Time Series Topic Modeling and Bursty Topic Detection of Correlated News and Twitter

6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference

Volumn , Issue , 2013, Pages 917-921

  Koike, Daichi ^a   Takahashi, Yusuke ^a   Utsuro, Takehito ^a   Yoshioka, Masaharu ^b   Kando, Noriko ^c  

^a UNIVERSITY OF TSUKUBA   (Japan)

^b HOKKAIDO UNIVERSITY   (Japan)

^c NATIONAL INSTITUTE OF INFORMATICS   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

TIME SERIES;

BURST DETECTION; BURSTY TOPICS; DETECTION MODELS; INFORMATION SOURCES; TEXT STREAMS; TIME SERIES NEWS; TIMES SERIES; TOPIC DETECTION; TOPIC MODELING;

SOCIAL NETWORKING (ONLINE);

EID: 84920795617     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. D. M. Blei and J. D. Lafferty. 2006. Dynamic topic models. In Proc. 23rd ICML, pages 113–120.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022.
3. Q. Diao, J. Jiang, F. Zhu, and E.-P. Lim. 2012. Finding bursty topics from microblogs. In Proc. 50th ACL, pages 536–544.
4. J. Kleinberg. 2002. Bursty and hierarchical structure in streams. In Proc. 8th SIGKDD, pages 91–101.
5. Y. Takahashi, T. Utsuro, M. Yoshioka, N. Kando, T. Fukuhara, H. Nakagawa, and Y. Kiyota. 2012. Applying a burst model to detect bursty topics in a topic model. In JapTAL 2012, volume 7614 of LNCS, pages 239–249. Springer.