ArticlePloS one2021
Latent Dirichlet allocation model for world trade analysis.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Analysis and prediction of cardiovascular research hotspots, trends and interdisciplinarity.Heart (British Cardiac Society) · 2026Article
- Analysis and prediction of research hotspots and trends in heart failure research.Journal of translational internal medicine · 2024Article
- Analysis and prediction of research hotspots and trends in pediatric medicine from 2,580,642 studies published between 1940 and 2021.World journal of pediatrics : WJP · 2023Article
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
- Intersectional inequalities in science.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Identifying Topics and Evolutionary Trends of Literature on Brain Metastases Using Latent Dirichlet Allocation.Frontiers in molecular biosciences · 2022Article
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Authors and funding
3 authors.
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Abstract
International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. This new possibility opens a research gap, as new, data-driven, ways of understanding international trade, can help our understanding of the underlying phenomena. The present paper shows the application of the Latent Dirichlet allocation model, a well known technique in the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries' exports of goods from 1962 to 2016. The results show that this technique can encode the main specialisation patterns of international trade. On the country-level analysis, the findings show the changes in the specialisation patterns of countries over time. As traditional international trade analysis demands expert knowledge on a multiplicity of indicators, the possibility of encoding multiple known phenomena under a unique indicator is a powerful complement for traditional tools, as it allows top-down data-driven studies.
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