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In this paper we propose the use of the Word2vec algorithm in order to obtainodor perception embeddings (or smell embeddings), only using publicly availableperfume descriptions. Besides showing meaningful similarity relationships amongeach other, these embeddings also demonstrate to possess some sharedinformation with their respective word embeddings. The meaningfulness of theseembeddings suggests that aesthetics might provide enough constraints for usingalgorithms motivated by distributional semantics on non-randomly combined data.Furthermore, they provide possibilities for new ways of classifying odors andanalyzing perfumes. We have also employed the embeddings in an attempt tounderstand the aesthetic nature of perfumes, based on the difference betweenreal and randomly generated perfumes. In an additional tentative experiment weexplore the possibility of a mapping between the word embedding space and theodor perception embedding space by fitting a regressor on the shared vocabularyand then predict the odor perception embeddings of words without an a prioriassociated smell, such as night or sky.

From meaning to perception -- exploring the space between word and odor perception embeddings