  The international trade is one of the classic areas of study in economics.
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. The present paper shows the application of the
Latent Dirichlet Allocation Models, a well known technique from 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 findings show the possibility to generate higher level
classifications of goods based on the empirical evidence, and also allow to
study the distribution of those classifications within countries. The latter
show interesting insights about countries' trade specialisation.
