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

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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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Notes

  1. https://github.com/apache/echarts.

  2. JASIST changed its name from Journal of the American Society for Information Science and Technology to Journal of the Association for Information Science and Technology in 2014.

  3. VantagePoint is a text mining visualization software for bibliometric data (such as scientific paper patents and academic project applications). Please visit the website for detail: www.thevantagepoint.com.

  4. https://mrvar.fdv.uni-lj.si/pajek/.

  5. https://www.vosviewer.com/.

  6. https://github.com/apache/echarts.

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Acknowledgements

This work was supported by the National Nature Science Foundation of China Funds [Grant No. 71774013]. Yi Zhang acknowledges supports from the Australian Research Council under Discovery Early Career Researcher Award DE190100994.

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, China

    Lu Huang, Xiang Chen, Changtian Wang, Xiaoli Cao & Jiarun Liu

  2. Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia

    Yi Zhang

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  1. Lu Huang
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Correspondence to Xiang Chen.

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Huang, L., Chen, X., Zhang, Y. et al. Identification of topic evolution: network analytics with piecewise linear representation and word embedding. Scientometrics 127, 5353–5383 (2022). https://doi.org/10.1007/s11192-022-04273-1

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