Aspect-Level Chinese Teaching Comments Unsupervised Sentiment Analysis Framework Based on MacBERT
An aspect-level unsupervised sentiment analysis framework for Chinese teaching comments based on the pre-trained language model MacBERT is proposed for sentiment analysis.Firstly,for each teaching aspect and emotional polarity,a semantically consistent category vocabulary table is constructed through pre-trained language models.Then,part of the comment sentences in the training corpus is annotated using the constructed vocabulary and part-of-speech tags and based on an overlap rate matrix.Finally,a neural network is constructed using anno-tated comment sentences to extract joint hidden features of aspect and emotion from the test data through MacBERT,and accurate aspect extraction and emotion classification are achieved using a noise-resistant loss func-tion.The experimental results show that the macro F1 values of the model in aspect extraction and sentiment classi-fication tasks are 78.10%and 89.20%respectively,indicating that the model can accurately extract aspect catego-ries from student teaching feedback and determine the emotional polarity of each comment sentence.
aspect-level sentiment analysisdeep learningMacBERTpre-trained language modeloverlapping Matrix
国家科技期刊平台
NSTL
万方数据
