INFORMATION RESEARCH

Research on Network Public Opinion Emergency Recognition Method Based on Syntactic Features and Syntactic Similarity

  • Chen Jianyao ,
  • Zhai Shanshan ,
  • Xia Lixin ,
  • Liu Deyin
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  • School of Information Management, Central China Normal University, Wuhan 430079

Received date: 2020-12-09

  Revised date: 2021-02-23

  Online published: 2021-06-02

Abstract

[Purpose/significance] This study aims to identify events from the text of sudden network public opinion quickly and accurately.[Method/process] This paper proposed a method to identify network public opinion emergencies by integrating syntactic features and syntactic similarity. An event oriented syntactic feature extraction method was proposed based on syntactic features. Event syntactic feature database was constructed by using event semantic annotation and syntactic feature extraction methods. The network public opinion emergencies were identified by calculating the syntactic similarity between the text to be tested and the syntax database.[Result/conclusion] Taking the novel coronavirus pneumonia epidemic as an example, the optimal similarity of the network public opinion emergency identification method proposed by the author is 0.93 in this public opinion. 160 events and 30 non events are identified from a new text under this similarity, and the F1 value reaches 0.848. Through the method evaluation, it is proved that the proposed method is effective in using syntactic similarity to identify events and merge the same adjacent parts of speech.

Cite this article

Chen Jianyao , Zhai Shanshan , Xia Lixin , Liu Deyin . Research on Network Public Opinion Emergency Recognition Method Based on Syntactic Features and Syntactic Similarity[J]. Library and Information Service, 2021 , 65(9) : 41 -50 . DOI: 10.13266/j.issn.0252-3116.2021.09.005

References

[1] 中国互联网络信息中心. 中国互联网络发展状况统计报告[R]. 北京:中国互联网络信息中心, 2019.
[2] DING X, LI Z, LIU T, et al. ELG:an event logic graph[J/OL]. arXiv preprint arXiv:1907.08015[2021-04-11].https://arxiv.org/abs/1907.08015.
[3] AGUILAR J, BELLER C, MCNAMEE P, et al. A comparison of the events and relations across ace, ere, tac-kbp, and framenet annotation standards[C]//Proceedings of the second workshop on events:definition, detection, coreference, and representation. USA:Association for Computational Linguistics, 2014:45-53.
[4] 吴刚. 基于主题的中文事件抽取技术研究及应用[D]. 苏州:苏州大学, 2009.
[5] 项威,王邦. 中文事件抽取研究综述[J]. 计算机技术与发展, 2020(1):1-9.
[6] CHUNG S, TIMBERLAKE A. Tense, aspect and mood[M]//Language typology and syntactic description. Cambridge:Cambridge University Press, 1985:202-258.
[7] DODDINGTON G R, MITCHELL A, PRZYBOCKI M A, et al. The automatic content extraction(ACE)program tasks, data, and evaluation[C]//Proceedings of the international conference on language resources and evaluation. Portugal:European Language Resources Association, 2004:837-840.
[8] 高强,游宏梁.事件抽取技术研究综述[J].情报理论与实践,2013,36(4):114-117,128.
[9] 李章超,李忠凯,何琳.《左传》战争事件抽取技术研究[J].图书情报工作,2020,64(7):20-29.
[10] 贺瑞芳,段绍杨. 基于多任务学习的中文事件抽取联合模型[J]. 软件学报, 2019, 30(4):1015-1030.
[11] 俞琰.基于隐马尔可夫模型的招聘网络信息抽取[J].北京电子科技学院学报,2008,16(4):93-98.
[12] 李响,杨小琳,魏勇,等. 基于支持向量机的新闻事件类型识别[J]. 地理信息世界,2019,26(2):73-78.
[13] 刘忠宝,党建飞,张志剑.《史记》历史事件自动抽取与事理图谱构建研究[J].图书情报工作,2020,64(11):116-124.
[14] 尉永清,杨玉珍,费绍栋,等.融合用户情感的在线突发事件识别研究[J].情报理论与实践,2015,38(2):92-96.
[15] 武澎,王恒山,刘奇,等.微博中突发事件信息发布者被"加关注"的阈值模型研究[J].情报杂志,2012,31(11):11-13,34.
[16] 刘雅姝,张海涛,徐海玲,等.多维特征融合的网络舆情突发事件演化话题图谱研究[J].情报学报,2019,38(8):798-806.
[17] 兰月新.突发事件网络衍生舆情监测模型研究[J].现代图书情报技术,2013(3):51-57.
[18] 兰月新,曾润喜.突发事件网络舆情传播规律与预警阶段研究[J].情报杂志,2013,32(5):16-19.
[19] 张玉亮.基于发生周期的突发事件网络舆情风险评价指标体系[J].情报科学,2012,30(7):1034-1037,1043.
[20] 陈思菁,李纲,毛进,等.突发事件信息传播网络中的关键节点动态识别研究[J].情报学报,2019,38(2):178-190.
[21] 李纲,徐伟,王馨平.基于事件要素的组合模型微博热点事件摘要提取[J].图书情报工作,2018,62(1):96-105.
[22] 夏立新,陈健瑶,余华娟.基于事理图谱的多维特征网络舆情事件可视化摘要生成研究[J].情报理论与实践,2020,43(10):157-164.
[23] 张宁,朱礼军.中文问答系统问句分析研究综述[J].情报工程,2016,2(1):32-42.
[24] 袁里驰.基于依存关系的句法分析统计模型[J].中南大学学报(自然科学版),2009,40(6):1630-1635.
[25] 郭喜跃,何婷婷,胡小华,等.基于句法语义特征的中文实体关系抽取[J].中文信息学报,2014,28(6):183-189.
[26] 徐飞,叶文豪,宋英华.基于BiLSTM-CRF模型的食品安全事件词性自动标注研究[J].情报学报,2018,37(12):1204-1211.
[27] 胡宝顺,王大玲,于戈,等.基于句法结构特征分析及分类技术的答案提取算法[J].计算机学报,2008(4):662-676.
[28] 陈永波,汤昂昂,姬东鸿.中文复杂名词短语依存句法分析[J].计算机应用研究,2015,32(6):1617-1620.
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