Internet Popular Topics Extraction of Traffic Content Words Correlation

周亚东 1; 孙钦东 1; 5; 管晓宏 1; 3; 4; 李卫 1; 2; 陶敬 1; 2

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Internet Popular Topics Extraction of Traffic Content Words Correlation

更新时间：2025-12-08

- Internet Popular Topics Extraction of Traffic Content Words Correlation
- Vol. 41, Issue 10, Pages: 1142-1145+1150(2007)
- 作者机构：
  
  1. 西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
  2. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393.4
- Online First：10 October 2007，
  
  Published：2007
- 稿件说明：
移动端阅览
周亚东 1, 孙钦东 1, 5, et al. Internet Popular Topics Extraction of Traffic Content Words Correlation[J]. 2007, 41(10): 1142-1145+1150.
DOI：

周亚东 1, 孙钦东 1, 5, et al. Internet Popular Topics Extraction of Traffic Content Words Correlation[J]. 2007, 41(10): 1142-1145+1150. DOI：

摘要

针对网络舆情分析的需求

给出了网络热点话题定义及其形式化描述

分析了流量内容中热点词语与热点话题的关系

提出了流量内容中热点词语的相关度计算算法.在此基础上

采用基于高密度连接区域的密度聚类方法得到热点词语簇

结合热点词语簇相关的网页标题及网站地址信息

得出网络热点话题的属性描述.实验结果表明

该算法能够有效获取当前网络中的热点话题

话题提取有效率达到16.7%

为网络热点话题传播特性研究提供了基础.与Web挖掘、话题监测与跟踪方法相比

所提算法通过选取合适的数据源

能更大程度地还原网络用户行为

从而得到了更为准确的网络信息传播状况.

Abstract

Aiming at the requirements of network public feeling analysis

the formal definition and description of the popular topic on Internet is presented

the relationship between hot words and popular topics is analyzed

and finally a hotpoint words correlation computing approach for extracting popular topics on Internet is introduced in traffic contents. Based on that

DBSCAN(Density-Based Spatical Clustering of Application with Noise)clustering algorithm is adopted to extract popular topics and formalized results are given. The test results show that this method has an availability of 16.7% in extracting Internet popular topics

which

compared to web mining and TDT(Topic Detection and Tracking)

can provide a more suitable data source for effective recovery of Internet public opinions.

关键词

Keywords

references

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