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

TL;DR

This paper uses Word2vec to create odor perception embeddings from perfume descriptions, revealing meaningful relationships and potential for new odor classification and aesthetic analysis.

Contribution

It introduces a novel application of distributional semantics to odor perception, demonstrating meaningful odor embeddings from textual data and exploring their relationship with word embeddings.

Findings

Odor perception embeddings show meaningful similarity relationships.

Embeddings share information with word embeddings.

Potential for new odor classification methods.

Abstract

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.