Influencing Factors For Learners’ Willingness To Give Negative Comments Based On Social Network

Extracting the correlations between online course quality elements and measurement elements from learners’ negative comments are important to effectively guide the quality iteration and learners’ satisfaction improvement of online courses. This study uses web crawler technology to collect learning comments about programming courses on Chinese university MOOC platforms, calculates sentiment polarity of online comments based on natural language processing technology of Baidu AI open platform, uses LDA topic model to extract quality factors from negative comment texts, and subsequently introduces social network analysis methods to construct thematic social networks. The results show that the nodes of "course explanation" and "knowledge difficulty" have high degree centrality, proximity centrality and intermediary centrality; The performance of the other elements in the central indicators is different.