Using Machine Learning to Uncover Latent Research Topics in Fishery Models

Shaheen Syed · Charlotte Teresa Weber

43 citations0/0 files storedReviews in Fisheries Science & Aquaculture

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Computational and Text Analysis Methods Marine and fisheries research scientometrics and bibliometrics research

Abstract

Modeling has become the most commonly used method in fisheries science, with numerous types of models and approaches available today. The large variety of models and the overwhelming amount of scientific literature published yearly can make it difficult to effectively access and use the output of fisheries modeling publications. In particular, the underlying topic of an article cannot always be detected using keyword searches. As a consequence, identifying the developments and trends within fisheries modeling research can be challenging and time-consuming. This paper utilizes a machine learning algorithm to uncover hidden topics and subtopics from peer-reviewed fisheries modeling publications and identifies temporal trends using 22,236 full-text articles extracted from 13 top-tier fisheries journals from 1990 to 2016. Two modeling topics were discovered: estimation models (a topic that contains the idea of catch, effort, and abundance estimation) and stock assessment models (a topic on the assessment of the current state of a fishery and future projections of fish stock responses and management effects). The underlying modeling subtopics show a change in the research focus of modeling publications over the last 26 years.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 43 citations exist upstream.

References

2013Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding 2010The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index 2009How to Analyze Political Attention with Minimal Assumptions and Costs 2010Latent Semantic Analysis: five methodological recommendations 2016Text Mining for Information Systems Researchers: An Annotated Topic Modeling Tutorial

Cited by

2020Less Fragmented Than We Thought? Toward Clarification of a Subdisciplinary Linkage in Communication Science, 2010–2019 2019Mapping the global network of fisheries science collaboration 2021Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis 2021Unearthing trends in environmental science and engineering research: Insights from a probabilistic topic modeling literature analysis 2024How do reward personalization options influence the public’s willingness to participate in innovation projects? Insights from crowdfunding in Industry 5.0 2022Capturing the Complexity of COVID-19 Research: Trend Analysis in the First Two Years of the Pandemic Using a Bayesian Probabilistic Model and Machine Learning Tools

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.