment Analysis of Electric Vehicle User Reviews: A Bidirectional LSTM Approach to Capturing Emotional Intensity in Chinese Text
- 采用双向LSTM建模中文评论，量化情感强度至0-5分。
- 在43,678条评论上，误差比SnowNLP降低超20%。
- 适合关注电动车服务优化的车企与平台方参考。
电动汽车产业的快速发展凸显了用户反馈在改进产品设计与充电基础设施中的重要性。传统情感分析方法常简化用户情绪复杂性，难以捕捉细微情感与强度。本研究提出基于双向长短期记忆网络（Bi-LSTM）的情感评分模型，对电动车充电设施用户评论进行细粒度分析。通过0到5的评分体系，实现对情感表达的精细化刻画。基于来自PC Auto的43,678条评论数据，经分词、去停用词等预处理后输入模型。实验表明，该模型在均方误差（MSE）、平均绝对误差（MAE）和解释方差分数（EVS）等指标上显著优于SnowNLP等传统方法，展现出更强的情感动态捕捉能力，为电动车生态中产品与服务的精准优化提供有力支持。
原文摘要 · Abstract (English)
The rapid expansion of the electric vehicle (EV) industry has highlighted the importance of user feedback in improving product design and charging infrastructure. Traditional sentiment analysis methods often oversimplify the complexity of user emotions, limiting their effectiveness in capturing nuanced sentiments and emotional intensities. This study proposes a Bidirectional Long Short-Term Memory (Bi-LSTM) network-based sentiment scoring model to analyze user reviews of EV charging infrastructure. By assigning sentiment scores ranging from 0 to 5, the model provides a fine-grained understanding of emotional expression. Leveraging a dataset of 43,678 reviews from PC Auto, the study employs rigorous data cleaning and preprocessing, including tokenization and stop word removal, to optimize input for deep learning. The Bi-LSTM model demonstrates significant improvements over traditional approaches like SnowNLP across key evaluation metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS). These results highlight the model's superior capability to capture nuanced sentiment dynamics, offering valuable insights for targeted product and service enhancements in the EV ecosystem.
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