基于情感分析的在线课程质量评价体系应用研究

Research on application of sentiment analysis in online course quality evaluation system

文章旨在探讨基于情感分析的在线课程质量评价体系的应用,并深入研究该体系在提升在线课程质量方面的效果。首先,构建了多维度的在线课程质量评价指标体系,为在线课程质量评价提供了更全面、客观的依据。其次,采用深度学习的StructBERT模型对学生在学习过程中产生的大量课程评论数据进行情感倾向分析,该模型的准确率相较主流机器学习模型有所提升。最后,通过实证应用,验证了该评价体系有效性和实用性。

The article aims to explore the application of an online course quality evaluation system based on sentiment analysis,and conduct in-depth research on the effectiveness of this system in improving the quality of online courses.Firstly,a multi-dimensional online course quality evaluation index system has been constructed,providing a more comprehensive and objective basis for online course quality evaluation.Secondly,using the StructBERT model of deep learning to analyze the emotional tendencies of a large amount of course review data generated by students during the learning process,the accuracy of this model has been improved compared to mainstream machine learning models.Finally,the effectiveness and practicality of the constructed evaluation system were verified through empirical application.