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research-article

Identification of topic evolution: network analytics with piecewise linear representation and word embedding

Authors:

Changtian Wang,

Jiarun LiuAuthors Info & Claims

Scientometrics, Volume 127, Issue 9

Pages 5353 - 5383

https://doi.org/10.1007/s11192-022-04273-1

Published: 01 September 2022 Publication History

Abstract

Understanding the evolutionary relationships among scientific topics and learning the evolutionary process of innovations is a crucial issue for strategic decision makers in governments, firms and funding agencies when they carry out forward-looking research activities. However, traditional co-word network analysis on topic identification cannot effectively excavate semantic relationship from the context, and fixed time window method cannot scientifically reflect the evolution process of topics. This study proposes a framework of identifying topic evolutionary pathways based on network analytics: Firstly, keyword networks are constructed, in which a piecewise linear representation method is used for dividing time periods and a Word2Vec mode is used for capturing semantics from the context of titles and abstracts; Secondly, a community detection algorithm is used to identify topics in networks; Finally, evolutionary relationships between topics are represented by measuring the topic similarity between adjacent time periods, and then topic evolutionary pathways are identified and visualized. An empirical study on information science demonstrates the reliability of the methodology, with subsequent empirical validations.

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Cited By

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Identification of topic evolution: network analytics with piecewise linear representation and word embedding

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Published In

cover image Scientometrics

Scientometrics Volume 127, Issue 9

Sep 2022

491 pages

ISSN:0138-9130

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© Akadémiai Kiadó, Budapest, Hungary 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2022

Accepted: 12 January 2022

Received: 13 March 2021

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National Natural Science Foundation of China
Australian Research Council under Discovery Early Career Researcher Award

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Cited By

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https://dl.acm.org/doi/10.1016/j.ipm.2023.103574
Gao QJiang M(2024)Exploring technology fusion by combining latent Dirichlet allocation with Doc2vec: a case of digital medicine and machine learningScientometrics10.1007/s11192-024-05069-1129:7(4043-4070)Online publication date: 1-Jul-2024
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Zhang JSun XLiu Z(2024)Measuring the evolving stage of temporal distribution of research topic keyword in scientific literature by research heat curveScientometrics10.1007/s11192-024-04937-0129:11(7287-7328)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11192-024-04937-0
Cao XChen XHuang LDeng LCai YRen H(2024)Detecting technological recombination using semantic analysis and dynamic network analysisScientometrics10.1007/s11192-023-04812-4129:11(7385-7416)Online publication date: 1-Nov-2024
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Cheng ZTang JYang JHuang Y(2024)Exploring Technology Evolution Pathways Based on Link Prediction on Multiplex Network: Illustrated as CRISPRWisdom, Well-Being, Win-Win10.1007/978-3-031-57860-1_8(105-121)Online publication date: 15-Apr-2024
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Zhang YGuo WMa JFu ZChang ZWang L(2023)Evolution analysis of cross-domain collaborative research topic: a case study of cognitive-based product conceptual designScientometrics10.1007/s11192-023-04865-5128:12(6695-6718)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s11192-023-04865-5

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