| [ 1 ] Yan Xiaohui,Guo Jiafeng,Lan Yanyan,et al. A Biterm
Topic Model for Short Texts [C] / / Proceedings of the
22nd International Conference Companion on World
Wide Web. Rio de Janeiro,Brazil:IW3C2 Press,2013:
1445-1456.
[ 2 ] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet
Allocation[J]. Journal of Machine Learning Research,
2003,3(1):993-1022.
[ 3 ] 张晨逸,孙建伶,丁轶群. 基于MB-LDA 模型的微博主
题挖掘[ J ]. 计算机研究与发展, 2011, 48 (10):
1795-1802.
[ 4 ] Zhao Xin,Jiang Jing,He Jing,et al. Comparing Twitter
and Traditional Media Using Topic Models [ C ] / /
Proceedings of the 33rd European Conference on IR
Research. Berlin, Germany: Springer-Verlag, 2011:
338-349.
[ 5 ] Hong Liangjie,Dom B,Gurumurthy S,et al. A Timedependent
Topic Model for Multiple Text Streams[C] / /
Proceedings of the 17th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining.
New York,USA:ACM Press,2011:832-840.
[ 6 ] Deerwester S C, Dumais S T, Landauer T K, et al.
Indexing by Latent Semantic Analysis [ J]. Journal of
American Society for Information Science,1990,41(6):
391-407.
[ 7 ] Griffiths T L,Steyvers M. Finding Scientific Topics[J].
National Academy of Sciences of the United States of
America,2004,101(S1):5228-5235.
[ 8 ] Minka T P,Lafferty J. Expectation-propagation for the
Generative Aspect Model [C] / / Proceeding of the 18th
Conference on Uncertainty in Artificial Intelligence.
Boston,USA:AUAI Press,2002:352-359.
[ 9 ] Blei D M,Lafferty J D. Correlated Topic Models[C] / /
Proceedings of NIPS’05. Cambridge,USA:MIT Press,
2005:147-155.
[10] Steyvers M,Smyth P,Griffiths T. Probabilistic Authortopic
Models for Information Discovery [ C ] / /
Proceedings of the 10th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining.
New York,USA:ACM Press,2004:306-315.
[11] Wang X,Mccallum A. Topics Over Time:A Non-Markov
Continuous-time Model of Topical Trends[C] / /
Proceedings of the 12th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining.
New York,USA:ACM Press,2006:424-433.
[12] Blei D M, Lafferty J. Dynamic Topic Models [ C ] / /
Proceedings of the 23rd International Conference on
Machine Learning. Pittsburgh,USA:IEEE Press,2006:
113-120.
[13] Ramage D, Dumais S, Liebling D. Characterizing
Microblogs with Topic Models[C] / / Proceedings of the
4th International AAAI Conference on Weblogs and
Social Media. Menlo Park,USA:AAAI Press,2010:130-
137.
[14] Diao Q,Jiang J,Zhu F,et al. Finding Bursty Topics from
Microblogs [ C ] / / Proceedings of the 50th Annual
Meeting of the Association for Computational
Linguistics. New York, USA: ACM Press, 2012:
536-544.
[15] Wang Y, Agichtein E, Benzi M. TM-LDA: Efficient
Online Modeling of Latent Topic Transitions in Social
Media [ C ] / / Proceedings of the 18th International
Conference on Knowledge Discovery and Data Mining.
Beijing,China:[s. n. ],2012:123-131.
[16] Khalid E A,Min X,Emily B F. Representing Documents
Through Their Readers [C] / / Proceedings of the 19th
International Conference on Knowledge Discovery and
Data Mining. New York,USA:ACM Press,2013:14-22.
[17] Cheng Z, Caverlee J, Lee K. You Are Where You
Tweet: A Content-based Approach to Geo-locating
Twitter Users [ C ] / / Proceedings of the 19th ACM
International Conference on Information and Knowledge
Management. New York, USA: ACM Press, 2010:
759-768.
[18] 王 莎,张连明. 基于标签的微博人脉网络挖掘算法
和结构分析[J]. 计算机工程,2014,40(5):7-11.
编辑 陆燕菲 |