Clustering scientific documents with topic modeling

Chyi-Kwei Yau, Alan Porter, Nils Newman and Arho Suominen ()
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Chyi-Kwei Yau: Georgia Tech
Alan Porter: Georgia Tech
Nils Newman: IISC
Arho Suominen: VTT Technical Research Centre of Finland, Innovations, Economy, and Policy

Scientometrics, 2014, vol. 100, issue 3, No 11, 767-786

Abstract: Abstract Topic modeling is a type of statistical model for discovering the latent “topics” that occur in a collection of documents through machine learning. Currently, latent Dirichlet allocation (LDA) is a popular and common modeling approach. In this paper, we investigate methods, including LDA and its extensions, for separating a set of scientific publications into several clusters. To evaluate the results, we generate a collection of documents that contain academic papers from several different fields and see whether papers in the same field will be clustered together. We explore potential scientometric applications of such text analysis capabilities.

Keywords: Topic modeling; Text analysis; Atent dirichlet allocation (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (42)

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DOI: 10.1007/s11192-014-1321-8

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