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.
Yin, Zhijun, Liangliang Cao, Jiawei Han, Cheng Xiang Zhai, and Thomas Huang. "Lpta: A probabilistic model for latent periodic topic analysis." In Data Mining (ICDM), 2011 IEEE 11th International Conference on, pp. 904-913. IEEE, 2011. [link]