[1]LIU Zhixiong,JIA Caiyan.Micro-blog topic detection based on users’ interests and communities[J].CAAI Transactions on Intelligent Systems,2016,11(3):294-299.[doi:10.11992/tis.201603341]
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Micro-blog topic detection based on users’ interests and communities

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 3 Page number: 294-299 Column: 学术论文—自然语言处理与理解 Public date: 2016-06-25
Title:
Micro-blog topic detection based on users’ interests and communities
Author(s):
LIU Zhixiong1; 2;  JIA Caiyan1; 2
1. School of Computer and Information Technology, University of Beijing Jiaotong, Beijing 100044, China;
2. University of Beijing Jiaotong Beijing Key Lab of Traffic Data Analysis and Mining, Beijing 100044, China
Keywords:
microblog; community; network; text; topic; interest; noise; theme
CLC:
TP393
DOI:
10.11992/tis.201603341
Abstract:
Microblog topic detection is a special type of topic detection. The traditional topic detection algorithms do not work well in special situations for Chinese microblogs. In this paper, a topic detection method cater to the user community of microblogs is proposed. Firstly, the users’ interests were analyzed by using the LDA(Latent Dirichlet Allocation) topic model on the text of microblogs generated by users/bloggers. Then the user/follower network associated with users’ interests was created and partitioned into different communities so that the users in the same group were not only densely connected but also shared similar interests. Then, the topics of interest in each community were detected. Together, this provides a microblog topic finding method that faces a user’s community and combines the importance of words as well as an ε neighboring graph. The experimental tests show that the method can effectively eliminate microblog noise, compute the importance of words, and rapidly and accurately obtain the topics of interest of each community.
References:
[1] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. The journal of machine learning research, 2003, 3(4-5): 993-1002.
[2] VON LUXBURG U. A tutorial on spectral clustering[J]. Statistics and computing, 2007, 17(4): 395-416.
[3] 郭庆琳, 李艳梅, 唐琦. 基于VSM的文本相似度计算的研究[J]. 计算机应用研究, 2008, 25(11): 3256-3258. GUO Qinglin, Li Yanmei, TANG Qi. Similarity computing of documents based on VSM[J]. Application research of computers, 2008, 25(11): 3256-3258.
[4] 周刚, 邹鸿程, 熊小兵, 等. MB-SinglePass: 基于组合相似度的微博话题检测[J]. 计算机科学, 2012, 39(10): 198-202. ZHOU Gang, ZOU Hongcheng, XIONG Xiaobing, et al. MB-SinglePass: microblog topic detection based on combined similarity[J]. Computer science, 2012, 39(10): 198-202.
[5] 郑斐然, 苗夺谦, 张志飞, 等. 一种中文微博新闻话题检测的方法[J]. 计算机科学, 2012, 39(1): 138-141. ZHENG Feiran, MIAO Duoqian, ZHANG Zhifei, et al. News topic detection approach on Chinese microblog[J]. Computer science, 2012, 39(1): 138-141.
[6] 方然, 苗夺谦, 张志飞. 一种基于情感的中文微博话题检测方法[J]. 智能系统学报, 2013, 8(3): 208-213. FANG Ran, MIAO Duoqian, ZHANG Zhifei, et al. An emotion-based method of topic detection from Chinese microblogs[J]. CAAI transactions on intelligent systems, 2013, 8(3): 004: 208-213.
[7] Heinrich G. Parameter estimation for text analysis[R]. Technical report, Darmstadt, Germany: Fraunhofer IGD, 2004.
[8] 乔健. 面向新浪微博的链接和内容相结合的社区划分方法[D]. 北京: 北京交通大学, 2015. QIAO Jian. Community detection by using link and content and it’s application in sina microblog[D]. Beijing: Beijing Jiaotong University, 2015.
[9] JIANG Yawen, JIA Caiyan, YU Jian. An efficient community detection method based on rank centrality[J]. Physica A: statistical mechanics and its applications, 2013, 392(9): 2182-2194.
[10] PAGE L, BRIN S, MOTWANI R, et al. The PageRank citation ranking: bringing order to the Web[R]. Stanford InfoLab, 1999: 189-194.
[11] KOJIMA K. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability[J]. American journal of human genetics, 1969, 21(4): 407-408.
[12] MIHALCEA R, TARAU P. TextRank: bringing order into texts[C]//Proceedings of EMNLP 2004: association for computational linguistics. Barcelona, Spain, 2004.
[13] CHATURVEDI P, DHARA M, ARORA D. community detection in complex network via BGLL algorithm[J]. International journal of computer applications, 2012, 48(1): 32-42.
[14] ZANGHI H, VOLANT S, AMBROISE C. Clustering based on random graph model embedding vertex features[J]. Pattern recognition letters, 2010, 31(9): 830-836.
[15] XU Zhiqiang, KE Yiping, WANG Yi, et al. A model-based approach to attributed graph clustering[C]//Proceedings of the 2012 ACM SIGMOD international conference on management of data. New York, NY, USA, 2012: 505-516.
[16] NEWMAN M E J. Fast algorithm for detecting community structure in networks[J]. Physical review E, 2004, 69(6): 066133.
[17] KARYPIS G, KUMAR V. Metis-unstructured graph partitioning and sparse matrix ordering system, version 2.0[Z]. Minnesota: University of Minnesota, Department of Computer, 1995: 202-205.
[18] MIMNO D, WALLACH H M, TALLEY E, et al. Optimizing semantic coherence in topic models[C]//Proceedings of the conference on empirical methods in natural language processing. Stroudsburg, PA, USA, 2011: 262-272.
[19] HU Yanqing, LI Menghui, ZHANG Peng, et al. Community detection by signaling on complex networks[J]. Physical review E, 2008, 78(1): 016115.
[20] BURK C F, HORTON F W. Infomap: a complete guide to discovering corporate information resources[J]. Lincoln: Prentice Hall, 1988.
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