面向高校疫情管控的跨媒体舆情情感分析

Cross-media Public Opinion Sentiment Analysis for Epidemic Control in Colleges and Universities

实证类的网络舆情分析研究大多针对单一媒体进行,时间成本高、主观性强。在后疫情时代媒体融合快速发展的背景下,跨媒体、多终端的异源信息为网络舆情分析与治理带来新的挑战。本研究聚焦高校疫情管控过程中所出现的舆情问题,提出一种基于词频分析与LDA模型的舆情情感分析方法。通过网络爬虫采集2021年8~10月关于“开学全封闭管理”与“国庆如期放假”的舆情数据,在进行数据清洗与分词处理之后,利用TF-IDF算法提取高频词并构建词云,通过LDA模型提取舆情的主题进而进行舆情分析。以跨媒体的高校疫情管控舆情实例进行验证,结果表明本研究可丰富公共危机政府应对体系,为政府有效管理提供理论依据和技术支撑。

Empirical research on network public opinion analysis is mostly conducted on a single media,with high time cost and strong subjectivity.In the context of the rapid development of media integration in the post-epidemic era,cross-media and multi-terminal heterogeneous information brings new challenges to the analysis and governance of online public opinion.This research focuses on the public opinion problems in the process of epidemic control in colleges and universities,and proposes a public opinion sentiment analysis method based on word frequency analysis and LDA model.Collect public opinion data from August~October 2021 that are too“closed for school opening”and“National Day holiday as scheduled”through web crawlers.After data cleaning and word segmentation,the TF-IDF algorithm is used to extract high-frequency words and build a word cloud,through the LDA model to extract the topic of public opinion and then conduct public opinion analysis.Verified by cross-media public opinion examples of epidemic control in colleges and universities,the results show that this research can enrich the government response system for public crises,and provide theoretical basis and technical support for effective government management.