CASIA OpenIR  > 毕业生  > 硕士学位论文
情感网络分析与核心人物挖掘
Alternative TitleOpinion Network Analysis and Leader Identification
周恒民
Subtype工学硕士
Thesis Advisor王飞跃 ; 曾大军
2010-05-13
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword核心人物 情感挖掘 情感网络 社会网络 社会网络分析 Opinion Leaders Opinion Mining Opinion Network Social Network Social Network Analysis
Abstract在线社会网络站点的日益流行为人们提供了很多情感表达的途径。人们在这些站点上参与在线社会活动,自由地发表观点,表达情感,使在线社会网络中承载了日益丰富的网民的情感信息。然而,目前大部分的在线社会网络研究文献中,很少有研究者将社区中人们表达的观点和情感作为构建和分析社会网络的重点。本文利用在线社会网络中富含成员观点和情感倾向信息这一特点,在传统的社会网络研究基础上提出了基于情感信息的特殊社会网络——情感网络,并根据情感网络的特征进行了核心人物挖掘相关研究。 本文的研究涉及社会网络分析和情感挖掘技术的相关内容和方法,并针对以下研究问题进行了探索:1)如何对在线社会网络中成员的情感信息进行合理的、可量化的呈现;2)如何充分利用在线社会网络中的情感因素来提高识别社区中的核心人物的准确度。 本文研究的主要贡献包括: 1) 提出了一种新的基于成员之间的隐式关系构建社会网络的方法,并引入了情感网络的概念。 2) 分析了情感网络的特征,包括隐式关系、时效性、分层性、传递性、松耦合等,针对挖掘在线社会网络核心人物的问题提出了基于节点属性和链接结构特征的情感网络节点排序算法。 3) 通过基于真实数据的实验,考察了情感网络的特点。在线社会网络中的成员之间的情感倾向总体上趋于正向,但情感的强度通常不是很极端,大部分都靠近中性的态度。在大规模情感网络中,有较多成员在交流的过程中表达出了自己的情感和观点,因而使情感因素成为识别核心人物的有效参考。
Other AbstractThe increasing popularity of social networking websites has greatly facilitated expression of people’s personal opinions. However, in the current literature, opinions expressed in online communities are not taken into consideration when constructing and analyzing social networks. This paper is aimed at enhancing traditional social network analysis by incorporating opinions mined from online user-generated content. We present a new approach based on opinion-based social networks and a related PageRank-like method, named OpinionRank, to identify opinion leaders in social networks. This study combines social network analysis and opinion mining technology to study opinion networks and is aimed at answering the following research questions: a) How can one extract people’s opinions about each other and represent them in social networks? b) Can sentiment clues in opinions help to identify leaders of online communities? The main contributions of this work are as follows a) We introduced the concept of opinion networks and proposed a new approach for construction of opinion networks based on the implicit relations between social members. b) By analyzing the characteristics of opinion networks, this study proposed node-based and link-based ranking models to identify core members in such social networks. The effectiveness of different network sorting methods are evaluated on real word datasets. c) This study also found that the overall opinion orientation of the entire opinion network are positive, and most social members express more positive opinions than negatives. Large scale opinion networks are rich of sentiment information, and the complexity of large scale opinion networks makes them more stable than small networks.
shelfnumXWLW1559
Other Identifier200728017029247
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7520
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
周恒民. 情感网络分析与核心人物挖掘[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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