融合立场检测和主题挖掘的突发公共事件网络舆情演化研究

刘高勇; 黄靖钊; 艾丹祥

doi:10.12052/gdutxb.210202

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融合立场检测和主题挖掘的突发公共事件网络舆情演化研究

更新时间：2026-05-27

- 融合立场检测和主题挖掘的突发公共事件网络舆情演化研究
- A Research on Online Public Opinion Evolution of Public Emergencies Based on Stance Detection and Topic Mining
- 广东工业大学学报 2022年39卷第3期页码：32-40, 48
- 作者机构：
  
  广东工业大学管理学院,广东,广州,510520
- 作者简介：
  
  刘高勇(1975–)，男，教授，博士，主要研究方向为大数据分析、智能信息处理
  艾丹祥(1978–)，女，副教授，博士，主要研究方向为知识工程、文本挖掘，E-mail：aidx@gdut.edu.cn
- 基金信息：
  
  广东省哲学社会科学“十三五”规划项目(GD20CTS01);广州市哲学社会科学发展“十三五”规划项目(2018GZYB67)
- DOI：10.12052/gdutxb.210202
  中图分类号： C916;G203;G210.7
- 纸质出版：2022
- 稿件说明：
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刘高勇, 黄靖钊, 艾丹祥. 融合立场检测和主题挖掘的突发公共事件网络舆情演化研究[J]. 广东工业大学学报, 2022, 39(3): 32-40, 48.

Liu Gao-yong, Huang Jing-zhao, Ai Dan-xiang. A Research on Online Public Opinion Evolution of Public Emergencies Based on Stance Detection and Topic Mining[J]. 2022, 39(3): 32-40, 48.
刘高勇, 黄靖钊, 艾丹祥. 融合立场检测和主题挖掘的突发公共事件网络舆情演化研究[J]. 广东工业大学学报, 2022, 39(3): 32-40, 48. DOI： 10.12052/gdutxb.210202.

Liu Gao-yong, Huang Jing-zhao, Ai Dan-xiang. A Research on Online Public Opinion Evolution of Public Emergencies Based on Stance Detection and Topic Mining[J]. 2022, 39(3): 32-40, 48. DOI： 10.12052/gdutxb.210202.

摘要

基于立场检测和主题挖掘的突发公共事件舆情演化研究，能够帮助政府及利益相关者快速地掌握突发公共事件网络舆情的演化规律，具有重要的意义。划分具体突发公共事件的舆情生命周期，提出新的立场检测模型和主题句挖掘方法，针对每个生命周期阶段，在识别大众网民的立场信息的基础上筛选出高效用的舆情信息，再挖掘高效用舆情信息的主题，以深入分析突发公共事件主题信息的演化规律。以“杭州女子失踪案”的微博数据为例，首先将本文方法与多种方法的实验结果进行对比分析，验证了方法的有效性；然后基于实验结果进行舆情演化分析，证明了其能够在实际的突发公共事件舆情中快速聚焦关键点，较好地分析突发公共事件舆情演化规律和特点。该方法能较有效、准确地识别和分析舆情内容，为网络舆情演化的研究提供了新视角。

Abstract

The research on the evolution of public opinions in public emergencies based on stance detection and topic mining can help the government and stakeholders quickly grasp the evolution law of online public opinions in public emergencies

which is of great significance. The public opinion life cycle of specific public emergencies is divided

and a new stance detection model and a topic sentence mining method are proposed. For each life cycle stage

on the basis of identifying the stance information of mass Internet users

the efficient public opinion information is screened out

and then the topics of efficient public opinion information are mined

so as to deeply analyze the evolution law of the topic information of public emergencies. Taking the microblog data of "A Hangzhou Woman Missing Case" as an example

firstly

the experimental results of this method and various methods are compared to verify the effectiveness of this method.Then

the evolution of public opinions is analyzed based on the experimental results of this method

which proves that this method can quickly focus on the key points in the actual public opinions of public emergencies

and better analyze the evolution law and characteristics of public opinions of public emergencies. This method can effectively and accurately identify and analyze the content of public opinions

and provide a new perspective for the study of the evolution of network public opinions.

