  In this paper we propose the use of the Word2vec algorithm in order to obtain
odor perception embeddings (or smell embeddings), only using publicly available
perfume descriptions. Besides showing meaningful similarity relationships among
each other, these embeddings also demonstrate to possess some shared
information with their respective word embeddings. The meaningfulness of these
embeddings suggests that aesthetics might provide enough constraints for using
algorithms motivated by distributional semantics on non-randomly combined data.
Furthermore, they provide possibilities for new ways of classifying odors and
analyzing perfumes. We have also employed the embeddings in an attempt to
understand the aesthetic nature of perfumes, based on the difference between
real and randomly generated perfumes. In an additional tentative experiment we
explore the possibility of a mapping between the word embedding space and the
odor perception embedding space by fitting a regressor on the shared vocabulary
and then predict the odor perception embeddings of words without an a priori
associated smell, such as night or sky.
