开发基于歌词的深度学习算法,用于识别酒精相关词汇(LYDIA)。

IF 2.1 4区 医学 Q3 SUBSTANCE ABUSE
Abraham Albert Bonela, Zhen He, Dan-Anderson Luxford, Benjamin Riordan, Emmanuel Kuntsche
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引用次数: 0

摘要

背景:音乐是我们生活中不可或缺的一部分,而且经常在餐厅等公共场所播放。在酒吧场景中,接触到含有酒精相关歌词的音乐的人比接触到酒精相关歌词较少的音乐的人饮酒量要高得多。现有的量化歌词中酒精暴露的方法都是采用人工注释,既繁琐又耗时。在本文中,我们旨在建立一种深度学习算法(LYDIA),它可以自动检测和识别歌词中的酒精暴露及其上下文:我们识别了 673 个可能与酒精有关的词汇,包括品牌名称、城市俚语和饮料名称。我们收集了从 1959 年到 2020 年公告牌百强歌曲中的所有歌词(N = 6110)。我们开发了一个注释工具,用于注释歌词中与酒精相关的单词(酒精、非酒精或不确定)以及该单词的上下文(正面、负面或中性):LYDIA 识别歌词中与酒精相关词语的准确率为 86.6%,识别歌词上下文的准确率为 72.9%。LYDIA 对与酒精有正面和负面关系的词语的识别准确率为 97.24%,对正面和负面语境的识别准确率为 98.37%:LYDIA可以自动识别歌词中的酒精暴露及其上下文,这将有助于对未来的歌词进行快速分析,并可用于帮助提高人们对音乐中酒精含量的认识。亮点 开发了一种深度学习算法(LYDIA)来识别歌曲中的酒精词汇。LYDIA 识别歌词中酒精相关词的准确率达到 86.6%。LYDIA 识别正面、负面或中性语境的准确率为 72.9%。LYDIA 可以自动提供数百万首歌曲中的酒精证据。这可以提高人们对聆听含酒精词语歌曲危害的认识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of the lyrics-based deep learning algorithm for identifying alcohol-related words (LYDIA).

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.

Methods: We identified 673 potentially alcohol-related words including brand names, urban slang, and beverage names. We collected all the lyrics from the Billboard's top-100 songs from 1959 to 2020 (N = 6110). We developed an annotation tool to annotate both the alcohol-relation of the word (alcohol, non-alcohol, or unsure) and the context (positive, negative, or neutral) of the word in the song lyrics.

Results: LYDIA achieved an accuracy of 86.6% in identifying the alcohol-relation of the word, and 72.9% in identifying its context. LYDIA can distinguish with an accuracy of 97.24% between the words that have positive and negative relation to alcohol; and with an accuracy of 98.37% between the positive and negative context.

Conclusion: 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.

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来源期刊
Alcohol and alcoholism
Alcohol and alcoholism 医学-药物滥用
CiteScore
4.70
自引率
3.60%
发文量
62
审稿时长
4-8 weeks
期刊介绍: About the Journal Alcohol and Alcoholism publishes papers on the biomedical, psychological, and sociological aspects of alcoholism and alcohol research, provided that they make a new and significant contribution to knowledge in the field. Papers include new results obtained experimentally, descriptions of new experimental (including clinical) methods of importance to the field of alcohol research and treatment, or new interpretations of existing results. Theoretical contributions are considered equally with papers dealing with experimental work provided that such theoretical contributions are not of a largely speculative or philosophical nature.
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