关键词

Keywords

references

中国法制出版社. 中华人民共和国突发事件应对法: 实用版[M]. 北京: 中国法制出版社, 2010.

MOHAMMAD S, KIRITCHENKO S, SOBHANI P, et al. SemEval-2016 task 6: detecting stance in Tweets[C]// ZHU X D. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). Stroudsburg: ACL, 2016: 31-41.

SRIDHAR D, GETOOR L, WALKER M . Collective stance classification of posts in online debate forums[C]// GETOOR L. Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media. Maryland: Association for Computational Linguistics, 2014: 109-117.

WALKER M A, ANAND P, ABBOTT R, et al. Stance classification using dialogic properties of persuasion[C]// ANAND P. Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Montreal: Association for Computational Linguistics, 2012: 592-596.

XU R, ZHOU Y, WU D, et al. Overview of NLPCC shared task 4: stance detection in Chinese microblogs[C]// LIN C Y. NLPCC-ICCPOL 2016. Switzerland: Springer International Publishing, 2016: 907-916.

DIAN Y J, JIN Q, WU H M. Stance detection in Chinese microblogs via fusing multiple text features [J]. Computer Engineering and Application, 2017, 53(21): 77-84.

MOHAMMAD S M, SOBHANI P, KIRITCHENKO S. Stance and sentiment in Tweets [J]. ACM Transactions on Internet Technology, 2017, 17(3): 23.

KAZUAKI H, AKIRA S, NAOAKI O, et al . Stance detection attending external knowledge from Wikipedia [J ] . Journal of Information Processing, 2019, 27: 499-506.

BAI J, LI F, JI D H. Attention based Bi-LSTM-CNN Chinese microblogging position detection model [J]. Computer Applications and Software, 2018(3): 266-274.

ZHOU Y F, ZHOU G, LU Z L. Approach of stance detection in micro-blog based on transfer learning and multi-representation [J]. Computer Science, 2017, 45(9): 243-247.

LUHN H P. A statistical approach to mechanized encoding and searching of literary information [J]. IBM Journal of Research and Development, 1957, 1(4): 309-317.

WEI W, GUO C. A text semantic topic discovery method based on the conditional co-occurrence degree [J]. Neurocomputing, 2019, 368(27): 11-24.

AN L, HU J Y, LI G. A method of identifying comment opinion leaders based on topic consistency and emotional support [J]. Journal of Management Science, 2019, 32(1): 1-13.

LI Y P, JIN C, JI J C. A keyword extraction algorithm based on word2vec[J]. E-science Technology and Application, 2015(4): 54-59.

KONG S, WANG Y. Topic sentences extraction method based on sentence similarity [J]. Journal of the China Society for Scientific and Technical Information, 2011, 30(6): 605-609.

TANG X B, XIAO L. Research of micro-blog topics mining based on sentence granularity [J]. Journal of the China Society for Scientific and Technical Information, 2014(33): 632.

WAN G, ZHANG G P, BAI Y, et al . News topic sentence extraction via weighted features [J ] . Journal of Chinese Information Processing, 2017, 31(5): 120-126.

DU H T, WANG J Z, LI J. Research on evolution model for online public opinion of emergent events based on multiple cases [J]. Journal of the China Society for Scientific and Technical Information, 2017, 10(10): 1038-1049.

FINK S. Crisis management: planning for the inevitable [M]. New York: American Management Association, 1986.

JIA Y M, AN L, LI G. On the online information dissemination pattern of city emergencies [J]. Journal of Intelligence, 2015(4): 94-100.

WANG Y F, WANG Y S, YANG J. Topic discovery and empirical analysis of network public opinion based on community detection and key node identification [J]. Library and Information, 2020(5): 48-58.

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