Abstract
Music, and the way it is composed, listened to, and even advertised and sold, has always been tightly connected to the psychological states it evokes. Already in 1921, the early ages of record music marketing, Thomas A. Edison collected data regarding the changes in the emotional states of music listeners. The data served as a basis to create music collections aimed at influencing the listeners' emotional states, and meant to be sold as records. Today, more than one hundred years later, large-scale music streaming platforms offer mood playlists that substantially contribute to the total number of streams. The tendency to use music to regulate one's emotional state is not only evident from the commercial choices of music distributors, it has also been backed up by scientific research. Therefore, the ability of music to evoke emotions has been a topic of active research in the field of psychology of music.
Citation
Marta
Moscati
Psychology-Inspired Machine Learning for Music: Evoked Emotion Recognition and Emotion-Aware Music Recommendation
ACM SIGWEB Newsletter,
2026(Summer):
1--6, doi:10.1145/3830455.3830458, 2026.
BibTeX
@article{Moscati2026sigweb_psychml,
title = {Psychology-Inspired Machine Learning for Music: Evoked Emotion Recognition and Emotion-Aware Music Recommendation},
author = {Moscati, Marta},
journal = {ACM SIGWEB Newsletter},
publisher = {Association for Computing Machinery (ACM)},
doi = {10.1145/3830455.3830458},
url = {https://doi.org/10.1145/3830455.3830458},
volume = {2026},
number = {Summer},
pages = {1--6},
month = {jul},
year = {2026}
}