From user-generated content to quality improvement: A multi-granularity analysis of customer satisfaction and attention in new energy vehicles using deep learning

Abstract

Understanding customer satisfaction is crucial to improving product quality and ensuring the market competitiveness of new energy vehicles (NEVs). Although user-generated content (UGC)-based analysis offers a cost-effective alternative to traditional customer satisfaction surveys, existing studies have largely overlooked users’ fine-grained needs and rarely translated sentiment insights into actionable guidance for product improvement. To address this, we propose a novel Multi-Aspect Dynamic Knowledge Graph Convolutional Network to extract aspect-level customer perceptions from UGC. The model utilizes a scaled dependency matrix to filter redundant syntactic relations and captures semantic interactions across various aspects. It integrates a sentiment knowledge base with a cross-attention mechanism to enhance sentiment feature extraction. Leveraging the extracted sentiment, we develop a quantitative method to evaluate customer attention and satisfaction across multi-granularity indicators. Experiments on benchmark datasets show that our model outperforms most state-of-the-art methods. A case study of BYD NEVs based on 55,511 sentences from Autohome further validates its superiority, achieving 91.46% Macro-F1 and 91.41% accuracy. Furthermore, by incorporating a customized importance–performance analysis, we pinpoint high-attention aspects with low satisfaction, such as air conditioner and trunk size, which are subsequently integrated into a house of quality measure to support quality improvement. Our analysis further reveals a steady improvement in customer satisfaction across major aspects, despite temporary declines in certain years. We also observe a 14% decline in attention to battery range, alongside a 3.7% increase in vehicle space. These insights can help NEV manufacturers align their product quality improvement efforts with evolving customer expectations.