摘要
针对传统的网络舆情监管预测算法对大广度、强干扰的网络舆情数据预测性能差的缺点,在深入研究现有网络舆情监管预测算法基础上提出一种基于大数据语义特征分析的网络舆情监管预测算法。该算法采用二元语义对网络舆情特征进行拟合,构建与匹配网络舆情关键词,构建时间序列模型,分析与提取语义特征,从而实现大数据分析法对网络舆情的监管预测。最后利用仿真实验对该算法进行验证,其结果表明,该算法预测精度高、实时性强,对提高网络舆情的监管能力具有重要意义。
In allusion to the problem that the traditional network public opinion monitoring and prediction algorithm has poor prediction performance for large amount of network public opinion data with strong interference, a network public opinion monitoring and prediction algorithm based on semantic feature analysis of big data is proposed after the in-depth study on the current network public opinion monitoring and prediction algorithm. In the algorithm, the two-tuple semantics is used to fit the features of network public opinions, construct and match the keywords of network public opinions, construct the time series model, and analyze and extract semantic features, so as to realize the monitoring and prediction of network public opinions by using the big data analysis method. The simulation experiment was carried out to verify the algorithm. The results show that the algorithm has high prediction precision and strong real-time performance, which is of great significance for improving the network public opinion monitoring capability.
出处
《现代电子技术》
北大核心
2017年第24期28-30,共3页
Modern Electronics Technique
基金
江苏省高校党建规划项目(16DJYB079)
关键词
大数据
网络舆情
特征提取
舆情监管
big data
network public opinion
feature extraction
public opinion monitoring
