Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics"
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
An n-gram topic model for time-stamped documents
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Mining web search topics with diverse spatiotemporal patterns
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
One theme in all views: modeling consensus topics in multiple contexts
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper studies the problem of latent periodic topic analysis from time stamped documents. The examples of time stamped documents include news articles, sales records, financial reports, TV programs, and more recently, posts from social media websites such as Flickr, Twitter, and Face book. 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.