LDA lyrics topics with example songs.

Music is a fundamental element in every culture, serving as a universal means of expressing our emotions, feelings, and beliefs. This work investigates the link between our moral values and musical choices through lyrics and audio analyses. We align the psychometric scores of 1,480 participants to acoustics and lyrics features obtained from the top 5 songs of their preferred music artists from Facebook Page Likes. We employ a variety of lyric text processing techniques, including lexicon-based approaches and BERT-based embeddings, to identify each song’s narrative, moral valence, attitude, and emotions. In addition, we extract both low- and high-level audio features to comprehend the encoded information in participants’ musical choices and improve the moral inferences. We propose a Machine Learning approach and assess the predictive power of lyrical and acoustic features separately and in a multimodal framework for predicting moral values. Results indicate that lyrics and audio features from the artists people like inform us about their morality. Though the most predictive features vary per moral value, the models that utilised a combination of lyrics and audio characteristics were the most successful in predicting moral values, outperforming the models that only used basic features such as user demographics, the popularity of the artists, and the number of likes per user. Audio features boosted the accuracy in the prediction of empathy and equality compared to textual features, while the opposite happened for hierarchy and tradition, where higher prediction scores were driven by lyrical features. This demonstrates the importance of both lyrics and audio features in capturing moral values. The insights gained from our study have a broad range of potential uses, including customising the music experience to meet individual needs, music rehabilitation, or even effective communication campaign crafting.音乐是各类文化中的核心要素，亦是传递人类情绪、情感与信仰的通用载体。本研究通过歌词与音频分析，探究人类道德价值观与音乐选择之间的关联。我们将1480名受试者的心理测量得分，与从其在Facebook主页点赞的偏好音乐人前五首热门歌曲中提取的声学与歌词特征进行匹配。本研究采用多种歌词文本处理技术，包括基于词典的方法与BERT（Bidirectional Encoder Representations from Transformers）嵌入技术，以识别每首歌曲的叙事主题、道德效价、态度与情绪倾向。此外，我们同时提取低阶与高阶音频特征，以解析受试者音乐选择中蕴含的编码信息，并优化道德推断任务的性能。我们提出一种机器学习（Machine Learning）方法，分别评估歌词特征与声学特征的预测能力，并在多模态框架下测试二者联合用于道德价值观预测的效果。实验结果表明，从大众偏好的音乐人作品中提取的歌词与音频特征，能够有效反映其道德观念。尽管针对不同道德价值观的最优预测特征存在差异，但结合歌词与音频特征的模型在道德价值观预测任务中表现最佳，其性能优于仅使用用户人口统计学信息、音乐人热度及用户点赞数等基础特征的模型。相较于文本特征，音频特征能够提升共情与平等维度的预测准确率；而在等级与传统维度上则恰好相反，其预测性能提升主要依赖歌词特征。这充分体现了歌词与音频特征在捕捉道德价值观信息中的重要价值。本研究所得结论具备广泛的潜在应用场景，包括定制化音乐体验以满足个体需求、音乐康复，乃至精准设计传播活动等。