基于深度学习的社交网络舆情信息抽取方法综述

Survey of Social Network Public Opinion Information Extraction Based on Deep Learning

随着社交媒体平台的快速发展,舆情信息得以在极短的时间内大范围传播,如果不对舆情信息加以管理和控制,将对网络环境乃至社会环境造成巨大威胁。信息抽取技术因其语义化和精准性成为舆情分析和管理的第一步,也是最关键的一步。近年来,随着深度学习的发展,其自动学习潜在特征、组合特征的能力使信息抽取各个子任务的准确率都得到了很大的提高。文中结合社交网络舆情的特点和深度学习技术在信息抽取领域的应用,对基于深度学习的社交网络舆情信息抽取方法进行了系统的梳理和总结。首先整理了社交网络舆情信息的组织方式,详细阐述了舆情信息抽取的框架、评价指标,然后对现有的基于深度学习的舆情信息抽取模型进行了全面的回顾和分析,讨论了现有方法的适用性及局限性,最后对未来的研究趋势进行了展望。

With the rapid development of social media platforms,public opinion information can be widely disseminated in a very short period of time.If the information of public opinion is not managed and controlled,it will pose a great threat to the network environment and even the social environment.Information extraction technology has become the first and the most significant step in public opinion analysis and management due to its semantization and accuracy.Over the last few years,with the development of deep learning,its ability to automatically learn potential features and combine these features has dramatically improved the accuracy of each sub-task of information extraction.This paper systematically composes and summarizes the methods of extracting information by combining the characteristics of social media public opinion and deep learning technology.Firstly,we sort out the organization of public opinion information in social networks,elaborate the framework and evaluation indexes of public opinion information extraction.Then we conduct a comprehensive review and analysis of existing deep learning-based public opinion information extraction models,discuss the applicability and limitations of existing methods.Finally,the future research trends is prospected.