Mood metadata on Chinese music websites: an exploratory study with user feedback

Xiao Hu, Christy W.L. Cheong, Siwei Zhang, J. Stephen Downie

Research output: Contribution to journalArticlepeer-review


Purpose: Music mood is an important metadata type on online music repositories and stream music services worldwide. Many existing studies on mood metadata have focused on music websites and services in the Western world to the exclusion of those serving users in other cultures. The purpose of this paper is to bridge this gap by exploring mood labels on influential Chinese music websites. Design/methodology/approach: Mood labels and the associated song titles were collected from six Chinese music websites, and analyzed in relation to mood models and findings in the literature. An online music listening test was conducted to solicit users’ feedback on the mood labels on two popular Chinese music websites. Mood label selections on 30 songs from 64 Chinese listeners were collected and compared to those given by the two websites. Findings: Mood labels, although extensively employed on Chinese music websites, may be insufficient in meeting listeners’ needs. More mood labels of high arousal semantics are needed. Song languages and user familiarity to the songs show influence on users’ selection of mood labels given by the websites. Practical implications: Suggestions are proposed for future development of mood metadata and mood-enabled user interfaces in the context of global online music access. Originality/value: This paper provides insights on understanding the mood metadata on Chinese music websites and uniquely contributes to existing knowledge of culturally diversified music access.

Original languageEnglish (US)
Pages (from-to)864-879
Number of pages16
JournalOnline Information Review
Issue number6
StatePublished - Sep 13 2018


  • Chinese music website
  • Global music access
  • Mood metadata
  • Music mood
  • Online music access
  • Online music repositories

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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