Text Mining in Sustainable Manufacturing for Topic Modeling

Wai Sze Yip; Suet To; Hongting Zhou; Jingzheng Ren

doi:10.1007/978-3-031-82986-4

Text Mining in Sustainable Manufacturing for Topic Modeling

Wai Sze Yip
, Suet To (Corresponding Author)
, Hongting Zhou
, Jingzheng Ren

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research

Abstract

The primary aim of this study is to identify the prevailing topics and themes within the current body of sustainable manufacturing (SM) research. To this end, a collection of SM research works was curated from the Web of Science database to serve as the raw data for analysis. The Latent Dirichlet Allocation (LDA) method was employed to extract latent topics from the abstracts of the SM literature. The topic modeling results highlight an imbalance, with social aspects being underrepresented compared to the economic and environmental dimensions. Consequently, this study suggests that there is a need for more interdisciplinary research efforts or projects to forge stronger links between the economic dimension and other SM parameters.

Original language	English
Title of host publication	Sustainable Machining and Micro-machining
Publisher	Springer Cham
Pages	63-78
Number of pages	16
ISBN (Electronic)	978-3-031-82986-4
ISBN (Print)	978-3-031-82985-7
DOIs	https://doi.org/10.1007/978-3-031-82986-4
Publication status	Published - 22 Apr 2025

Publication series

Name	Industrial Ecology and Environmental Management (IEEM)
Volume	4

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Sustainable manufacturing
Text mining
Latent Dirichlet allocation
Jensen-Shannon divergence
Unsupervised learning

More information

10.1007/978-3-031-82986-4

Cite this

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abstract = "The primary aim of this study is to identify the prevailing topics and themes within the current body of sustainable manufacturing (SM) research. To this end, a collection of SM research works was curated from the Web of Science database to serve as the raw data for analysis. The Latent Dirichlet Allocation (LDA) method was employed to extract latent topics from the abstracts of the SM literature. The topic modeling results highlight an imbalance, with social aspects being underrepresented compared to the economic and environmental dimensions. Consequently, this study suggests that there is a need for more interdisciplinary research efforts or projects to forge stronger links between the economic dimension and other SM parameters.",

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Text Mining in Sustainable Manufacturing for Topic Modeling. / Yip, Wai Sze ; To, Suet (Corresponding Author); Zhou, Hongting et al.
Sustainable Machining and Micro-machining. Springer Cham, 2025. p. 63-78 (Industrial Ecology and Environmental Management (IEEM); Vol. 4).

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research

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N1 - The content of this chapter has been written based on the previous work of the authors Zhou et al. (2022). Thematic analysis of sustainable ultra-precision machining by using text mining and unsupervised learning method. Journal of Manufacturing Systems, 62, 218–233. Some of the figures of this chapter are reprinted from Zhou et al. (2022). Thematic analysis of sustainable ultra-precision machining by using text mining and unsupervised learning method. Journal of Manufacturing Systems, 62, 218–233, with permission from Elsevier.

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Text Mining in Sustainable Manufacturing for Topic Modeling

Abstract

Publication series

UN SDGs

Keywords

More information

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Cite this