## 4 Discussion
### Factors Influencing of Sentiment Attitudes
#### 4.3.2 Discussion Based on the MGWR  
From the perspective of spatial heterogeneity in the MGWR model analysis, the driving mechanisms of positive and negative emotions exhibit significant differences. In the positive emotion model, except for technology transfer amount (F6), which shows local variation (bandwidth = 712 km, ENP = 4.059), other variables such as urbanization rate (F2), per capita disposable income (F7), and revenue from technology and software services (F8) have globally consistent effects on positive emotions. In contrast, in the negative emotion model, the bandwidth of technology transfer amount (F6) slightly increases to 729 km, while the bandwidth of mobile internet users (F5) is 4417 km, indicating a certain degree of local variation. This difference suggests that the formation of negative emotions is more susceptible to region-specific factors, such as the localized impact of technology transfer or the uneven distribution of internet access. Urbanization rate (F2) and technology transfer amount (F6) play significant roles in fostering positive emotions, while their effects diminish in the negative emotion model. This finding suggests that highly urbanized regions with higher levels of technology transfer, driven by two major socioeconomic transformation forces--technological innovation and urbanization--are more inclined to promote the widespread adoption of AI in education through positive effects such as improved infrastructure, increased employment opportunities, and enhanced convenience.  
Notably, revenue from technology and software services (F8) emerges as the strongest positive driving factor in both emotion models, whereas per capita disposable income (F7) serves as a strong negative factor. This finding highlights the dual influence of economic development on emotional attitudes. The high positive effect of F8 suggests that the growth of technology and software service revenue not only reflects the modernization of regional economic structures but may also directly enhance public positive emotional experiencesby increasing employment opportunities, improving living convenience, and fostering social recognition. Its global bandwidth (>9975 km) further indicates the widespread and consistent nature of this effect. The stable and significant role of F8 in both emotion models suggests that the prosperity of the technology industry can both reinforce public recognition of the benefits of AI applications in education and, to some extent, raise concerns about its potential risks.  
The strong negative effect of F7 may reflect a form of \"relative deprivation\" or increased life pressure associated with economic growth. In particular, at the spatial scale, rising regional per capita income is often accompanied by higher living costs and intensified competition, with this negative association being more pronounced in the negative emotion model (\(\beta=-0.617\)). This suggests that the benefits of economic development have not been uniformly translated into emotional well-being. Additionally, population density (F4) has no significant impact on positive emotions but shows a strong positive effect on negative emotions (\(\beta=0.508\), \(p=0.002\)), implying that high-density areas may be more prone to technology-related anxiety due to resource competition and disparities in technology accessibility.