Research on Sentiment Analysis Model for Online Course Comments Based on R-Boson
With the popularization of online education platforms,online course review texts containing rich emotional information continue to emerge,which is of great significance for optimizing online education platforms and improving teaching effectiveness.Therefore,a sentiment analysis model for online course comments based on R-Boson sentiment dictionary is constructed.Firstly,it crawls course comments from bilibili and uses techniques such as jieba for data preprocessing.Secondly,it establishes a dictionary of negative words and degree adverbs in the field of education based on the characteristics of comments.Finally,it uses the R-Boson sentiment analysis model to calculate the sentiment tendency of comments.The results show that compared with the basic Boson dictionary,the R-Boson model with negative words and degree adverbs improves its performance.Its F1 value increases from 93%to 95%,the negative recall rate increases from 54%to 79%,and the negative accuracy rate increases from 76%to 87%.At the same time,the F1 value of the model gradually increases from 89%to 95%in increasing data size.
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