Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (19): 82-91.DOI: 10.3778/j.issn.1002-8331.2206-0167

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Research on Multi-Dimensional Evolution Model of Doctor-Patient Public Opinion Based on Deep Learning

TAN Xu, WU Pu, JIANG Zhiyi, ZOU Kai, LYU Xin   

  1. 1.School of Public Administration, Xiangtan University, Xiangtan, Hunan 411105, China
    2.COME Center, Shenzhen Institute of Information Technology, Shenzhen, Guangdong 518172, China
    3.School of System Engineering, National University of Defense Technology, Changsha 410076, China
  • Online:2023-10-01 Published:2023-10-01

基于深度学习的医患舆情多维演化仿真分析

谭旭,吴璞,蒋知义,邹凯,吕欣   

  1. 1.湘潭大学 公共管理学院,湖南 湘潭 411105
    2.深圳信息职业技术学院 素质赋能中心,广东 深圳 518172
    3.国防科技大学 系统工程学院,长沙 410076

Abstract: It can better guide the trend of network public opinion and improve the relationship between doctors and patients by analyzing the sentiment evolutionary trends of doctor-patient relationships in recent years and assisting the government in providing the theoretical basis for decision-making and intervention. In the large-scale public opinion text of doctors and patients in the complex context of the Internet, it has constructed a sentiment classifier in the downstream tasks of the pre-training model BERT. It is combined with LDA topic extraction technology for multi-dimensional emotional evolution analysis. Finally, the ARIMA model is combined to predict sentiment trends. The experimental results show that the sentiment prediction model accuracy based on LDA-BERT reaches 98%. The average prediction error of the ARIMA time series prediction of the doctor-patient public opinion model is less than 11.25%. It is demonstrated that the algorithm presented in this research can be effectively applied to the multi-dimensional monitoring and analysis of large-scale doctor-patient public opinion evolution.

Key words: doctor-patient public opinion, LDA-BERT model, ARIMA model, sentiment evolution

摘要: 为了探析医患关系近年来的情感演化过程,助力政府部门决策干预提供理论依据,从而更好引导网络舆论走向,促进和谐医患关系的构建。针对互联网复杂语境下大规模医患舆情文本数据,在BERT预训练模型下游任务中构建情感分类器,并与LDA主题抽取技术相结合进行多维情感演化仿真分析,最后结合ARIMA模型进行情感走势预测。通过实验分析表明,LDA-BERT医患舆情多维情感分析模型的情感预测准确度达到98%,ARIMA医患舆情时间序列预测模型的预测平均误差低于11.25%,证明其能够有效运用于大规模医患舆情演化的多维度监测与分析。

关键词: 医患舆情, LDA-BERT模型, ARIMA模型, 情感演化