Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation

Authors:
Levent Bolelli;Şeyda Ertekin;C. Lee Giles
Affiliations:
Google Inc., New York, USA NY 10011;Department of Computer Science and Engineering, Pennsylvania State University, University Park, USA 16802;College of Information Sciences and Technology, Pennsylvania State University, University Park, USA 16802
Venue:
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Year:
2009

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Abstract

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.