## 4 Empirical Study
### Analysis and Results
#### 4.2.4 Phase Four: Sentiment Polarity Association Mining via Co-Occurrence Network Analysis (CONA)  
The objective of this research phase was to explore the associations between sentiment-tendency keywords of the target competitors identified in the previous stage using CONA. This analysis helped to uncover how affective polarity terms interact with other aspects of UX, thereby identifying key elements and potential factors of affective evaluations.  
Step 1. Sentiment-tendency text screening and data preprocessing: based on the DASA results of keywords in each theme, we selected texts with strong positive (\(\geq\)0.9) and very negative (<0.1) sentiments from the target competitors for corpus mining and preprocessing the data.  
Step 2. Construction of co-occurrence matrix: for each preprocessed comment, we set criteria for the top 100 noun frequency and technical terms, with a window size encompassing the entire document, thereby considering the co-occurrence of keywords throughout the text to construct a co-occurrence matrix based on frequency.  
Step 3. Calculation of network characteristics and comprehensive weights: by calculating EC, JC, and TF, the nodes and interconnections of the co-occurrence network were established.  
Step 4. Calculation of comprehensive weights: Normalization of network characteristic values was followed by the computation of comprehensive weights for each keyword. EC, JC, and TF were allocated weights of 0.5, 0.3, and 0.2, respectively. The results for the keyword \"interior\", indicative of \"pain points\" due to very negative sentiment extremity within TC1, are outlined in Appendix D, Table A6. On the other hand, Table A7 presents the results for the keyword \"interior\" in TC2, which demonstrated strong positive sentiment extremity and served as a reference for \"best practices\". Following the computation of weights for all keywords across various themes of the target competitors, we derived the association factors and weight coefficients. As an example, within the theme of SP, the factor calculation results for the keyword \"interior\" with very negative and strong positive sentiment extremities for all competitors are illustrated in Appendix D, Tables A8 and A9, respectively.  
Results of phase four--visualization and interpretation of co-occurrence networks:  
1. Taking the keyword \"interior\" as an example, the co-occurrence networks of TC1 and TC2 for the \"interior\" were presented as typical results. The depth of the node color indicates the EC of the keywords, where darker blue signifies higher centrality, representing strongly positive evaluations, and yellow indicates lower centrality, suggesting more peripheral terms. Orange and purple represent strongly negative evaluations. The connections and numerical values between nodes denote the strength of the associations.
2. As represented in Figure 4, in the realm of very negative interior experience, TC1's issues were primarily concentrated on poor material quality and large gaps. For  
Figure 3: Sentiment score comparison for the “interior” keyword in subjective perception.  
TC2, the focus was on the red interior decoration and the chrome high-gloss trim, which were significantly influenced by individual preferences. TC3 did not exhibit any notably related results. In contrast, TC4's concerns were centered on the design of the dashboard and the use of carbon fiber shells and suede decorations.
3. As represented in Figure 5, on the other hand, regarding the strongly positive interior experience, TC1 received higher evaluations for its sense of technology and brand-representing design and style. TC2, however, garnered attention for the quality of its leather interior, the texture of its seats, and the masterful design.  
Figure 4: Co-occurrence network of keyword eigenvector centrality for the extremely negative “interior” label.  
Figure 5: Co-occurrence network of keyword eigenvector centrality for the strongly positive “interior” label.