## 3 Research Methods
### Data Analysis
#### 3.4.1 Phase One: Topic Modeling with Latent Dirichlet Allocation (LDA) and pyLDAvis
Step 5. Document topic assignment and interpretation: Each document was assigned to a topic based on the probability distribution derived from the LDA model. Topics were then interpreted using domain-specific knowledge, focusing on feature words that define each topic. This helped identify significant themes related to BEVUX and classify potential influencing factors. This topic modeling process facilitated a deeper understanding of the complexity of the UX, providing actionable insights for BEV manufacturers to improve product design and user satisfaction. 4.2 Phase Two: Weight Calculation of Multidimensional Factors Based on the Entropy Weight Method (EWM)  
The EWM determines the significance of keywords in user reviews by measuring informational entropy, highlighting their relevance in the context of user experience. The EWM refines unstructured review data into precise and unbiased keyword weights, which are critical for subsequent sentiment analysis and association mining. Based on information theory, the EWM evaluates information uncertainty by computing the entropy of each keyword's distribution in the dataset. A higher entropy value indicates greater information dispersion and lower relevance, whereas lower entropy suggests concentrated information and a higher weight. The procedure was as follows:  
Step 1. Data standardization: The frequency data of each keyword was normalized to mitigate discrepancies in the frequency data across different keywords. The normalization formula is as follows, see Equation (6):  
\[p_{ij}=\frac{x_{ij}}{\sum\limits_{i=1}^{n}x_{ij}} \tag{6}\]  
where \(p_{ij}\) represents the relative frequency of the \(j\)-th keyword within the \(i\)-th document, \(x_{ij}\) is the original frequency, and \(n\) is the total number of documents. Step 2. Entropy computation: The normalized data were used to ascertain the entropy for each keyword, employing the entropy formula as follows (7):  
\[E_{j}=-k\sum\limits_{i=1}^{n}p_{ij}\log p_{ij} \tag{7}\]  
where \(k\) is a constant, usually taken as \(k=\frac{1}{\log n}\). If \(p_{ij}=0\), then define \(\log p_{ij}=0\). Step 3. Coefficient of variation determination: The coefficient of variation was computed for each keyword, applying the formula as follows (8):  
\[CV_{j}=\frac{\sqrt{\sum\limits_{i=1}^{n}\left(p_{ij}-\overline{p}_{j}\right)^ {2}}}{\overline{p}_{j}} \tag{8}\]  
where \(CV_{j}\) represents the coefficient of variation for the \(j\)-th keyword. The term \(p_{ij}\) denotes the relative frequency of the \(j\)-th keyword in the \(i\)-th document, while \(\overline{p}_{j}\) is the average relative frequency of the \(j\)-th keyword across all documents, calculated as \(\overline{p}_{j}=\frac{1}{n}\sum\limits_{i=1}^{n}p_{ij}\). The variable n stands for the total number of documents. Step 4. Keyword weight assignment: The weight of each keyword was determined based on its coefficient of variation, using the formula as follows (9):  
\[W_{j}=\frac{1}{m}\cdot\frac{1}{CV_{j}} \tag{9}\]  
where \(W_{j}\) is the weight of the \(j\)-th keyword, and \(m\) is the number of keywords. These steps ensured that the frequency data of different keywords were standardized, and their importance weights were calculated based on their distribution in the texts. This process provides a solid foundation for subsequent SA and association mining, facilitating comprehensive and systematic analysis of the multidimensional factors affecting BEVUX.