论文标题
“不仅仅是单词”：通过歌词链接音乐偏好和道德价值观
"More Than Words": Linking Music Preferences and Moral Values Through Lyrics
论文作者
论文摘要
这项研究通过将文本分析技术应用于歌词来探讨音乐偏好与道德价值观之间的关联。从Facebook主持的应用程序中收集数据，我们将1,386个用户的心理测量评分与来自Facebook页面喜欢的前5首歌曲的歌词相提并论。我们提取了与每首歌的总体叙事，道德价，情感和情感有关的一组抒情特征。机器学习框架旨在利用回归方法，并评估抒情特征的预测能力来推断道德价值。结果表明，艺术家顶级歌曲的歌词喜欢告知他们的道德。层次结构和传统的优点比移情和平等的值（$ .08 \ leq r \ leq .11 $）获得更高的预测分数（$ .20 \ leq r \ leq .30 $），而基本的人口统计学变量仅占模型可解释性的一小部分。这表明了音乐听力行为的重要性，如通过抒情偏好所评估，仅在捕获道德价值方面就进行了评估。我们讨论技术和音乐的含义以及未来可能的改进。
This study explores the association between music preferences and moral values by applying text analysis techniques to lyrics. Harvesting data from a Facebook-hosted application, we align psychometric scores of 1,386 users to lyrics from the top 5 songs of their preferred music artists as emerged from Facebook Page Likes. We extract a set of lyrical features related to each song's overarching narrative, moral valence, sentiment, and emotion. A machine learning framework was designed to exploit regression approaches and evaluate the predictive power of lyrical features for inferring moral values. Results suggest that lyrics from top songs of artists people like inform their morality. Virtues of hierarchy and tradition achieve higher prediction scores ($.20 \leq r \leq .30$) than values of empathy and equality ($.08 \leq r \leq .11$), while basic demographic variables only account for a small part in the models' explainability. This shows the importance of music listening behaviours, as assessed via lyrical preferences, alone in capturing moral values. We discuss the technological and musicological implications and possible future improvements.
