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A Simple, interpretable method to identify surprising topic shifts in scientific fields.

ABSTRACT: This paper proposes a text-mining framework to systematically identify vanishing or newly formed topics in highly interdisciplinary and diverse fields like cognitive science. We apply topic modeling via non-negative matrix factorization to cognitive science publications before and after 2012; this allows us to study how the field has changed since the revival of neural networks in the neighboring field of AI/ML. Our proposed method represents the two distinct sets of topics in an interpretable, common vector space, and uses an entropy-based measure to quantify topical shifts. Case studies on vanishing (e.g., connectionist/symbolic AI debate) and newly emerged (e.g., art and technology) topics are presented. Our framework can be applied to any field or any historical event considered to mark a major shift in thought. Such findings can help lead to more efficient and impactful scientific discoveries.

SUBMITTER: Cheng L

PROVIDER: S-EPMC9597295 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A Simple, interpretable method to identify surprising topic shifts in scientific fields.

Cheng Lu L Foster Jacob G JG Lee Harlin H

Frontiers in research metrics and analytics 20221012

This paper proposes a text-mining framework to systematically identify vanishing or newly formed topics in highly interdisciplinary and diverse fields like cognitive science. We apply topic modeling via non-negative matrix factorization to cognitive science publications before and after 2012; this allows us to study how the field has changed since the revival of neural networks in the neighboring field of AI/ML. Our proposed method represents the two distinct sets of topics in an interpretable, ...[more]

PMID: 36312829

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