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A BERT Fine-tuning Model for Targeted Sentiment Analysis of Chinese Online Course Reviews
International Journal on Artificial Intelligence Tools ( IF 1.0 ) Pub Date : 2020-11-30 , DOI: 10.1142/s0218213020400187 Huibing Zhang 1 , Junchao Dong 1 , Liang Min 2 , Peng Bi 2
International Journal on Artificial Intelligence Tools ( IF 1.0 ) Pub Date : 2020-11-30 , DOI: 10.1142/s0218213020400187 Huibing Zhang 1 , Junchao Dong 1 , Liang Min 2 , Peng Bi 2
Affiliation
Accurate analysis of targeted sentiment in online course reviews helps in understanding emotional changes of learners and improving the course quality. In this paper, we propose a fine-tuned bidirectional encoder representation from transformers (BERT) model for targeted sentiment analysis of course reviews. Specifically, it consists of two parts: binding corporate rules — conditional random field (BCR-CRF) target extraction model and a binding corporate rules — double attention (BCR-DA) target sentiment analysis model. Firstly, based on a large-scale Chinese review corpus, intra-domain unsupervised training of a BERT pre-trained model (BCR) is performed. Then, a Conditional Random Field (CRF) layer is introduced to add grammatical constraints to the output sequence of the semantic representation layer in the BCR model. Finally, a BCR-DA model containing double attention layers is constructed to express the sentiment polarity of the course review targets in a classified manner. Experiments are performed on Chinese online course review datasets of China MOOC. The experimental results show that the F1 score of the BCR-CRF model reaches above 92%, and the accuracy of the BCR-DA model reaches above 72%.
更新日期：2020-11-30
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