基于峰值密度聚类的电信业投诉热点话题检测方法

Telecom complaint hot topic detection method based on density peaks clustering

针对电信业对投诉热点话题缺乏有效的检测方法问题,提出一种基于峰值密度聚类算法的投诉热点话题检测方法。首先建立电信业专用词库用于投诉样本的文本分词,采用向量空间模型表示文本分词,然后通过计算文本分词相似度和密度,并运用密度峰值聚类算法对分词进行聚类分析。最终通过类簇关键词选取并排序,从而得到热点话题描述。将本方法应用到某电信企业投诉热点话题检测中,结果表明本方法有效并具有实际应用价值。

In view of the lack of effective detection methods for hot topics in telecom industry,a method of complaint hotspots detection based on density peaks clustering algorithm was proposed.Firstly,a special vocabulary for telecommunication industry was established to segment the complaint samples.The vector space model was used to represent the text segmentation.Then,the similarity and density of the text segmentation were calculated,and the clustering analysis of the words was carried out by using the density peaks clustering algorithm.Finally,keywords were selected and sorted by clustering.This method was applied to the complaint hotspots detection tasks within a telecom company.The results show that this method is effective and has practical application value.