## 4 Empirical Study
### Analysis and Results  
#### 4.2.1 Phase One: Topic Modeling Utilizing Latent Dirichlet Allocation (LDA) and pyLDAvis  
In this section, an effective topic model was constructed to analyze potential UX factors related to BEVs. The LDA model, selected for its robust capability, served as the primary analytical tool owing to its capacity for revealing latent topics within textual data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TC1** & **TC2** & **TC3** & **TC4** \\ \hline Level & Mid-size car & Mid-to-large car & Mid-size car & Mid-size car \\ Release Date & May 2019 & July 2020 & December 2021 & March 2022 \\ NEDC Pure Electric & 445 & 550 & - & - \\ Range (km) & - & - & 710 & 526 \\ \hline Body Structure & 4-door, 5-seat & 4-door, 5-seat & 4-door, 5-seat & 4-door, 5-seat \\ Dimensions (mm) & 4694 \(\times\) 1850 \(\times\) 1443 & 4980 \(\times\) 1910 \(\times\) 1495 & 4790 \(\times\) 1960 \(\times\) 1499 & 4872 \(\times\) 1846 \(\times\) 1481 \\ Official 0-100 km/h & & & & \\ Acceleration (s) & 5.6 & 3.9 & 4 & 6.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on data collection objects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **TC1** & **TC2** & **TC3** & **TC4** \\ \hline Response Time & 1 April 2016 to 17 & 10 May 2020 to 14 & 9 January 2021 to 14 & 3 August 2013 to 15 \\  & November 2022 & November 2022 & November 2022 & November 2022 \\ Number of Respondents & 1126 & 442 & 524 & 128 \\ Number of Words & 435,097 & 161,791 & 269,917 & 85,200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Raw data collection information. Step 1. Feature extraction: This involved converting raw text data into a numerical format suitable for machine learning techniques. Employing a bag-of-words approach, the 1000 most prominent feature words were identified, chosen for their prevalence and relevance within the corpus. Words occurring at the extremes of frequency were omitted to concentrate on significant terms, thereby generating a numerical feature set that was well suited for LDA analysis. Step 2. Optimal topic number determination: An experimental approach was utilized, with varying numbers of topics established and corresponding perplexity scores calculated to evaluate model performance. The optimal topic count was determined through perplexity measurements and the application of the elbow method, balancing topic clarity and model intricacy. The detailed perplexity outcomes and LDA model visualizations for the UX corpora of the four TCs are provided in Appendix A, Table A1. Specifically, these experiments identified seven topics for TC1, five topics for TC2, six topics for TC3, and three topics for TC4. Iterative experimentation ensured model stability and convergence. Visualization with pyLDAvis facilitated an intuitive comprehension of each topic, enhancing the interpretation and presentation of the model. Step 3. LDA model analysis: Once the optimal number of topics was validated, the LDA model analysis proceeded to extract topics and corresponding keywords, along with the proportion of each topic in the documents. For instance, in the case of TC1, the top 20 keywords for each topic were identified according to their probability distribution, normalized on a scale from 0 to 1. The detailed results are delineated in Appendix A, Table A2. Phase one results--classification of topic labels: The intrinsic nature of each topic was identified by examining the top 20 keywords extracted from each topic. The model assigned each review document to a specific topic, thereby streamlining the data-labeling process. Expanding upon the research of [PERSON] et al. on plug-in electric vehicles (PEVs) [63], which encompassed the BEVUX, this study analyzed 6492 references from diverse sources to identify a range of topics related to UX, typically divided into driving and travel behavior, interaction with the vehicle, and subjective aspects of UX.