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基于深度学习的社交网络舆情信息抽取方法综述 被引量:5

Survey of Social Network Public Opinion Information Extraction Based on Deep Learning
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摘要 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.
作者 王剑 彭雨琦 赵宇斐 杨健 WANG Jian;PENG Yu-qi;ZHAO Yu-fei;YANG Jian(School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450000,China;School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450000,China;Yunnan Key Laboratory of Smart City in Cyberspace Security,Yuxi Normal University,Yuxi,Yunnan 653100,China)
出处 《计算机科学》 CSCD 北大核心 2022年第8期279-293,共15页 Computer Science
基金 国家自然科学基金(61972133) 云南省智慧城市网络空间安全重点实验室开放课题项目(202105AG070010)。
关键词 社交网络 社交媒体 舆情信息 信息抽取 深度学习 Social network Social media Public opinion Information extraction Deep learning
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  • 1孙逊,胡光锐,李剑萍.一种基于模糊聚类的隶属函数定义方法[J].计算机应用与软件,2005,22(7):86-88. 被引量:8
  • 2张晓艳,王挺,陈火旺.命名实体识别研究[J].计算机科学,2005,32(4):44-48. 被引量:66
  • 3俞鸿魁,张华平,刘群,吕学强,施水才.基于层叠隐马尔可夫模型的中文命名实体识别[J].通信学报,2006,27(2):87-94. 被引量:157
  • 4Wikipedia:Message Understanding Conference[EB/OL].2013-12-27.http://en.wikipedia.org/wiki/Message_Understanding_Conference.
  • 5Wikipedia:Named Entity Recognition[EB/OL].2013-12-28.http://en.wikipedia.org/wiki/Named_Entity_Recognition.
  • 6Rizzo G,Troncy R.NERD:Evaluating Named Entity Recognition Toolsinthe Web of Data[J].Lecture Notesin Computer Science,2012(7295):39-55.
  • 7Rizzo G,Troncy R.NERD:A Framework for Unifying Named Entity Recognition and Disam biguation Extraction Tools[C]∥13th Conference ofthe European Chapter of the Association for ComputationalL inguistics.2012:73-76.
  • 8Li Chen-liang,Weng Jian-shu.TwiNER:Named Entity Recognition in Targeted Twitter Stream[C]∥SIGIR.2012:721-730.
  • 9Liu Xiao-hua,Zhang Shao-dian,et al.Recognizing Named Entitiesin Tweets[C]∥ACL.2011:359-367.
  • 10Finin T,Murnane W.Annotating Named Entitiesin TwitterDatawith Crowdsourcing[C]∥ACL.2010.

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