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作 者:夏一雪[1] 袁野[1] 张文才[2] 兰月新[1] Xia Yixue;Yuan Ye;Zhang Wencai;Lan Yuexin(The Chinese People's Armed Police Force Academy, Langfang 065000, China;Information Technology, Jilin Provincial Public Security Traffic Police Corps, Changchun 130022, China)
机构地区:[1]中国人民武装警察部队学院,河北廊坊065000 [2]吉林省公安厅交警总队信息技术处,吉林长春130022
出 处:《现代情报》2018年第6期80-85,共6页Journal of Modern Information
基 金:河北省科技计划项目"大数据环境下涉警舆情风险建模与预警机制研究"(项目编号:16455602);国家社会科学基金青年项目"公共安全视角下网络舆情风险建模与对策研究"(项目编号:15CXW015);全国统计科学研究重点项目"舆情大数据环境下突发事件民意监测与评估研究"(项目编号:2017LZ37);河北省统计科研计划重点项目"大数据环境下网络舆情数据分析与决策支持研究"(项目编号:2016HZ09);河北省高等教育教学改革研究与事件项目"面向大数据的虚拟情报作战室构建与教学实践研究"(项目编号:2016GJJG239)
摘 要:[目的/意义]通过对网络舆情数据的动态监测和异常感知,及时预警舆情异常,为政府掌握舆情决策的先动优势提供理论模型和可行思路。[方法/过程]分析大数据环境下激增、波动等网络舆情数据异常现象,明确舆情趋势预测、动态感知异常等异常数据监测机理。基于此,首先运用Gompertz模型进行舆情趋势区间预测,其次定义偏离度进行数据异常评级,并确定预警等级,实现异常数据的及时捕捉和快速预警。[结论/结果]通过实例验证,证明了模型可行性,可以为政府舆情引导程度提供度量依据,也为编制智能化的舆情监测软件提供算法支持。[Purpose/Significance] Through dynamic monitoring and abnormal perception of network public opinion data,it made early warning of abnormal network public opinion and provided theoretical models and practicable methods for grasping the preemptive advantage of network public opinion decision. [Method/Process] It analysed the abnormal phenomena of network public opinion data such as skyrocketing data and trend fluctuation under big data environment,made clear the monitoring mechanism of abnormal data,such as trend prediction,dynamic perception of abnormal data,etc. On this basis,the first step was using Gompertz model to predict the trend of network public opinion theoretical interval. Secondly,the deviation degree was defined for data anomaly rating,and the early warning level was determined to realize the timely capture and rapid early warning of abnormal data. [Result/Conclusion] The feasibility of the model was proved by an example,which could provide the measurement basis for the guidance degree of network public opinion,and also provided the algorithm support for compiling intelligent monitoring software of network public opinion.
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