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- Title
A BERT Fine-tuning Model for Targeted Sentiment Analysis of Chinese Online Course Reviews.
- Authors
Zhang, Huibing; Dong, Junchao; Min, Liang; Bi, Peng
- Abstract
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%.
- Subjects
CHINA; SENTIMENT analysis; ONLINE education; CONSUMERS' reviews; RANDOM fields; USER-generated content
- Publication
International Journal on Artificial Intelligence Tools, 2020, Vol 29, Issue 7/8, p1
- ISSN
0218-2130
- Publication type
Article
- DOI
10.1142/S0218213020400187