Emotion Classification of Course Reviews Based on BiLSTM Algorithm and its Underlying Causes
This study examines the sentiment of students'comments on an online education platform and categorizes them into three categories:"Positive,""Negative,"and"Neutral."It does this by building a sentiment classification model of Chinese comments based on the BiLSTM algorithm.Word vectors combined with the Bert pre-training model were trained using the review data of the NetEase cloud course platform.The model is optimized by combining resampling technology with the SVM classification principle.The optimized model performs exceptionally well in terms of precision,accuracy,recall rate,and F1 value,according to the experimental data.The emotional origins of remarks are represented using hierarchical clustering and semantic network analysis,offering a rationale for curriculum development based on science.