广东工业大学学报 ›› 2021, Vol. 38 ›› Issue (04): 41-51.doi: 10.12052/gdutxb.210022

• • 上一篇    下一篇

基于观点挖掘的突发事件微博意见领袖识别方法

刘高勇, 谭依雯, 艾丹祥, 黄靖钊   

  1. 广东工业大学 管理学院，广东 广州 510520
  • 收稿日期:2021-02-01 出版日期:2021-07-10 发布日期:2021-05-25
  • 通信作者: 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 E-mail:aidx@gdut.edu.cn
  • 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
  • 基金资助:
    国家自然科学基金资助项目(71740024)；广东省哲学社会科学“十三五”规划2020年度课题(GD20CTS01)；广州市哲学社会科学发展“十三五”规划2018年度课题(2018GZYB67)

A Microblog Opinion Leader Identification Method Based on Opinion Mining in Emergencies

Liu Gao-yong, Tan Yi-wen, Ai Dan-xiang, Huang Jing-zhao   

  1. School of Management, Guangdong University of Technology, GuangZhou 510520, China
  • Received:2021-02-01 Online:2021-07-10 Published:2021-05-25

HTML

PDF (PC)

2695

摘要： 基于意见领袖概念的本质, 运用观点挖掘技术研究突发事件中微博意见领袖的识别, 为网络舆情治理提供参考。提出三步识别方法框架: 首先采用文献分析法构建指标模型, 评价微博博主的信息影响力; 其次构建文本主客观分类模型, 计算高影响力博主事件相关博文的观点输出性, 识别观点博文; 然后针对观点博文的评论文本构建情感极性分类模型, 计算博文观点获得的支持度以及博主观点的支持率, 最终将输出了观点并获得较多支持的高影响力博主作为该事件的“意见领袖”。同时, 运用上述方法对典型实际案例的微博舆情数据进行分析, 识别该事件的微博意见领袖, 对其特征和舆情参与行为进行观察。并将该结果与社会网络分析法、专家人工分析法识别的结果进行了对比, 验证了本方法的科学性和有效性。结果表明, 本方法能够识别在突发事件舆情生命周期中真正拥有舆论引导力, 具备治理价值的微博意见领袖。

关键词: 观点挖掘, 文本主客观分类, 文本情感极性分析, 突发事件, 意见领袖

Abstract: Based on the nature of opinion leaders, the opinion mining technology is used to study the identification of microblog opinion leaders in emergencies in order to provide a reference for the governance of online public opinions. A three-step opinion leaders identification framework is proposed: first, using a literature analysis, an indicator model is built to evaluate the influence of microblog bloggers; second, a text subjective and objective classification model is built to determine whether high-influence bloggers publish event-related opinions and information, and to identify opinion blog posts; then an emotional polarity classification model is built based on the comment text of the opinion blog post to calculate the support degree of the blog post opinions and the support rate of the blogger’s opinions so that those high-influence bloggers who have first output their opinions and then received more support can be rated as the event "Opinion Leaders". These methods are used in the following text to analyze the microblog public opinion data of the typical practical cases, identifying the microblog opinion leaders of the incident, with their characteristics and public opinion participation behaviors observed and analyzed, and the results compared with those of social network analysis methods and the expert manual analysis method to verify the scientificity and effectiveness of this method. The results show that this method can identify microblog opinion leaders who truly have the guiding power of public opinions and can have governance value in the evolution of public opinions in emergencies.

Key words: opinion mining, text subjective and objective classification, text emotional polarity analysis, emergency events, opinion leaders

中图分类号: 

