A false emotion opinion target extraction model with two stage BERT and background information fusion

Abstract

As social media has gradually become an indispensable part of people’s life, more and more users begin to express their opinions on social media. These opinions contain rich emotional information, as well as many abnormal false emotion. And the opinion target rich in false emotion will have great potential value in the field of movie box office prediction, public opinion guidance and so on. Therefore, we propose a false emotion opinion target extraction task and two stage BERT+CRF model based on the concept of false emotion. Firstly, two stage BERT network includes C-BERT and W-BERT. We use C-BERT to learn the users comment feature, and W-BERT to learn the background content features of microblog. Then, we design five feature interaction fusion methods to fuse the features learned by C-BERT and W-BERT. At last, we transform the false emotion opinion target extraction task into a sequence labeling task, and use the CRF method to learn the label dependency of the context and output the optimal sequence among all candidate sequences. Due to the lack of false emotion opinion target datasets, we collate a false emotion opinion target dataset Weibo23. Comparing with the advanced models used in related research, including some sequence labeling models and named entity recognition models, two stage BERT+CRF model achieves an F1 score of 0.8834, which is about 10%–20% higher.