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Topic and trend detection in text collections using latent dirichlet allocation
Date
2009-01-01
Author
Bolelli, Levent
Ertekin Bolelli, Şeyda
Giles, C Lee
Metadata
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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.
Subject Keywords
Digital library
,
Document collection
,
Latent dirichlet allocation
,
Time segment
,
Multinomial distribution
URI
https://doi.org/10.1007/978-3-642-00958-7_84
https://hdl.handle.net/11511/78067
Relation
Advances in information retrieval
Collections
Department of Computer Engineering, Book / Book chapter
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L. Bolelli, Ş. Ertekin Bolelli, and C. L. Giles,
Topic and trend detection in text collections using latent dirichlet allocation
. 2009, p. 780.