基于舆情分析视角的网络借贷问题平台甄别模型研究  

Identification Model for Online Lending Problem Platforms Based on Public Opinion Analysis

作　　者：涂艳[1] 刘蕊 TU Yan;LIU Rui(School of Information, Central University of Finance and Economics, Beijing 100081, China;Bank Card Center, Bank of China, Beijing 100032, China)

机构地区：[1]中央财经大学信息学院,北京100081 [2]中国银行银行卡中心,北京100032

出　　处：《北京邮电大学学报（社会科学版）》2021年第3期1-12,34,共13页Journal of Beijing University of Posts and Telecommunications(Social Sciences Edition)

基　　金：国家社会科学基金资助项目(16BXW045)。

摘　　要：从舆情分析视角,将多维度舆情信息纳入网络借贷平台风险分析范畴,针对不同类型的舆情信息采用不同文本处理方式,基于实验研究并结合内部平台基础信息指标与外部舆情信息指标,采用神经网络、支持向量机、随机森林和逻辑回归方法构建网络借贷问题平台甄别模型,验证舆情信息指标对甄别模型的性能提升作用。实验结果表明:第一,综合采用平台基础信息、运营信息和舆情信息进行网络借贷问题平台甄别,准确率更高;第二,相较于随机森林模型、支持向量机模型、逻辑回归模型而言,神经网络模型的甄别效果最佳。本研究有助于金融监管部门科学全面地了解网络借贷平台运营状况,并有针对性地开展精准化监管治理工作。From the perspective of public opinion analysis,an online lending problem platform identification model is built based on experimental research,combining internal platform information and external public opinion information.The model is constructed by using neural network,support vector machine,random forest and logistic regression in order to verify the role of public opinion information indicators in improving the performance of the identification model.The results show that,firstly,the accuracy of comprehensively using fundamental information,operation information and public opinion information to identify the online lending problem platform is higher;secondly,compared with the random forest model,the support vector machine model and the logistic regression model,the identification effect of the neural network model is the best.Therefore,the study is helpful for regulators to understand the platform's operating status and to carry out targeted regulatory governance for different platforms scientifically and comprehensively.

关 键 词：网络借贷 网络舆情 主题提取 机器学习 

分 类 号：F831.2[经济管理—金融学]