基于峰值密度聚类的电信业投诉热点话题检测方法

江俊; 黄骅; 任条娟; 张登辉

doi:10.11959/j.issn.1000-0801.2019076

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基于峰值密度聚类的电信业投诉热点话题检测方法

研究与开发 | 更新时间：2024-06-05

- 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
- 电信科学 2019年35卷第5期页码：97-103
- 作者机构：
  
  1. 浙江树人大学信息科技学院，浙江杭州 310015
  2. 浙江大学信息与电子工程学院，浙江杭州 310058
  3. 东方通信股份有限公司，浙江杭州 310053
  4. 万向集团公司万向研究院，浙江杭州 311215
- 作者简介：
  
  [ "江俊（1983- ），男，博士，东方通信股份有限公司工作站及浙江大学流动站博士后，浙江树人大学讲师，主要研究方向为人工智能、数据科学与大数据处理、信息检索等。" ]
  [ "黄骅（1983- ），男，博士，浙江大学流动站博士后，主要研究方向为文本挖掘、信息检索和自然语言处理等。" ]
  [ "任条娟（1965- ），女，浙江树人大学信息科技学院院长、教授，中国电子学会理事，主要研究方向为无线传感网和车联网、数据科学与大数据处理等。" ]
  [ "张登辉（1970- ），男，浙江树人大学信息科技学院副院长、教授，主要研究方向为机器学习与社交计算、数据科学与大数据处理等。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2019076
  中图分类号： TP391
- 网络出版日期：2019-05，
  
  纸质出版日期：2019-05-20
- 稿件说明：
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江俊, 黄骅, 任条娟, 等. 基于峰值密度聚类的电信业投诉热点话题检测方法[J]. 电信科学, 2019,35(5):97-103.

Jun JIANG, Hua HUANG, Tiaojuan REN, et al. Telecom complaint hot topic detection method based on density peaks clustering[J]. Telecommunications science, 2019, 35(5): 97-103.
江俊, 黄骅, 任条娟, 等. 基于峰值密度聚类的电信业投诉热点话题检测方法[J]. 电信科学, 2019,35(5):97-103. DOI： 10.11959/j.issn.1000-0801.2019076.

Jun JIANG, Hua HUANG, Tiaojuan REN, et al. Telecom complaint hot topic detection method based on density peaks clustering[J]. Telecommunications science, 2019, 35(5): 97-103. DOI： 10.11959/j.issn.1000-0801.2019076.

摘要

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。

Abstract

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.

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Keywords

references

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