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

Abraham Albert BonelaZhen HeDan-Anderson LuxfordBenjamin RiordanEmmanuel Kuntsche
Published in: Alcohol and alcoholism (Oxford, Oxfordshire) (2024)
LYDIA can automatically identify alcohol exposure and its context in song lyrics, which will allow for the swift analysis of future lyrics and can be used to help raise awareness about the amount of alcohol in music. Highlights Developed a deep learning algorithm (LYDIA) to identify alcohol words in songs. LYDIA achieved an accuracy of 86.6% in identifying alcohol-relation of the words. LYDIA's accuracy in identifying positive, negative, or neutral context was 72.9%. LYDIA can automatically provide evidence of alcohol in millions of songs. This can raise awareness of harms of listening to songs with alcohol words.
Keyphrases
  • deep learning
  • alcohol consumption
  • machine learning
  • convolutional neural network
  • drug induced