## 4 Research Results
### Proportion Analysis of Sentiment Across Topics  
Six themes were first extracted from the comments by LDA modeling, and then SnowNLP was used to analyze the sentiment distribution of each theme (see Figure 8 and Table 3). The public reaction to artificial intelligence in the field of education demonstrates a complex emotional distribution. Most topics exhibit a positive sentiment tendency, with Topic 3 showing the highest number of positive sentiments. This suggests that Topic 3 likely involves advancements or innovations in artificial intelligence or education, eliciting a positive attitude from the public. On the other hand, Topic 5 displays a higher level of negative sentiment, particularly the largest number of negative sentiments among all topics. This could be attributed to challenges or controversies related to artificial intelligence and education, such as privacy concerns, inequality in education, and other issues, which evoke strong negative reactions from the public. Additionally, several topics exhibit a certain proportion of neutral sentiments, particularly Topic 2 and Topic 4, indicating that the public holds a more rational perspective or has yet to form a clear opinion on these topics. Overall, the discussions on artificial intelligence and education reflect a coexistence of optimism about technological innovation and concerns about its applications. These concerns are especially prominent regarding the potential impacts of technology on educational equity, privacy protection, and job displacement. Future research could further investigate the root causes of these negative sentiments and explore ways to improve the application of educational technology through policies and practices, thereby reducing public negative reactions and enhancing acceptance. When analyzing the word clouds of the six topics in Figure 9, it is essential to highlight how AI is reshaping human learning and educational environments in unprecedented ways. With the widespread application of advanced AI models such as ChatGPT, artificial intelligence has transitioned from theory to practice, becoming a pivotal tool in education, as detailed below:  
The co-occurrence of \"human\" and \"AI\" in Topic 1 underscores the core of this transformation: the deep integration of humans and intelligent technologies. In educational contexts, terms like \"robot\" and \"artificial intelligence\" highlight their emergence as innovative teaching aids, gradually transforming traditional teaching methods to provide students with more personalized and efficient learning experiences [59; 60]. Words like \"model\" and \"tool\" emphasize specific AI applications in education, such as intelligent recommendation systems and online learning platforms, which leverage data analysis and machine learning to deliver tailored learning pathways and resources. However, terms such as \"pressure\" and \"data\" reveal challenges in this transformation. While AI-driven education relies on data collection and analysis, it also raises concerns about data security and privacy. Additionally, both teachers and students face psychological and learning pressures in adapting to these new teaching approaches. On a positive note, terms like \"development\" and \"era\" convey an optimistic attitude, suggesting that despite challenges, AI offers immense potential for educational progress. With continuous learning and adaptation, humans can fully harness AI's advantages to drive innovation in education. Topic 2 focuses on children, particularly primary school students, who are the future backbone of society. The term \"children\" highlights the critical importance of their growth and educational environment. Words like \"sense of crisis\" and \"feeling\" reflect society's perception of uncertainty and challenges brought about by rapid AI development.