摘要
网络舆情的迅速发展使舆情发展成为研究热点,它对舆情的预测预警具有重要意义。从文本聚类入手,针对舆情的演化分析过程,在时间序列上进行K-means聚类研究,得到聚类中心,又依此对聚类中的词频统计进行时序加权处理,使统计所得关键词更具有代表性。通过对时间聚类法和时序加权统计法所得关键词的分析,得到了舆情演化的趋势。研究结果表明该方法降低了聚类的维数,减少了噪声,提高了聚类的准确度,增强了演化分析的可信度。
The rapid development of network public opinion makes the evolution of public opinion become the research hotspot,which is of great significance for the forecast of public opinion.In this paper,we started from text clustering,for the evolution of public opinion analysis process,making the K-means clustering research in time series,and got clustering center.The time-weighted weighting of word frequency statistics in clustering was made,which makes the statistical keywords more representative.Through the analysis of the keywords obtained by time clustering and time series weighted statistical method,the trend of public opinion evolution was got.The results show that the method reduces the dimension of clustering and the noise,improves the accuracy of clustering,and enhances the reliability of evolution analysis.
出处
《计算机科学》
CSCD
北大核心
2017年第B11期418-421,共4页
Computer Science
关键词
网络舆情
时间聚类
加权
关键词
演化分析
Network public opinion
Time clustering
Weighting
Keywords
Evolutionary analysis
