Identification of false comments on online shopping products based on ETW-BERT model
A false comment recognition method based on BERT bidirectional pre-training fine-tuning model is proposed to ad-dress the issue of false comment recognition for online shopping products.Analyze the text,emotional,and temporal characteristics of comments,propose 12 rules for manually annotating comment data.Manually annotating Chinese comments on some electronic products crawled from the JD online shopping platform,obtaining 5190 annotation data.The weight covariance alignment algorithm is added to the fine-tuning process of BERT to obtain the model W-BERT.The emotional estimation and temporal features are em-bedded to obtain the model ET-BERT,and the two are fused to obtain the model ETW-BERT.The experiments on the annotated da-taset mentioned above indicate that all three improved models have achieved better results than the BERT basic model.
false commentsBERTpre-trained large modelsemotional estimationtemporal features
NETL
NSTL
万方数据
