TY - GEN
T1 - Topic and trend detection in text collections using latent dirichlet allocation
AU - Bolelli, Levent
AU - Ertekin, Şeyda
AU - Giles, C. Lee
PY - 2009
Y1 - 2009
N2 - Algorithms that enable the process of automatically mining distinct topics in document collections have become increasingly important due to their applications in many fields and the extensive growth of the number of documents in various domains. In this paper, we propose a generative model based on latent Dirichlet allocation that integrates the temporal ordering of the documents into the generative process in an iterative fashion. The document collection is divided into time segments where the discovered topics in each segment is propagated to influence the topic discovery in the subsequent time segments. Our experimental results on a collection of academic papers from CiteSeer repository show that segmented topic model can effectively detect distinct topics and their evolution over time.
AB - Algorithms that enable the process of automatically mining distinct topics in document collections have become increasingly important due to their applications in many fields and the extensive growth of the number of documents in various domains. In this paper, we propose a generative model based on latent Dirichlet allocation that integrates the temporal ordering of the documents into the generative process in an iterative fashion. The document collection is divided into time segments where the discovered topics in each segment is propagated to influence the topic discovery in the subsequent time segments. Our experimental results on a collection of academic papers from CiteSeer repository show that segmented topic model can effectively detect distinct topics and their evolution over time.
UR - http://www.scopus.com/inward/record.url?scp=67650705495&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67650705495&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-00958-7_84
DO - 10.1007/978-3-642-00958-7_84
M3 - Conference contribution
AN - SCOPUS:67650705495
SN - 3642009573
SN - 9783642009570
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 776
EP - 780
BT - Advances in Information Retrieval - 31th European Conference on IR Research, ECIR 2009, Proceedings
T2 - 31th European Conference on Information Retrieval, ECIR 2009
Y2 - 6 April 2009 through 9 April 2009
ER -