计算机技术与发展2024，Vol.34Issue(5)：157-162,6.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0054
FinBERT-RCNN-ATTACK:金融文本情感分析模型
FinBERT-RCNN-ATTACK:Emotional Analysis Model of Financial Text
摘要
Abstract
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%.关键词
金融文本/情感分析/FinBERT/循环卷积神经网络/对抗训练Key words
financial text/emotional analysis/FinBERT/recurrent convolutional neural networks/adversarial training分类
信息技术与安全科学引用本文复制引用
段魏诚,薛涛..FinBERT-RCNN-ATTACK:金融文本情感分析模型[J].计算机技术与发展,2024,34(5):157-162,6.基金项目
陕西省技术创新引导专项计划资助项目(2020CGXNG-012) （2020CGXNG-012）
