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Development of the lyrics-based deep learning algorithm for identifying alcohol-related words (LYDIA)
Abraham Albert Bonela1,2, Zhen He2, Dan-Anderson Luxford1
1Centre for Alcohol Policy Research, La Trobe University, Melbourne, Australia.
Alcohol and Alcoholism (Oxford, Oxfordshire)
|January 18, 2024
Summary
A new deep learning algorithm, LYDIA, can automatically detect alcohol-related content in song lyrics. This tool helps analyze music
Area of Science:
- Computational Linguistics
- Musicology
- Public Health
Background:
- Music frequently features alcohol-related lyrics, influencing listener behavior.
- Manual analysis of alcohol content in song lyrics is time-consuming.
- Automated detection is needed to assess music's public health impact.
Purpose of the Study:
- To develop a deep learning algorithm (LYDIA) for automatic detection of alcohol exposure in song lyrics.
- To identify the context (positive, negative, neutral) of alcohol references in music.
Main Methods:
- Compiled a dataset of 6,110 Billboard top-100 song lyrics (1959-2020).
- Created an annotation tool to identify 673 alcohol-related terms and their context.
- Trained a deep learning model (LYDIA) on the annotated lyrics.
Main Results:
- LYDIA achieved 86.6% accuracy in identifying alcohol-related words.
- The algorithm reached 72.9% accuracy in determining the context of alcohol references.
- LYDIA demonstrated high accuracy (97.24% and 98.37%) in distinguishing positive/negative alcohol relations and contexts.
Conclusions:
- LYDIA offers an efficient method for analyzing alcohol content across vast music libraries.
- Automated lyric analysis can enhance awareness of alcohol's presence in music.
- This technology can support public health initiatives by quantifying music's alcohol exposure.
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