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Clustering scientific documents with topic modeling

Authors:

Arho SuominenAuthors Info & Claims

Scientometrics, Volume 100, Issue 3

Pages 767 - 786

https://doi.org/10.1007/s11192-014-1321-8

Published: 01 September 2014 Publication History

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.

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cover image Scientometrics

Scientometrics Volume 100, Issue 3

September 2014

183 pages

ISSN:0138-9130

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Copyright © Copyright © 2014 Akadémiai Kiadó, Budapest, Hungary.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2014

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Cited By

Musso MArnold KNanni FCannelli B(2024)What Is in a ? Cross-lingual Topic Detection & Information Retrieval in Archives Portal EuropeJournal on Computing and Cultural Heritage 10.1145/349457217:2(1-23)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3494572
Wu MZhang YMarkley MCassidy CNewman NPorter A(2024)COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solutionScientometrics10.1007/s11192-023-04747-w129:11(7229-7259)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11192-023-04747-w
Alrizq MAlghamdi A(2024)Customer satisfaction analysis with Saudi Arabia mobile banking apps: a hybrid approach using text mining and predictive learning techniquesNeural Computing and Applications10.1007/s00521-023-09400-436:11(6005-6023)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00521-023-09400-4
Huang XZhu PChen YMa J(2023)A transfer learning approach to interdisciplinary document classification with keyword-based explanationScientometrics10.1007/s11192-023-04825-z128:12(6449-6469)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11192-023-04825-z
Shausan AVuorinen A(2023)Thirty-six years of contributions to queueing systems: a content analysis, topic modeling, and graph-based exploration of research published in the QUESTA journalQueueing Systems: Theory and Applications10.1007/s11134-023-09876-w104:1-2(3-18)Online publication date: 2-Jun-2023
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Zielinski A(2022)Impact of model settings on the text-based Rao diversity indexScientometrics10.1007/s11192-022-04312-x127:12(7751-7768)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11192-022-04312-x
Huang LChen XZhang YWang CCao XLiu J(2022)Identification of topic evolution: network analytics with piecewise linear representation and word embeddingScientometrics10.1007/s11192-022-04273-1127:9(5353-5383)Online publication date: 1-Sep-2022
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Principe Vde Souza Vale Rde Castro JCarvano LHenriques Rde Almeida e Sousa Lobo Vde Alkmim Moreira Nunes R(2022)A computational literature review of football performance analysis through probabilistic topic modelingArtificial Intelligence Review10.1007/s10462-021-09998-855:2(1351-1371)Online publication date: 1-Feb-2022
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