Please wait a minute...
New Technology of Library and Information Service  2013, Vol. 29 Issue (3): 58-64    DOI: 10.11925/infotech.1003-3513.2013.03.10
Current Issue | Archive | Adv Search |
Quantified Evaluation for Social Networks Based on LDA Model
Wang Jiaqi, Xu Chaojun, Li Yi
School of Educational Science, Nanjing Normal University, Nanjing 210097, China
Download: PDF(749 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  As propelled by the rapid growth of text data, it is urgent to utilize automated tools to monitor the user relationship, topic trend and the implying values of the platforms. A new modeling framework based on LDA is proposed to evaluate the social networks automatically. The authors first map the text into topic space, eliminating the uncorrelated information based on topic distribution and user feature, then create an evaluation method from social network analysis perspective, mining the structure of the social network from three aspects including user centrality, topic popularity and community activity. Experiments show that promising results are achieved by the new model.
Key wordsSocial network      LDA      Topic model      Two-stage evaluation     
Received: 20 February 2013      Published: 14 May 2013
:  TP391  

Cite this article:

Wang Jiaqi, Xu Chaojun, Li Yi. Quantified Evaluation for Social Networks Based on LDA Model. New Technology of Library and Information Service, 2013, 29(3): 58-64.

URL:     OR

[1] Kent State University.Website Evaluation Form[EB/OL].[2012-12-20].
[2] University of Michigan Law School. The Argus Clearinghouse[EB/OL].[2012-12-20].
[3] Jupiter Research Corporation[EB/OL].[2012-12-10].
[4] 李长玲,王效岳,付鑫金.网站定量评价指标体系的构建与权值分配[J]. 图书情报工作 ,2008,52(7): 52-56.(Li Changling, Wang Xiaoyue, Fu xinjin. Construction of Quantitative Evaluation Index System and Weight Assignment for Websites[J].Library and Information Service, 2008,52(7):52-56.)
[5] 张圣亮,杨俊,刘彦初.虚拟社区之BBS服务质量实证研究[J]. 世界标准化与质量管理 ,2007(2): 24-29.(Zhang Shengliang, Yang Jun, Liu Yanchu. An Empirical Research on the BBS Service Quality of Virtual Community[J]. World Standardization & Quality Management,2007(2):24-29.)
[6] 王蕾, 房俊民. 网络论坛质量评价的影响因素研究[J]. 情报科学 , 2011,29(11): 1647-1652.(Wang Lei, Fang Junmin. Research on Influence Factors of Online Community Evaluation[J]. Information Science, 2011,29(11):1647-1652.)
[7] Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J].Journal of Machine Learning Research, 2003,3:993-1022.
[8] Blei D M. Probabilistic Topic Models[J].Communications of the ACM, 2012,55(4):77-84.
[9] Wei X,Croft W B. LDA-based Document Models for Ad-hoc Retrieval[C].In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.2006:178-185.
[10] 刁宇峰,杨亮,林鸿飞.基于LDA模型的博客垃圾评论发现[J]. 中文信息学报 , 2011,25(1):41-47.(Diao Yufeng, Yang Liang, Lin Hongfei. LDA-based Opinion Spam Discovering[J].Journal of Chinese Information Processing,2011,25(1):41-47.)
[11] 韩晓晖,马军,邵海敏,等.一种基于LDA的Web论坛低质量回帖检测方法[J]. 计算机研究与发展 ,2012,49(9):1937-1946.(Han Xiaohui, Ma Jun, Shao Haimin, et al. An LDA Based Approach to Detect the Low-Quality Reply Posts in Web Forums[J].Journal of Computer Research and Development, 2012,49(9):1937-1946.)
[12] Heinrich G. Parameter Estimation for Text Analysis[R].2005.
[13] Peters G W, Sisson S A. Bayesian Inference, Monte Carlo Sampling and Operational Risk[J]. Journal of Operational Risk, 2006(2):69-104.
[14] Kullback S. Information Theory and Statistics[M].New York: John Wiley and Sons,1959.
[15] 王满,徐朝军.网络课程资源自动量化评价研究[J]. 现代图书情报技术 , 2010(1):88-93.(Wang Man, Xu Chaojun. Study on Automatic Quantitative Evaluation of Web Course Resources[J].New Technology of Library and Information Service,2010(1):88-93.)
[1] Lixin Xia,Jieyan Zeng,Chongwu Bi,Guanghui Ye. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
[2] Qingtian Zeng,Xiaohui Hu,Chao Li. Extracting Keywords with Topic Embedding and Network Structure Analysis[J]. 数据分析与知识发现, 2019, 3(7): 52-60.
[3] Peng Guan,Yuefen Wang,Zhu Fu. Analyzing Topic Semantic Evolution with LDA: Case Study of Lithium Ion Batteries[J]. 数据分析与知识发现, 2019, 3(7): 61-72.
[4] Liqing Qiu,Wei Jia,Xin Fan. Influence Maximization Algorithm Based on Overlapping Community[J]. 数据分析与知识发现, 2019, 3(7): 94-102.
[5] Bengong Yu,Yangnan Chen,Ying Yang. Classifying Short Text Complaints with nBD-SVM Model[J]. 数据分析与知识发现, 2019, 3(5): 77-85.
[6] Xiaolan Wu,Chengzhi Zhang. Analysis of Knowledge Flow Based on Academic Social Networks:
A Case Study of
[J]. 数据分析与知识发现, 2019, 3(4): 107-116.
[7] Peiyao Zhang,Dongsu Liu. Topic Evolutionary Analysis of Short Text Based on Word Vector and BTM[J]. 数据分析与知识发现, 2019, 3(3): 95-101.
[8] Linna Xi,Yongxiang Dou. Examining Reposts of Micro-bloggers with Planned Behavior Theory[J]. 数据分析与知识发现, 2019, 3(2): 13-20.
[9] Jie Zhang,Junbo Zhao,Dongsheng Zhai,Ningning Sun. Patent Technology Analysis of Microalgae Biofuel Industrial Chain Based on Topic Model[J]. 数据分析与知识发现, 2019, 3(2): 52-64.
[10] Junwan Liu,Zhixin Long,Feifei Wang. Finding Collaboration Opportunities from Emerging Issues with LDA Topic Model and Link Prediction[J]. 数据分析与知识发现, 2019, 3(1): 104-117.
[11] Guijun Yang,Xue Xu,Fuqiang Zhao. Predicting User Ratings with XGBoost Algorithm[J]. 数据分析与知识发现, 2019, 3(1): 118-126.
[12] Yue He,Yue Feng,Shupeng Zhao,Yufeng Ma. Recommending Contents Based on Zhihu Q&A Community: Case Study of Logistics Topics[J]. 数据分析与知识发现, 2018, 2(9): 42-49.
[13] Tao Zhang,Haiqun Ma. Clustering Policy Texts Based on LDA Topic Model[J]. 数据分析与知识发现, 2018, 2(9): 59-65.
[14] Yanhua Xu,Yujie Miao,Lin Miao,Xueqiang Lv. Generating HSK Writing Essays with LDA Model[J]. 数据分析与知识发现, 2018, 2(9): 80-87.
[15] Jiehua Wu,Jing Shen,Bei Zhou. Classifying Multilayer Social Network Links Based on Transfer Component Analysis[J]. 数据分析与知识发现, 2018, 2(9): 88-99.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938