## 3 Research Methods, Research Platform, and Data Sources
### Data Crawling and Cleaning  
This study utilized Python programming tools and the requests module to collect comment data from platforms such as Weibo, Bilibili, and Douyin. Approximately 40,325 comments were collected using Python web crawling programs on 30 September 2024. Keywords used in the search included \"artificial intelligence\", \"ChatGPT\", \"Sora\", or \"machine learning\"  
Figure 4: TOE theoretical framework. in combination with \"education\". The collected data included user IDs, cities, comment content, and timestamps. Table 2 provides several typical examples with distinct emotional features. During the initial data collection phase, it was observed that most of the data were unstructured and varied in format. To ensure data completeness and accuracy, empty values, duplicate records, advertisements, and other irrelevant information were manually removed, resulting in a final dataset of 29,135 valid entries. To improve the precision and reliability of subsequent analysis, the Jieba tokenizer was employed for meticulous text segmentation, while English letters, numbers, and other meaningless words were excluded [56]. According to the syntax rules of Chinese, the operation of dividing comment texts into independent words is referred to as segmentation. When filtering textual elements, the quality of segmentation directly affects the accuracy of emotional scoring for words, thereby significantly influencing the validity of the analytical conclusions [57]. Furthermore, the textual data from Weibo, Bilibili, and Duoyin are characterized by their brevity, high degree of colloquialism, non-critical linguistic structures, and frequent use of internet slang. These features necessitate sentiment analysis methods distinct from traditional text sentiment analysis approaches [58]. Additionally, the diversity and complexity of Chinese expressions add further challenges and difficulties to the sentiment analysis of Weibo data. The sentiment knowledge network dictionary used in this study is structured into six subcategories: \"Positive Emotions\", \"Negative Emotions\", \"Positive Evaluations\", \"Negative Evaluations\", \"Academic Levels\", and \"Needs Categories\", encompassing a total of 17,877 lexical entries. To enhance the precision and practicality of lexical filtering, low-frequency words were excluded during the initial stage. Subsequently, with the help of regular expression techniques, user comment content was matched against the sentiment dictionary to identify and retain emotional terms, resulting in a total of 1833 valid words. Among these, 20 positive sentiment words (e.g., \"amazing\", \"perfect, \"exciting\") and 20 negative sentiment words (e.g., \"rigid\", \"harsh\", \"terrible\") that appeared more than 100 times were selected. Using \"stiff\", \"disappointed\", and \"regret\" as benchmark terms, the semantic orientation pointwise mutual information (SO-PMI) method was employed to calculate the pointwise mutual information values between these 1833 emotional terms and each benchmark term. Based on the obtained SO-PMI values, the emotional tendencies of the sentiment terms were categorized, resulting in the construction of a domain-specific sentiment lexicon.