基于宏观结构情绪理论的社会风险事件微博情绪分析

冷洁, 唐锡晋

系统科学与数学 ›› 2022, Vol. 42 ›› Issue (10) : 2634-2646.

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系统科学与数学 ›› 2022, Vol. 42 ›› Issue (10) : 2634-2646. DOI: 10.12341/jssms22610KSS

基于宏观结构情绪理论的社会风险事件微博情绪分析

    冷洁1,2, 唐锡晋1,2
作者信息 +

Macro-Structural Emotion Analysis of Social Risk Events in Weibo

    LENG Jie1,2, TANG Xijin1,2
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文章历史 +

摘要

近十年来,人们已习惯于在社交媒体平台上参与社会热点讨论.细粒度地分析人们对社会风险事件的情绪态度,将有助于有关部门及时发现热点问题并进行有效回应.文章引入了英国社会学家J.M.Barbalet关于影响社会结构和秩序、影响社会和谐的宏观结构情绪理论,同时结合了心理学人类情感分类和多种情感词典,并借助预训练词向量模型,拓展构建了宏观结构情绪词典.再以2019年4月“脆皮安全帽”事件为案例,识别了该事件下微博言论的主要情感类型,并基于点互信息(PMI)和2阶依存距离,抽取了与宏观结构情绪关联程度较高的实体和描述,获取了公众的主要观点和态度.

Abstract

It has been more than a decade since people got used to engaging in hot social discussions on social media platforms.Fine-grained sentiment analysis of public commons towards social risk events will be helpful for relevant departments to deal with hot issues in time and respond effectively.This paper introduces British sociologist J.M.Barbalet's theory of macrostructural emotion,which affects social structure,order and harmony.And then we construct a related macro-structured emotion lexicon using pre-trained word vector models,combining with psychological human emotion classifications and various emotion lexicons.By taking the"helmet incident"in April 2019 as a case,this paper identifies the main emotion types in Weibo comments under this event,and extracts entities and descriptions highly relevant with the macro structure emotions through point mutual information (PMI) and 2-order dependence distance,so as to obtain the main views and attitudes of the public.

关键词

宏观结构情绪理论 / 情绪分析 / 社会风险事件 / 新浪微博

Key words

Macro-structural emotion theory / emotion analysis / social risk events / Sina Weibo

引用本文

导出引用
冷洁 , 唐锡晋. 基于宏观结构情绪理论的社会风险事件微博情绪分析. 系统科学与数学, 2022, 42(10): 2634-2646. https://doi.org/10.12341/jssms22610KSS
LENG Jie , TANG Xijin. Macro-Structural Emotion Analysis of Social Risk Events in Weibo. Journal of Systems Science and Mathematical Sciences, 2022, 42(10): 2634-2646 https://doi.org/10.12341/jssms22610KSS
中图分类号: 68U15    62P25   

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基金

国家自然科学基金项目(7173002,71971190)资助课题.
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