Analysis of the influencing factors of online classes satisfaction based on text mining Take MOOC platform art education online classes as an example

Abstract

The normalization of the epidemic and the convenience of online classes have led to the rapid rise of online education courses. The MOOC teaching platform represents the online course field and is popular in various disciplines. Unlike disciplines such as natural science and engineering technology, art education courses require real-time emotional expression and transmission. However, the existing technical framework is difficult to support the interactive needs of art teaching mode, and the learning effect of art students is not ideal, and the user The learning effect of art students is not ideal, and the phenomenon of low user satisfaction and low willingness to continue learning is more serious. Based on the above background, this paper uses Python to crawl the review texts of art online courses and summarizes the factors influencing students' satisfaction with art education online courses through sentiment analysis and LDA model to provide valuable references for the quality of art online education.