## 4 Empirical Study
### Analysis and Results
#### 4.2.3 Phase Three: Domain-Adaptive Sentiment Analysis (DASA)  
This section aims to quantify and clarify the emotional tendencies of thematic elements in the UX of competitive BEVs through DASA. We conducted a comprehensive investigation of users' emotional reactions to various influencing factors. Step 1. Corpus preprocessing: We standardized and refined the original data from competing products, aligning them with the multidimensional influencing factors identified in the previous stage. Separately, we extracted text data relevant to the topic keywords to form a corpus for DASA. This corpus included four topic-specific sub-corpora and ten corresponding independent sub-corpora for the influencing factors. Step 2. Constructing a training set for DASA: We used the Python-based sentiment analysis package SnowNLP to analyze the extracted corpus. To enhance the accuracy of DASA, a pre-trained model was developed using manual labeling as the training dataset. DASA of UX texts for four types of BEVs was performed. Initially, we gathered texts with the keyword \"BEV experience\" from the platform where the test dataset was sourced. We employed the built-in training dataset for sentiment scoring, manually selected the appropriate positive and negative review samples, and integrated them into the training dataset to create a customized corpus. Subsequently, we manually assigned sentiment scores to the extracted theme-related corpus, which acted as the test dataset. We used the customized corpus for machine learning, assigned sentiment scores to the test dataset, and compared these with manual labeling, repeating this process until an accuracy of 83.9% was attained in the evaluation test, thereby creating a specialized training dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Topic Number**} & **Primary Factor** & \multirow{2}{*}{**Secondary Factors (Top 10 Keywords’ Ranking)**} \\  & **(Topic Label)** & \\ \hline T1 & SP & Space, Design, Model, Feeling, Interior, Rear Seat, Mode, \\  & & Experience, Seat, Body \\ \hline T2 & IB & Voice, Car Owner, Mode, Music, Car Machine, Mobile Phone, \\  & & Assistance, Key, Intelligent, Autonomous Driving \\ \hline T3 & SS & Brand, Price, Model, Product, Market, Consumer, Service, \\  & & Tradition, Subsidy, Domestic \\ \hline T4 & DE & Battery, Brake, Mileage, Parking, Route, Parking Lot, \\  & & Platform, Map, Power, Temperature \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Topic label encoding of four types of BEVs. Ultimately, we applied the optimized training dataset to calculate sentiment scores for BEVs and presented the semantic sentiment probabilities. Step 3. DASA for thematic keywords: We conducted DASA on the extracted thematic keywords with the refined training set, quantified the sentiment of influencing factors in user experiences of competitive products, pinpointed pain points, and identified best practices for enhancing product UX. Initially, we performed a comprehensive DASA on the thematic corpus. We employed the refined training set to assign sentiment scores ranging from 0 to 1, with the following categorizations: scores of 0.9 or above are \"excellent\", 0.7 to 0.9 are \"good\", 0.3 to 0.7 are \"neutral\", 0.1 to 0.3 are \"poor\", and below 0.1 are \"very poor\". We created a histogram of sentiment scores and determined the proportion of each sentiment category. Subsequently, using \"interior\" as an example, we conducted a detailed analysis of UX sentiment, adjusted the score range to 0-1 with a granularity of 0.02, excluded data at the extremes of 0 and 1, and created a histogram. Results of phase three:  
1. From the analysis of T1, Figure 2 shows a pronounced positive sentiment, \"excellent\", comprising over one-third of the responses. TC2 exhibited the highest proportion of positive sentiment at 44%, whereas TC1 had the lowest at 34%. Users of TC1 reported the highest rate of extremely negative evaluations, 13%, while those of TC2 reported the lowest, at 10%.