## 3 Research Process and Methods
### Emotional Image Words for Ordering Interfaces  
The main aim of principal component analysis (PCA) is to extract the principal components that can maximize the explanation of the variance in the data, helping us to identify the underlying patterns and important features in the data, which can reduce the problem of multiple covariance between the variables and make the data easier to interpret in a high-dimensional space. This can reduce multiple highly correlated perceptual image words into a few principal components, thus simplifying the data structure and avoiding the redundant information interference, making the subsequent analysis more clear and effective [59].  
In this study, we use PCA to deal with the correlation between perceptual imagery words and the problem of excessive data dimensionality. In order to explore the sorting interface, the correlations between the data were analyzed using SPSSAU software. The results showed that the [PERSON]'s alpha coefficient reached 0.927, indicating that the questionnaire had high internal consistency and good data reliability. Meanwhile, the KMO value was 0.742, indicating that the variables had correlations suitable for factor analysis. In addition, the results of [PERSON]'s test of sphericity showed that the approximate chi-square value was 171.912, the degree of freedom was 36, and the significance \(p\)-value was less than 0.05, which further confirmed that the correlation between the variables was statistically significant and suitable for PCA analysis [60]. According to the judgment criteria of the scree plot, the first three principal components with eigenroot values greater than 1 were selected. The first principal components \"complex-simple\", \"uncomfortable-comfortable\", \"fuzzy-intuitive\", and \"distraction\" can be categorized as operation and function; the second principal components \"outdated-modern\" and \"indifferent-friendly\" can be categorized as related to visual and emotional impact; and the third principal components \"unpleasant-enjoyable\", and \"unidirectional-interactive\" can be categorized as related to interaction and feedback experience, as shown in Table 4. This not only simplified the structure of variables but also makes our interpretation of the data more intuitive and clear.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sample** & **Sensual Image Vocabulary Pairs** & \multicolumn{6}{c}{**Quantitative Score**} \\ \hline Complex–Simple & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ Uncomfortable–Comfortable & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ Unpleasant–Pleasant & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ Obscure–Intuitive & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ Outdated–Modern & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ False–Trusted & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ One-way–interactive & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ Indifferent–friendly & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ Distracted–Focused & \(-3\) & \(-2\) & \(-1\) & \(0\) & \(1\) & \(2\) & \(3\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of a semantic survey questionnaire on the ordering interface.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample Code** & **Simple** & **Comfortable** & **Pleasant** & **Intuitive** & **Modern** & **Credible** & **Interactive** & **Friendly** & **Dedicated** \\ \hline Sample \(1\) & \(1.47\) & \(-1.30\) & \(0.42\) & \(-0.22\) & \(0.20\) & \(-0.17\) & \(-0.15\) & \(-0.47\) & \(-1.14\) \\ Sample \(2\) & \(-0.30\) & \(2.02\) & \(1.42\) & \(1.15\) & \(-0.76\) & \(-0.78\) & \(-0.02\) & \(0.