Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation

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2009-01-01

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Bolelli, Levent
Ertekin Bolelli, Şeyda
Giles, C. Lee

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Algorithms that enable the process of automatically mining distinct topics in document collections have become, increasingly important clue 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 call effectively detect, distinct; topics and their evolution over time.

Subject Keywords

Digital library, Document collection, Latent dirichlet allocation, Time segment, Multinomial distribution

URI

https://hdl.handle.net/11511/48299

DOI

https://doi.org/10.1007/978-3-642-00958-7_84

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

L. Bolelli, Ş. Ertekin Bolelli, and C. L. Giles, “Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation,” 2009, vol. 5478, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48299.