Development of the lyrics-based deep learning algorithm for identifying alcohol-related words (LYDIA).

Journal: Alcohol and alcoholism (Oxford, Oxfordshire)
PMID: 38234055

Abstract

BACKGROUND: Music is an integral part of our lives and is often played in public places like restaurants. People exposed to music that contained alcohol-related lyrics in a bar scenario consumed significantly more alcohol than those exposed to music with less alcohol-related lyrics. Existing methods to quantify alcohol exposure in song lyrics have used manual annotation that is burdensome and time intensive. In this paper, we aim to build a deep learning algorithm (LYDIA) that can automatically detect and identify alcohol exposure and its context in song lyrics.

Authors

  • Abraham Albert Bonela
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia; Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.
  • Zhen He
  • Dan-Anderson Luxford
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia.
  • Benjamin Riordan
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia.
  • Emmanuel Kuntsche
    Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia. Electronic address: e.kuntsche@latrobe.edu.au.

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