## 5 Discussion  
### Interpretation of Results  
The study's initial phase utilized LDA and pyLDAvis for thematic modeling, successfully identifying core topics within the UGC related to BEVs. These topics were found to be highly correlated with key dimensions of the UX, including subjective perception, interaction behavior, social symbolism, and driving experience. By mapping these topics onto [PERSON]'s hierarchical UX theory, the study achieved a systematic understanding of user commentary, establishing a foundation for DASA and providing a structured framework for elucidating the holistic BEVDX. This approach not only identified specific aspects of user concern but also revealed their relative significance within the UX paradigm, offering theoretical guidance for electric vehicle improvements and corroborating the thematic modeling method's effectiveness with existing literature.  
In the second phase, the establishment of a coding system for the top 10 keywords (level 2 factor) under each comprehensive theme label (level 1 factor) streamlined data complexity, enhancing the precision and efficiency of subsequent analyses. Under the SP theme, users placed particular emphasis on aspects such as space, design, and vehicle models. These keywords exposed the core elements of the UX and offered a solid foundation for DASA. The multidimensional factor coding provided a more comprehensive perspective compared to traditional single-dimensional analysis methods, capturing key factors in the UX more accurately.  
The third phase employed SnowNLP for DASA, revealing significant differences in users' subjective feelings toward different competitive products. TC2 outperformed the others in positive evaluations, while TC1 had a higher proportion of negative evaluations within the primary influencing factor of subjective feelings. This finding is of considerable importance for manufacturers looking to optimize product design. Additionally, sentiment measurement analysis of the secondary influencing factor \"interior\" indicated that TC1 had the highest proportion of negative assessments, whereas TC2 had the highest proportion of positive evaluations. These insights unveiled specific user demands and expectations regarding interior design, showcasing the practicality of DASA in identifying emotional tendencies and quantifying the performance of various competitors in specific aspects.  
The fourth phase involved CONA, which uncovered the complex associations between different emotional factors. By analyzing the co-occurrence association network of the extremely negative and strongly positive experiences of the level 2 factor \"interior\", specific tertiary associating factors affecting UX polarity were identified. TC1's negative sentiment was primarily concentrated on material and gap issues, while TC2 faced criticism for its red interior trim and high-gloss panels. On the other hand, TC1's technological sense and design style, along with TC2's leather texture and seat design, received high praise from users. The EC analysis identified key emotional factors in user commentary, offering a novel perspective for understanding the emotional associations in UX and providing abundant data for further sentiment analysis.  
Although this study presented conclusions based on the emotional associations of tertiary factors within the level 1 factor \"subjective perception\" and the level 2 factor \"interior\", its core value lies in constructing a systematic and multi-level research framework for the multidimensional elements of UX. This framework provides a new perspective for understanding UX and reveals potential connections between different factors through multi-level analysis.