Examining the Relationship Between Songs and Psychological Characteristics

Miran Pyun, Donghun Kim, Chaeyun Lim, Eunbyul Lee, Jihey Kwon, Sangyup Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Listening to music may serve both as an indicator of the listener’s emotional situation and as an actor on the listener’s emotions. In this study, we explored the relationship between music and listeners’ psychological characteristics. A total of 59 participants’ music playlists were retrieved, and the audio features of each song were extracted via Spotify API. A total of 8,936 songs were analyzed, and 13 different features were extracted. To understand listeners’ psychological characteristics, a survey was conducted. We found that anxiety and stress are closely related to instrumentalness. Also, anxiety is significantly related to liveness and loudness. This study suggests that music playlists can be an indicator of an individual’s psychological characteristics and acknowledges the possibility that music can enhance and reflect the listener’s psychological well-being.

Original languageEnglish
Title of host publicationHCI International 2020 – Late Breaking Papers
Subtitle of host publicationCognition, Learning and Games - 22nd HCI International Conference, HCII 2020, Proceedings
EditorsConstantine Stephanidis, Don Harris, Wen-Chin Li, Dylan D. Schmorrow, Cali M. Fidopiastis, Panayiotis Zaphiris, Andri Ioannou, Andri Ioannou, Xiaowen Fang, Robert A. Sottilare, Jessica Schwarz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030601270
Publication statusPublished - 2020
Event22nd International Conference on Human-Computer Interaction,HCII 2020 - Copenhagen, Denmark
Duration: 2020 Jul 192020 Jul 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12425 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Human-Computer Interaction,HCII 2020

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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