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Finding Topic Trends in Digital Libraries
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Date
2009-01-01
Author
Bolelli, Levent
Ertekin Bolelli, Şeyda
Zhou, Ding
Giles, C. Lee
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We propose a generative model based on latent Dirichlet allocation for mining distinct topics in document collections by integrating the temporal ordering of documents into the generative process. 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. We conduct experiments on the collection of academic papers from Cite Seer repository. We augment the text corpus with the addition of user queries and tags and integrate the citation graph to boost the weight of the topical terms. The experiment results show that segmented topic model can effectively detect distinct topics and their evolution over time.
Subject Keywords
Information Systems
,
Information Systems Applications
,
Data Mining
URI
https://hdl.handle.net/11511/48706
DOI
https://doi.org/10.1145/1555400.1555411
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Department of Computer Engineering, Conference / Seminar
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L. Bolelli, Ş. Ertekin Bolelli, D. Zhou, and C. L. Giles, “Finding Topic Trends in Digital Libraries,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48706.