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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation
verfasst von : Levent Bolelli, Şeyda Ertekin, C. Lee Giles
Erschienen in: Advances in Information Retrieval
Verlag: Springer Berlin Heidelberg
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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.