福建电脑2024，Vol.40Issue(5)：21-26,6.DOI:10.16707/j.cnki.fjpc.2024.05.004
深度学习技术在高校网络舆情分析中的应用
Application of Deep Learning Technology in University Network Public Opinion Analysis
摘要
Abstract
With the continuous popularization of social media in the lives of college students,the management of online public opinion has become increasingly crucial.The natural language processing of traditional machine learning has problems such as strong dependence on manually labeled features,time consumption,and dimensional explosion.This article applies CNN based deep learning technology to analyze online public opinion in universities.By collecting campus hot topics from social platforms such as Baidu Tieba and Xiaohongshu,Word2vec model is used to generate word vectors,and sentiment tendency classification is performed based on CNN feature extraction.The experimental results show that the accuracy of sentiment classification based on CNN is 90.82%,which is 4.54%higher than traditional support vector machines and 2.17%higher than K-nearest neighbor methods.This article's method can provide practical support for achieving modernization of university governance.关键词
舆情分析/自然语言处理/深度学习/情感分类Key words
Public Opinion Analysis/Natural Language Processing/Deep Learning/Sentiment Classification分类
信息技术与安全科学引用本文复制引用
郑锐斌,贺丹,王凯,何卓琳..深度学习技术在高校网络舆情分析中的应用[J].福建电脑,2024,40(5):21-26,6.基金项目
本文得到2023年大学生创新创业训练计划项目"基于深度学习的高校网络舆情分析与预警系统"(No.S202313844022)资助. （No.S202313844022）
