FinBERT-RCNN-ATTACK:Emotional Analysis Model of Financial Text
The financial text contains investor sentiment and public attitudes towards the events.In recent years,natural language processing has been widely used in financial field,and the emotional analysis of financial text data can get rich investment value and regu-latory reference value.However,due to the professionalism and particularity of financial vocabulary,the existing general emotional analysis model is not suitable for the emotional analysis task in the financial field,and the accuracy needs to be improved,and the existing model is vulnerable to the interference of antagonistic samples,leading to the wrong model results.In order to solve these problems,we proposed a FinBERT-RCNN-ATTACK model.The FinBERT model pre-trained in the financial corpus is used for word embedding processing to extract semantic features,and the extracted features are introduced into the RCNN model to further excavate the key features of the context.In addition,adversarial training is introduced into the model,that is,disturbance is added in the embedding stage to improve the robustness and generalization of the model.The experimental results show that the proposed model is better than the other e-motional analysis models,and the accuracy is improved by 3%～35%.
financial textemotional analysisFinBERTrecurrent convolutional neural networksadversarial training
国家科技期刊平台
NSTL
万方数据
