@inproceedings{3387b45d37464729b9b17f54079451d6,
title = "LPTA: A probabilistic model for latent periodic topic analysis",
abstract = "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.",
keywords = "Periodic topics, Topic modeling",
author = "Zhijun Yin and Liangliang Cao and Jiawei Han and Chengxiang Zhai and Thomas Huang",
year = "2011",
doi = "10.1109/ICDM.2011.96",
language = "English (US)",
isbn = "9780769544083",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "904--913",
booktitle = "Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011",
address = "United States",
note = "11th IEEE International Conference on Data Mining, ICDM 2011 ; Conference date: 11-12-2011 Through 14-12-2011",
}