Multidimensional User Experience Analysis of Chinese Battery Electric Vehicles’ Competition: An Integrated Association Mining Framework

Abstract

This study introduces an integrative framework for association mining within the Chinese battery electric vehicle market, aiming to reveal key user experience (UX) factors and their interrelationships through multidimensional analysis. Utilizing latent Dirichlet allocation (LDA), the study discerned primary themes from user-generated content (UGC). The entropy weight method categorized level 2 factors, while domain-adaptive sentiment analysis quantified emotional responses to BEV user experience dimensions, highlighting significant sentiment disparities among competitors. Co-occurrence network analysis deepened insights into the emotional fabric of UX by exploring tertiary factor associations. Theoretically, this study advances a novel framework informed by Norman’s UX theory, integrating analytical techniques to capture the complexity of UX. Practically, it delivers strategic guidance for BEV manufacturers by analyzing emotional polarities and attribute associations, guiding product innovation and responding to market dynamics. The empirical evidence corroborates the framework’s efficacy in revealing the emotional associations within BEVUX factors, offering valuable implications for both theoretical development and practical application.

Keywords

• battery electric vehicle
• user experience
• multidimensional analysis
• domain-adaptive sentiment analysis
• co-occurrence network analysis