摘要
随着互联网技术的飞速发展,互联网的访问量逐渐增大,由此形成了大规模的虚拟社交网络数据。巨量的网络数据中有效和无效的数据互相掺杂,在分析当前社交舆情时如何从中挖掘有效且有价值的数据和信息成为重点研究问题。运用社会网络分析的数据挖掘方法,探讨了社会网络分析方法在研究社交网络信息关联性和舆情分析上的可行性,并立足于当前实际,选择使用Python抓取网页信息,并利用权威网页和神经网络方法对社会网络数据进行分析预测。
With the rapid development of Internet technology and the increasing volume of Internet traffic,a large amount of virtual social network data has been generated.In the vast amount of network data,effective and ineffective data are mixed together.How to mine effective and valuable data and information from it when analyzing current social public opinion has become a key research issue.This paper discusses the feasibility of social network analysis methods in studying the correlation of social network information and public opinion analysis,and based on the current reality,it selects Python to scrape webpage information,and uses authoritative webpages and neural network methods to analyze and predict social network data.
作者
赵倩
Zhao Qian(Anhui Vocational College Police Officers,Hefei Anhui 23003)
出处
《安徽警官职业学院学报》
2024年第5期124-128,共5页
Journal of Anhui Vocational College of Police Officers
基金
2024年安徽省线下一流核心课程《计算机视觉》(2023hxkc088),2022年院级科研课题“基于视觉的网络舆情预警可视化分析研究”(2022zkxm002)
2022年院级教研课题“新工科背景下《计算机视觉》‘四维一体’教学模式探索与实践”(2022yjjyxm08)。
