## 5 Contributions  
As artificial intelligence (AI) technology continues to be applied in the education sector, understanding public perceptions and emotional responses has become increasingly important. This study addresses a gap in the literature by investigating the regional differences and influencing factors of sentiment attitudes towards AI in education. The data collected from major Chinese social media platforms over a year provides a deeperinsight into how user groups from different regions perceive this technology, particularly because most existing studies treat sentiment data as a whole, rather than broken down by region. Therefore, this study offers a unique opportunity to combine sentiment analysis with spatial analysis, enabling macro-regional monitoring and analysis, and offering a more comprehensive examination of user opinions across various social media platforms.  
The study highlights the significant moderating role of regional factors in sentiment attitudes, which has not been sufficiently discussed in existing research. Additionally, the study uncovers a new relationship between residents' income levels and sentiment attitudes, emphasizing the role of the socio-economic environment in shaping public perceptions of AI applications.  
In this study, the SnowNLP algorithm is first applied to classify online comments from various provinces in China into positive, neutral, and negative categories, providing foundational data for subsequent spatial analysis. GIS technology is then used to visualize the spatial distribution characteristics of positive sentiment, reflecting regional differences in public sentiment. Moran's I and Getis-Ord Gi* are employed to test the spatial autocorrelation of both positive and negative emotions, revealing patterns of emotional clustering. Furthermore, a multi-index spatial weighted regression model and geographical detector are used to systematically analyze the impact of factors such as digital economy development, urban vitality, policy attention, digital literacy, and infrastructure development on the distribution of positive sentiment, exploring their underlying driving mechanisms.  
This study employs two analytical methods--Geo-Detector and Multiscale Geographically Weighted Regression (MGWR)--to examine the influencing mechanisms of public sentiment towards AI. The Geo-Detector method quantifies the explanatory power of various factors (e.g., per capita disposable income, urbanization rate, and number of authorized patents) on sentiment attitudes by computing the q value, identifying economic development level and technological innovation capacity as the primary driving factors.  
The MGWR analysis not only validates the global effects of these variables based on the Geo-Detector results but also further identifies their local spatial influence and variation in intensity across regions. This approach provides a refined understanding of how different factors contribute to sentiment formation in diverse geographic contexts.  
From a theoretical perspective, the socio-technical systems theory posits that the introduction and effective operation of new technologies are not merely technical issues but also involve the coordinated optimization of human, organizational, and societal factors [53]. This study corroborates the existing theoretical perspective that technology, society, and individuals interact and mutually influence one another. Moreover, it reveals significant spatial heterogeneity in the effects of influencing variables across different regions, emphasizing the importance of spatial nonstationarity in sentiment analysis.  
Ultimately, the integration of these two methods provides mutual validation and effective complementarity, offering a multidimensional perspective from both impact intensity and spatial heterogeneity. This joint approach enhances the understanding of the spatial distribution patterns and formation mechanisms of public sentiment towards AI at the provincial level, providing robust empirical support for further research and policymaking.