Sentiment and Topic Analytics for Electric Vehicle User Reviews

With the advancement of the “dual carbon” goals, the electric vehicle market has experienced explosive growth, and user review mining has become key data support for industrial quality improvement and low-carbon transportation transition. Addressing the limitations of existing sentiment classification methods in long-distance feature capture, cross-sentence semantic association, and emotional feature focus, this study proposes a BERT-Bi-xLSTM-Attention fusion model: BERT pre-trained semantic representation extracts deep contextual information, Bi-xLSTM models long-range dependency relationships, and the Attention mechanism locates sentiment-critical markers. Based on multi-platform review data from Chinese Autohome, Yiche, and China Quality Inspection Network, experiments show that the model achieves Accuracy, Recall, Precision, and F1 values of 0.9323, 0.9326, 0.9321, and 0.9328, significantly outperforming baseline models. A “sentiment-topic” fusion analysis framework is constructed, identifying five positive themes and four negative themes, revealing the dual emotional characteristics of range, driving experience, and smart features. Temporal analysis finds that negative attention to intelligent system reliability has continued to rise from 2021 to 2024, becoming an emerging user pain point. Combined with the above findings, it is recommended that consumers comprehensively evaluate multi-attribute experiences when purchasing; manufacturers prioritize optimizing user-concerned attributes; and policymakers improve industrial standards and regulatory mechanisms. This promotes high-quality development of electric vehicles, contributes to the realization of carbon neutrality goals in the transportation sector, and facilitates sustainable transportation development.