The Media Spatial Diffusion Effect and Distribution Characteristics of AI in Education: An Empirical Analysis of Public Sentiments Across Provincial Regions in China

Abstract

With the rapid integration of artificial intelligence (AI) technologies in the field of education, public sentiment towards this development has gradually emerged as an important area of research. This study focuses on the sentiment analysis of online public opinions regarding the application of AI in education. Python was used to scrape relevant online comments from various provinces in China. Using the SnowNLP algorithm, sentiments were classified into three categories: positive, neutral, and negative. The study primarily analyzes the spatial distribution characteristics of positive and negative sentiments, with a visualization of the results through Geographic Information Systems (GIS). Additionally, Moran’s I and Getis-Ord Gi* are introduced to detect the spatial autocorrelation of sentiment attitudes. Furthermore, by constructing a multivariable geographical detector model and MGWR, the study explores the impact of factors such as the development of the digital economy, the construction of smart cities, local government policy attention, the digital literacy of local residents, and the level of education infrastructure on the distribution of sentiment attitudes. This research will reveal the regional disparities in AI and education-related online public sentiment and its driving mechanisms, providing data support and empirical references for optimizing the application of AI in education.