TY - GEN
T1 - LPTA
T2 - 11th IEEE International Conference on Data Mining, ICDM 2011
AU - Yin, Zhijun
AU - Cao, Liangliang
AU - Han, Jiawei
AU - Zhai, Chengxiang
AU - Huang, Thomas
PY - 2011
Y1 - 2011
N2 - This paper studies the problem of latent periodic topic analysis from timestamped documents. The examples of timestamped documents include news articles, sales records, financial reports, TV programs, and more recently, posts from social media websites such as Flickr, Twitter, and Facebook. Different from detecting periodic patterns in traditional time series database, we discover the topics of coherent semantics and periodic characteristics where a topic is represented by a distribution of words. We propose a model called LPTA (Latent Periodic Topic Analysis) that exploits the periodicity of the terms as well as term co-occurrences. To show the effectiveness of our model, we collect several representative datasets including Seminar, DBLP and Flickr. The results show that our model can discover the latent periodic topics effectively and leverage the information from both text and time well.
AB - This paper studies the problem of latent periodic topic analysis from timestamped documents. The examples of timestamped documents include news articles, sales records, financial reports, TV programs, and more recently, posts from social media websites such as Flickr, Twitter, and Facebook. Different from detecting periodic patterns in traditional time series database, we discover the topics of coherent semantics and periodic characteristics where a topic is represented by a distribution of words. We propose a model called LPTA (Latent Periodic Topic Analysis) that exploits the periodicity of the terms as well as term co-occurrences. To show the effectiveness of our model, we collect several representative datasets including Seminar, DBLP and Flickr. The results show that our model can discover the latent periodic topics effectively and leverage the information from both text and time well.
KW - Periodic topics
KW - Topic modeling
UR - http://www.scopus.com/inward/record.url?scp=84857169805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84857169805&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2011.96
DO - 10.1109/ICDM.2011.96
M3 - Conference contribution
AN - SCOPUS:84857169805
SN - 9780769544083
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 904
EP - 913
BT - Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Y2 - 11 December 2011 through 14 December 2011
ER -