开发基于歌词的深度学习算法,用于识别与酒精有关的单词 (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
概括
一个新的深度学习算法LYDIA可以在歌词中自动检测与酒精有关的内容. 这个工具有助于分析音乐.
科学领域:
- 计算语言学 计算语言学
- 音乐学 音乐学 音乐学
- 公共卫生 公共卫生
背景情况:
- 音乐经常包含与酒精有关的歌词,影响听众的行为.
- 手动分析歌曲歌词中的酒精含量是耗时的.
- 需要自动检测来评估音乐对公共卫生的影响.
研究的目的:
- 开发一个深度学习算法 (LYDIA),用于在歌词中自动检测酒精暴露.
- 识别音乐中酒精引用的背景 (正面,负面,中性).
主要方法:
- 编制了一套数据集,包括6,110首Billboard前100名歌词 (1959-2020年).
- 创建了一个注释工具来识别673个与酒精有关的术语及其上下文.
- 在注释的歌词上训练了一个深度学习模型 (LYDIA).
主要成果:
- 在识别与酒精有关的单词时,LYDIA的准确率达到了86.6%.
- 该算法在确定酒精引用的上下文时达到72.9%的准确性.
- 在区分积极/消极的酒精关系和背景方面,LYDIA表现出高准确度 (97.24%和98.37%).
结论:
- LYDIA提供了一种有效的方法来分析广大的音乐库中的酒精含量.
- 自动化歌词分析可以提高对音乐中酒精存在的认识.
- 这项技术可以通过量化音乐的酒精暴露来支持公共卫生倡议.
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