基于模块度的话题发现及网民情感波动研究——以新浪微博“中美间贸易摩擦”话题为例
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摘要
[目的/意义]探索热点事件评论网络中话题社群及网民的情感波动,掌握舆情事件发展过程,对于整体把握热点事件的发展方向,做好新时期网络舆论的引导工作具有重大意义。[方法/过程]以复杂网络理论为基础,基于评论词语间的共现关系构建基于事件发展的子事件网络,通过社群发现算法来识别子事件评论网络中的话题社群,将情感词依据情感词典赋予情感分类属性,基于事件的演化过程动态地跟踪网民意见以及情感波动。[结果/结论]研究结果表明,评论网络群落发现以及变异系数方法可以有效地衡量网民话题讨论的规模与集中程度;评论网络中赋予情感词节点情感分类属性方法可以体现事件演化过程中网民的情感变化;舆论衍生话题对事件的舆情发展有持续性影响;网民话题讨论内容对于事件演化具有一定程度上的前瞻性。
[Purpose/significance] Exploring topical communities and sentiment fluctuations of Internet users and grasping the process of development of events have great significance to control the development direction of the events and lead guidance of the network public opinion in the new period. [Method/process] Based on the theory of complex networks, the study constructed sub event network based on co-occurrence relations among user comments, identifying topic community in sub-event commenting networks through community discovery algorithms and giving the attribute to emotion word according to the emotional dictionary. The study dynamically tracked the opinions and emotions of Internet users based on the evolution process of events. [Result/conclusion] The conclusion showed that the commenting network community discovery and coefficient of variation method can effectively measure the scale and concentration of Internet users' topic discussion; emotional word sentiment classification attribute method can reflect the emotional changes of Internet users in the process of event evolution; the derived topic of public opinion has a continuous influence on the development of the event public opinion; the content of the topic discussion of Internet users has some foresight to the evolution of the event.
[Purpose/significance] Exploring topical communities and sentiment fluctuations of Internet users and grasping the process of development of events have great significance to control the development direction of the events and lead guidance of the network public opinion in the new period. [Method/process] Based on the theory of complex networks, the study constructed sub event network based on co-occurrence relations among user comments, identifying topic community in sub-event commenting networks through community discovery algorithms and giving the attribute to emotion word according to the emotional dictionary. The study dynamically tracked the opinions and emotions of Internet users based on the evolution process of events. [Result/conclusion] The conclusion showed that the commenting network community discovery and coefficient of variation method can effectively measure the scale and concentration of Internet users' topic discussion; emotional word sentiment classification attribute method can reflect the emotional changes of Internet users in the process of event evolution; the derived topic of public opinion has a continuous influence on the development of the event public opinion; the content of the topic discussion of Internet users has some foresight to the evolution of the event.
引文
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[2]CHEN Y,AMIRI H,LI Z,et al.Emerging topic detection for organizations from microblogs[C]//Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval.Dublin:ACM,2013:43-52.
[3]STILO G,VELARDI P.Efficient temporal mining of micro-blog texts and its application to event discovery[J].Data mining and knowledge discovery,2016,30(2):372-402.
[4]HU M,LIU B.Mining and summarizing customer reviews[C]//Proceedings of the ACM SIGKDD international conference on knowledge discovery&data mining.Seattle:ACM,2004,168-197.
[5]李亚星,王兆凯,冯旭鹏,等.基于实时共现网络的微博话题发现[J].计算机应用,2016,36(5):1302-1306.
[6]宋丽娜,冯旭鹏,刘利军.基于SOM聚类的微博话题发现[J].计算机应用研究,2018,35(3):671-679.
[7]唐晓波,王洪艳.基于潜在语义分析的微博主题挖掘模型研究[J].图书情报工作,2012,56(24):114-119.
[8]唐晓波,肖璐.基于依存句法分析的微博主题挖掘模型研究[J].情报科学,2015,33(9):61-65.
[9]王正成,袁竹星.面向主题的微博意见领袖挖掘研究[J].情报科学,2018,36(3):112-116.
[10]肖璐,唐晓波.基于句子成分的微博热点主题挖掘模型研究[J].情报科学,2015,32(11):44-47.
[11]赵妍妍,秦兵,刘挺.文本情感分析[J].软件学报,2010,21(8):1834-1848.
[12]PANG B,LEE L,VAITHYANATHAN S.Thumbs up?sentiment classification using machine learning techniques[EB/OL].[2018-05-08].https://arxiv.Org/pdf/cs/0205070.pdf.
[13]PANG B,LEE L.A sentimental education:sentiment analysis using subjective summarization based on minimum based on minimum cuts[EB/OL].[2018-05-08].https://arxiv.org/pdf/cs/0205070.pdf.
[14]BOLLEN J,MAO H,PEPE A.Modeling public mood and emotion:Twitter sentiment and socio-economic phenomena[J].Computer science,2009,44(12):2365-2370.
[15]ZOU X,YANG J,ZHANG J.Microblog sentiment analysis using social and topic context[J].PLo S one,2018,13(2):e0191163.
[16]ZHANG L,GHOSH R,DEKHIL M,et al.Combining lexicon-based and learning-based methods for Twitter sentiment analysis[EB/OL].[2018-05-10].http://www.hpl.hp.Com/techreports/2011/HPL-2011-89.pdf.
[17]唐晓波,兰玉婷.基于特征本体的微博产品评论情感分析[J].图书情报工作,2016,60(16):121-127.
[18]杨亮,林原,林鸿飞.基于情感分布的微博热点事件发现[J].中文信息学报,2012,26(1):84-90.
[19]黄卫东,陈凌云,吴美蓉.网络舆情话题情感演化研究[J].情报杂志,2014,33(1):102-107.
[20]张向阳,那日萨,孙娜.基于有向网络的在线评论情感倾向性分析[J].情报科学,2016,34(11):66-69.
[21]杨锋,彭勤科,徐涛.基于随机网络的在线评论情绪倾向性分类[J].自动化学报,2010,36(6):837-844.
[22]BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fast unfolding of communities in large networks[J].Journal of statistical mechanics,2008,2008(10):155-168.
[23]LEWIS T G.网络科学:原理与应用[M].陈向阳,巨修炼,等译.北京:机械工业出版社,2011:1-12.
[24]WALLACE M L,GINGRAS Y,DUHON R.A new approach for detecting scientific specialties from raw cocitation networks[J].Association for Information Science and Technology,2009,60(2):240-246.
[25]NEWMAN M E J,GIRVAN M.Finding and evaluating community structure in networks[J].Physical review E,2004,69(2):1-15.
[26]汪小帆,李翔,陈关荣.网络科学导论[M].北京:高等教育出版社,2012:130-140.
[27]TANG L,LIU H.社会计算:社区发现和社会媒体挖掘[M].文益民,闭应洲,译.北京:机械工业出版社,2013:20-30.
[28]徐琳宏,林鸿飞,潘宇,等.情感词汇本体的构造[J].情报学报,2008,27(2):180-185.
[29]WECHSLER D.Die messung der intelligenz erwachsener[M].Bem-Stuttgart:Huber,1964.