基于文本挖掘的网络舆情主题发现与情感分析

Topic Discovery and Sentiment Analysis of Internet Public Opinion Based on Multi Source Data Mining

随着网络的飞速发展,社交网络成为网络舆情传播的主要平台,用户可以随时随地通过文本、图片、视频等方式表达对热点事件的看法。分析用户对突发事件的情感态度,可以发现舆情演变规律和潜在风险,为舆情的引导和控制提供决策支持。利用python对爬取的文本数据进行数据预处理,从多语言多源数据的角度出发,挖掘网络舆情中高价值的舆情主题,并利用数据可视化方法来研究网民的情感倾向,分析舆情的时空演化规律。研究结果能够清晰的表明网民的情感态度和舆情每个阶段的特征。

With the rapid development of the network,social network has become the main platform for the dissemination of network public opinion.Users can express their views on hot events through text,pictures,videos and other means anytime and anywhere.By analyzing users'emotional attitude towards emergencies,we can find the evolution rules and potential risks of public opinion,and provide decision support for the guidance and control of public opinion.This paper uses Python to preprocess the crawled text data.From the perspective of multi language and multi-source data,it mines high-value public opinion topics in network public opinion,and uses data visualization method to study the emotional tendency of Internet users and analyze the temporal and spatial evolution of public opinion.The results can clearly show the characteristics of netizens'emotional attitude and public opinion in each stage.