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