基于深度学习的网络舆情监测系统研究

Research on Network Public Opinion Monitoring System Based on Deep Learning

随着国内互联网的快速发展,网络舆情监测工作已经成为相关部门、企业工作内容的一部分。构建舆情监测系统可以提前发现舆情危机,及时处理危机公关。文中提出了一个完整的网络舆情监测系统框架,该系统由信息采集层、数据资源层、数据分析应用层和应用服务层4部分组成。该系统首先根据关键词自动采集全网多数门户网站、微博和微信公众号中的数据,包括文章与评论;然后将这些数据进行清洗、分词并过滤停用词,利用Word2Vec模型进行词嵌入,得到矢量化文本;随后再将矢量化的文本导入LSTM深度学习模型中进行情感分析,进一步将数据分为敏感数据、中性数据和非敏感数据;最后将舆情预警信息通过可视化技术显示。文中所提出的网络舆情监测系统可以帮助监管部门及时监测和引导相关舆论,促进社会和谐发展。

With the rapid development of the domestic Internet,network public opinion monitoring has become a part of the work of relevant departments and enterprises.Establishment of a public opinion monitoring system can detect public opinion crises in advance and deal with crisis public relations in time.The current study presents a complete framework of network public opinion monitoring system,which consists of four parts:information collection layer,data resource layer,data analysis application layer and application service layer.First,the proposed system can automatically collect data from most portals,microblogs and WeChat accounts,including articles and comments according to keywords.Then,these data are cleaned,segmented and filtered,and the word is embedded using Word2Vec model to obtain the vectorized text.The vectorized text is imported into LSTM deep learning model for sentiment analysis,and the data can be divided into sensitive data,neutral data and non-sensitive data.Finally,the public opinion warning information is displayed by visualization technology.The proposed network public opinion monitoring system can help regulators to monitor and guide relevant public opinions in a timely manner,and promote the harmonious development of society.