基于文本挖掘技术的网络舆情采集与分析应用研究

Research on the Application of Network Public Opinion Collection and Analysis Based on Text Mining Technology

该文就基于文本挖掘技术的网络舆情采集与分析应用展开研究,从网络舆情生命周期理论、网络舆情采集与分析相关技术、中文分词算法、文本挖掘技术、以及具体的文本预处理、词频分析和LDA主题建模等方面展开讨论。在网络舆情数据预处理中,通过去噪、自定义词典及分词、停用词过滤等手段,可提升数据质量,文本词频分析利用TF-IDF算法,准确挖掘关键词,深入理解舆情事件的重要性,而LDA主题建模技术则通过发现主题结构,为舆情事件提供更为深刻的分析视角。研究表明,通过建立起舆情监测管理机制,可以更好地构建网络舆情环境。

This article explores the application of text mining technology in the collection and analysis of online public opinion.It discusses the lifecycle theory of online public opinion,related technologies for online public opinion collection and analysis,Chinese word segmentation algorithms,text mining techniques,as well as specific text preprocessing,word frequency analysis,and LDA topic modeling.In the preprocessing of online public opinion data,data quality can be improved through methods such as denoising,custom dictionaries and word segmentation,stop word filtering,etc.Text word frequency analysis utilizes the TF-IDF algorithm to accurately mine keywords and gain a deeper understanding of the importance of public opinion events.LDA topic modeling technology,on the other hand,provides a more profound analysis perspective for public opinion events by discovering topic structures,indicating that establishing a public opinion monitoring and management mechanism can better construct the online public opinion environment.