Personalized musically induced emotions of not-so-popular Colombian music

Abstract

This work presents an initial proof of concept of how Music Emotion Recognition (MER) systems could be intentionally biased with respect to annotations

of musically-induced emotions in a political context. In specific, we analyze traditional Colombian music containing politically-charged lyrics of two types: (1) vallenatos and social songs from the “left-wing” guerrilla Fuerzas Armadas Revolucionarias de Colombia (FARC) and (2) corridos from the “right-wing” paramilitaries Autodefensas Unidas de Colombia (AUC). We train personalized machine learning models to predict induced emotions for three users with diverse political views – we aim at identifying the songs that may induce negative emotions

for a particular user, such as anger and fear. To this extent, a user’s emotion judgements could be interpreted as problematizing data – subjective emotional judgments

could in turn be used to influence the user in a human-centered machine learning environment. In short, highly desired “emotion regulation” applications could potentially deviate to “emotion manipulation” – the recent discredit of emotion recognition technologies might transcend ethical issues of diversity and inclusion.