特定事件意见领袖挖掘
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摘要
为挖掘适用于特定事件的意见领袖,提出一种采用特定事件相关的新闻文本构建人物关系网络,结合社会网络分析方法挖掘意见领袖的方法。从特定事件相关的新闻文本中识别人名序列,采用滑动窗口和段落划分的方式确定人物之间的影响关系,计算影响关系的强弱,构建特定事件的人物关系网络。采用改进的LeaderRank算法计算人物关系网络中人物的影响力得分。实验结果表明,该方法能够有效识别特定事件的意见领袖,改进后的LeaderRank算法相比LeaderRank等算法能够更有效地识别意见领袖。
To identify opinion leaders on specific event,a method was proposed using event-specific news articles to build a network of people relationships,combined with social network analysis methods.The sequence of names was identified from news articles related to specific events,sliding window and paragraph partition were used to determine the influence relationship among people,and the strength of influence relationship between them was calculated for constructing the event-specific network of people relationships.An improved LeaderRank algorithm was proposed to calculate the influence scores of people in the network.Experimental results show that the proposed method can effectively identify event-specific opinion leaders,and the improved LeaderRank algorithm can identify opinion leaders more effectively than LeaderRank algorithm etc.
To identify opinion leaders on specific event,a method was proposed using event-specific news articles to build a network of people relationships,combined with social network analysis methods.The sequence of names was identified from news articles related to specific events,sliding window and paragraph partition were used to determine the influence relationship among people,and the strength of influence relationship between them was calculated for constructing the event-specific network of people relationships.An improved LeaderRank algorithm was proposed to calculate the influence scores of people in the network.Experimental results show that the proposed method can effectively identify event-specific opinion leaders,and the improved LeaderRank algorithm can identify opinion leaders more effectively than LeaderRank algorithm etc.
引文
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[2]Zhou Xueyan,Yang Jing,Zhang Jianpei,et al.A BBS opinion leader mining algorithm based on topic model[J].Journal of Computational Information Systems,2014,10(6):2571-2578.
[3]Jonnalagadda S,Peeler R,Topham P.Discovering opinion leaders for medical topics using news articles[J].Journal of Biomedical Semantics,2012,3(1):1-13.
[4]Song Kaisong,Wang Daling,Feng Shi,et al.Detecting opinion leader dynamically in Chinese news comments[M]//Web-Age Information Management.Berlin Heidelberg:Springer,2012:197-209.
[5]WANG Jue,ZENG Jianping,ZHOU Baohua,et al.Online forum opinion leaders discovering method[J].Computer Engineering,2011,37(5):44-46(in Chinese).[王珏,曾剑平,周葆华,等.基于聚类分析的网络论坛意见领袖发现方法[J].计算机工程,2011,37(5):44-46.]
[6]Bai Wenjie,Zhou Tao,Wang Binghong.Immunization of susceptible-infected model on scale-free networks[J].Physica A Statistical Mechanics&Its Applications,2007,384(2):656-662.
[7]Aral S,Walker D.Identifying influential and susceptible members of social networks[J].Science,2012,337(6092):337-341.
[8]Zhou Yanbo,Lyu Linyuan,Li Menghui.Quantifying the influence of scientists and their publications:Distinguish prestige from popularity[J].New Journal of Physics,2012,14(3):33033-33049(17).
[9]Salesses P,Schechtner K,Hidalgo CA.The collaborative image of the city:Mapping the inequality of urban perception[J].Plos One,2013,8(7):e68400.
[10]Hou Bonan,Yao Yiping,Liao Dongsheng.Identifying allaround nodes for spreading dynamics in complex networks[J].Physica A Statistical Mechanics&Its Applications,2012,391(15):4012-4017.
[11]Liu YY,Slotine JJ,Barabási A.Control centrality and hierarchical structure in complex networks[J].Plos One,2012,7(9):e44459.
[12]Mui L.Computational models of trust and reputation:Agents,evolutionary games,and social networks[J].Acta Paulista De Enfermagem,2014,20(4):452-457.
[13]BakóI,Bencsura A,Hermannson K,et al.Hydrogen bond network topology in liquid water and methanol:A graph theory approach[J].Physical Chemistry Chemical Physics,2013,15(36):15163-15171.
[14]Katona Z,Zubcsek PP,Sarvary M.Network effects and personal influences:Diffusion of an online social network[J].Journal of Marketing Research,2013,48(48):425-443.
[15]Csermely P,London A,Wu LY,et al.Structure and dynamics of core/periphery networks[J].Journal of Complex Networks,2013,1(2):93-123.
[16]Chen Duanbing,Lyu Linyuan,Shang Mingsheng,et al.Identifying influential nodes in complex networks[J].Physica A Statistical Mechanics&Its Applications,2012,391(4):1777-1787.
[17]Kitsak M,Gallos LK,Havlin S,et al.Identification of influential spreaders in complex networks[J].Nature Physics,2010,6(11):888-893.
[18]Lyu Linyuan,Zhang Yicheng,Chi Hoyeung,et al.Leaders in social networks,the delicious case[J].Plos One,2011,6(6):e21202.
[19]Aleksandra Kla2nja-Milic′evic′,Mirjana Ivanovic′,Alexandros Nanopoulos.Recommender systems in e-learning environments:A survey of the state-of-the-art and possible extensions[J].Artificial Intelligence Review,2015,44(4):571-604.
[20]XU Junming,ZHU Fuxi,LIU Shichao,et al.Identifying opinion leaders by improved algorithm based on LeaderRank[J].Computer Engineering&Applications,2015,51(1):110-114(in Chinese).[徐郡明,朱福喜,刘世超,等.改进LeaderRank算法的意见领袖挖掘[J].计算机工程与应用,2015,51(1):110-114.]
[21]Wang Changbo.SentiView:Sentiment analysis and visualization for internet popular topics[J].IEEE Transactions on Human-Machine Systems,2013,43(43):620-630.
[22]Cao S,Snavely N.Graph-based discriminative learning for location recognition[J].International Journal of Computer Vision,2015,112(2):239-254.
[23]Manning CD,Surdeanu M,Bauer J,et al.The stanford CoreNLP natural language processing toolkit[C]//Meeting of the Association for Computational Linguistics:System Demonstrations.Baltimore,2014.
[24]Sienkiewicz A,Gubiec T,Kutner R,et al.Dynamic structural and topological phase transitions on the Warsaw stock exchange:A phenomenological approach[J].Acta Physica Polonica,2013,123(3):615-620.
[25]Gao Shuai,Ma Jun,Chen Zhumin,et al.Ranking the spreading ability of nodes in complex networks based on local structure[J].Physica A Statistical Mechanics&Its Applications,2014,403(6):130-147.