提示学习驱动的新闻舆情风险识别方法研究OACSTPCD

Identifying a company's risks from news reports can quickly locate the risk categories involved in the company,so as to help enterprises to take response measures timely.Generally speaking,news public opinion risk identification is a multi-classification task of risk labels.The deep learning method represented by BERT uses the mode of pre-training + fine-tuning,which is prominent in text classification tasks.However,there is little labeled data in the field of news and public opinion,which constitutes a small-sample machine learning problem.The new paradigm represented by prompt learning provides a new way and means to improve the performance of small sample classification,and existing studies have shown that this paradigm is superior to the pre-training + fine-tuning method in many tasks.Inspired by the existing research work,this paper proposes a news public opinion risk identification method based on prompt learning,designs a news public opinion risk prompt template based on the idea of prompt learning on the basis of the BERT pre-training model,and after training by the MLM(masked language model)model,the predicted label is mapped to the existing risk label through answer engineering.The experimental results show that the training method of prompt learning is better than the training method of fine-tuning on different numbers of small samples of the news public opinion datasets.