Charlotte Weber and Shaheen Syed publish a paper in Reviews in Fisheries Science and Aquaculture.

  • Using Machine Learning to Uncover Latent Research Topics in Fishery Models

Our ESRs Charlotte Weber from UiT and Shaheen Syed from MMU/UU have published their paper entitled “Using Machine Learning to Uncover Latent Research Topics in Fishery Models” in the journal Reviews in Fisheries Science and Aquaculture.

During their collaboration (blog post), they utilized an unsupervised machine learning method called Latent Dirichlet Allocation to automatically uncover topics from a dataset comprised of 22,000 fisheries research articles. Their analyses looked into topics related to fisheries models and the subsequent sub-topics found therein. By looking at these sub-topics they have explored temporal trends and identified common names of geographical areas, names of authors and species.

The paper is available open access:
http://www.tandfonline.com/doi/full/10.1080/23308249.2017.1416331

or can be downloaded from here:
Using Machine Learning to Uncover Latent Research Topics in Fishery Models

Syed, S., & Weber, C. T. (2018). Using Machine Learning to Uncover Latent Research Topics in Fishery Models. Reviews in Fisheries Science & Aquaculture, 26:3, 319-336. https://doi.org/10.1080/23308249.2017.1416331

2018-04-23T14:12:26+00:00 January 17th, 2018|