## 2 Materials and Methods
### Product Attribute Extraction and Evaluation
#### 2.2.3 User Perspective Product Attribute Evaluation  
In the product evaluation process, users make subjective judgements based on their experiences, perspectives, skills, and backgrounds, leading to ineffective data and inaccurate assessment information that cannot be precisely expressed in numerical terms. To reliably obtain evaluation data for different solutions and to quantify the vague judgements of each user and designer using rough numbers, this study employs intuitionistic fuzzy sets (IFSs) to support three states--support, oppose, and neutral--in evaluating conceptual design solutions. The intuitionistic fuzzy sets (IFSs) can objectively describe customer attitudes as natural attributes in the decision-making process. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Product**} & \multicolumn{4}{c}{**Principle Solutions**} \\ \cline{2-5}  & **1** & **2** & \(\dots\) & **k** \\ \hline \(Pa_{1}\) & \(PS_{11}\) & \(PS_{12}\) & \(\dots\) & \(PS_{1k}\) \\ \(Pa_{2}\) & \(PS_{21}\) & \(PS_{22}\) & \(\dots\) & \(PS_{2k}\) \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) \\ \(Pa_{m}\) & \(PS_{n1}\) & \(PS_{n2}\) & \(\dots\) & \(PS_{nk}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Morphological matrix for product conceptual design. When the product attribute \(X\) is a fixed set,  
\[IFS=\{x,[\mu_{A}(x),v_{A}(x)]|x\in X\} \tag{1}\]  
This function was initially proposed by Professor [PERSON] [38], where \(\mu_{A}(x)\in[0,1]\), \(v_{A}(x)\in[0,1]\), and \(0\leq\mu_{A}(x)+v_{A}(x)\leq 1\). Here, \(\mu_{A}(x)\) represents the user's satisfaction with the product attribute, \(v_{A}(x)\) represents the user's dissatisfaction with the product attribute, and \(\pi_{A}(x)\) represents the user's hesitancy about the judgement. The formula is characterized by the following expression:  
\[\pi_{A}=1-\mu_{A}-v_{A} \tag{2}\]  
Since the comments used in this paper are structured data and the amount of data is small, this paper uses a rule-based method for sentiment analysis. Compared with the machine learning model of sentiment analysis, the accuracy is higher, and the calculation is simpler. First, it locates the words related to product attributes in the sentences and identifies the presence of sentiment, degree, and negation words in the sentences. The quantification is detailed in Table 2.  
The total sentiment for product attributes in user comments can be calculated as follows:  
\[ \begin{split} sentiment\ total\ value=degree\ word\ polarity\ value\ \times\\ sentiment\ word\ polarity\ value\times negation\ word\ polarity\ value\end{split}  \tag{3}\]  
For example, the sentiment value of \"I am very satisfied with the appearance of my phone\" is \(1\times 1=1\). The sentiment value for \"I'm a little disappointed with plastic phone cases\" is \(0.5\times(-1)=-0.5\). The calculated total sentiment has a range of \(\{-1,\ -0.5,\ 0,\ 0.5,\ 1\}\). To better represent the uncertainty of users' feelings towards product attributes, following the transformation method proposed by scholar [PERSON] [33], the user sentiment \(V\) is represented using IFSs. Results of the transformation are shown in Table 3.  
Therefore, the overall user assessment value \(V(Pa_{i})\) for product attributes \(Pa_{i}\) is calculated by the following equation:  
\[V(Pa_{i})=\frac{1}{K}\sum_{k=1}^{K}V_{k} \tag{4}\] \begin{table}
\begin{tabular}{l l c} \hline \hline
**Word Category** & **Example** & **Polarity Value** \\ \hline \multirow{3}{*}{Sentiment} & Satisfied, Good, OK, Comfortable, Like... & \(1\) \\  & Dislike, Disappointed, Antipathy, Disgust.. & \(-1\) \\ \multirow{3}{*}{Degree} & Too, Very, Extremely, Most, Unexpectedly & \(1\) \\  & Somewhat, Comparatively, Relatively, Slightly, & \(0.5\) \\ \multirow{3}{*}{Negation} & Some, A Bit & \(-1\) \\  & Not, Unable to, Non & \(-1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative representation of word sentiment polarity.