  • TP3-05
[1] LAZARSFELD P F, BERELSON B, GAUDET H. The people’s choice[M]. New York: Columbia University Press, 1948.
[2] 王佳敏, 吴鹏, 陈芬, 等. 突发事件中意见领袖的识别和影响力实证研究[J]. 情报学报, 2016, 35(2): 169-176.
WANG J M, WU P, CHEN F, et al. Empirical study on recognition and influence of opinion leaders in emergency [J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(2): 169-176.
[3] 彭丽徽, 李贺, 张艳丰. 基于灰色关联分析的网络舆情意见领袖识别及影响力排序研究——以新浪微博“8·12滨海爆炸事件”为例[J]. 情报理论与实践, 2017, 40(9): 90-94.
PENG L H, LI H, ZHANG Y F. Research on the identification and influence ranking of network public opinion leaders based on grey relational analysis [J]. Intelligence Theory and Practice, 2017, 40(9): 90-94.
[4] 吴江, 赵颖慧, 高嘉慧. 医疗舆情事件的微博意见领袖识别与分析研究[J]. 数据分析与知识发现, 2019, 3(4): 53-62.
WU J, ZHAO Y H, GAO J H. Research on Microblog opinion leaders identification and analysis in medical public opinion incidents [J]. Data Analysis and Knowledge Discovery, 2019, 3(4): 53-62.
[5] 陈芬, 陈佩帆, 吴鹏, 等. 融合用户特征与多级文本倾向性分析的网络意见领袖识别[J]. 情报理论与实践, 2018, 41(7): 143-148.
CHEN F, CHEN P F, WU P, et al. Online opinion leader identification method integrating user feature and multilevel text sentiment analysis [J]. Information Theory and Practice, 2018, 41(7): 143-148.
[6] 安璐, 胡俊阳, 李纲. 基于主题一致性和情感支持的评论意见领袖识别方法研究[J]. 管理科学, 2019, 32(1): 3-13.
AN L, HU J Y, LI G. A method of identifying opinion leaders based on topic consistency and emotional support [J]. Journal of Management Science, 2019, 32(1): 3-13.
[7] 孙乃利, 王玉龙, 沈奇威. 微博客意见领袖识别的研究[J]. 电信技术, 2012(12): 78-80.
SUN N L, WANG Y L, SHEN Q W. Research on the identification of microblog opinion leaders [J]. Telecommunications Technology, 2012(12): 78-80.
[8] 魏志惠, 何跃. 基于信息熵和未确知测度模型的微博意见领袖识别——以“甘肃庆阳校车突发事件”为例[J]. 情报科学, 2014, 32(10): 38-43.
WEI Z H, HE Y. Identify microblogging opinion leaders based on information entropy and unascertained measure model—taking “emergencies of Qingyang school bus” as an example [J]. Information Science, 2014, 32(10): 38-43.
[9] 胡若涵, 张慧明. 中兴通讯案: 中美科技贸易摩擦的意见领袖识别研究[J]. 中国经贸导刊(中), 2018(23): 11-13.
HU R H, ZHANG H M. ZTE case: research on the identification of opinion leaders in Sino-US technology trade friction [J]. China Economic and Trade Guide (Part 2), 2018(23): 11-13.
[10] 陈芬, 高小欢, 彭玥, 等. 融合文本倾向性分析的微博意见领袖识别[J]. 数据分析与知识发现, 2019, 3(11): 120-128.
CHEN F, GAO X H, PENG Y, et al. Text sentiment analysis and its application in Microblog opinion leader identification [J]. Data Analysis and Knowledge Discovery, 2019, 3(11): 120-128.
[11] WIEBE J, WILSON T, BRUCE R, et al. Learning subjective language [J]. Computational Linguistics, 2004, 30(3): 277-308.
[12] 杨武, 宋静静, 唐继强. 中文微博情感分析中主客观句分类方法[J]. 重庆理工大学学报, 2013, 27(1): 51-56.
YANG W, SONG J J, TANG J Q. A study on the classification approach for Chinese Microblog subjective and objective sentences [J]. Journal of Chongqing University of Technology, 2013, 27(1): 51-56.
[13] 丁晟春, 吴靓婵媛, 李红梅. 基于SVM的中文微博观点倾向性识别[J]. 情报学报, 2016, 35(12): 1235-1243.
DING S C, WU L C Y, LI H M. Chinese Micro-blogging opinion recognition based on SVM model [J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(12): 1235-1243.
[14] 刘培玉, 旬静, 费绍栋, 等. 基于隐马尔可夫模型的主观句识别[J]. 中文信息学报, 2016, 30(4): 206-212.
LIU P Y, XUN J, FEI S D, et al. Subjective sentence recognition based on hidden Markov model [J]. Journal of Chinese Information Processing, 2016, 30(4): 206-212.
[15] 谭有新, 滕少华. 短文本特征的组合加权方法[J]. 广东工业大学学报, 2020, 37(5): 51-61.
TAN Y X, TENG S H. Combined weighting method for short text features [J]. Journal of Guangdong University of Technology, 2020, 37(5): 51-61.
[16] 杜洪涛, 王君泽, 李婕. 基于多案例的突发事件网络舆情演化模式研究[J]. 情报学报, 2017, 36(10): 1038-1049.
DU H T, WANG J Z, LI J. Research on evolution Model of online public opinion in emergencies events based on multiple cases [J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(10): 1038-1049.
[1] 杜松华, 李展尧, 李东霞, 蒋瑞新. 交互式社交媒体广告中用户共情表达和购买意愿的影响机制研究[J]. 广东工业大学学报, 2021, 38(04): 17-25.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发