Benetos Emmanouil, Ragano Alessandro, Sgroi Daniel, Tuckwell Anthony
Queen Mary University of London, London, England, UK.
The Alan Turing Institute, London, England, UK.
Behav Res Methods. 2022 Dec;54(6):3085-3092. doi: 10.3758/s13428-021-01747-7. Epub 2022 Feb 25.
We propose a new measure of national valence based on the emotional content of a country's most popular songs. We first trained a machine learning model using 191 different audio features embedded within music and use this model to construct a long-run valence index for the UK. This index correlates strongly and significantly with survey-based life satisfaction and outperforms an equivalent text-based measure. Our methods have the potential to be applied widely and to provide a solution to the severe lack of historical time-series data on psychological well-being.
我们基于一个国家最流行歌曲的情感内容，提出了一种衡量国民情绪效价的新方法。我们首先使用音乐中嵌入的191种不同音频特征训练了一个机器学习模型，并使用该模型构建了英国的长期情绪效价指数。该指数与基于调查的生活满意度密切相关且具有显著相关性，并且优于同等的基于文本的衡量方法。我们的方法有广泛应用的潜力，并能为心理健康方面历史时间序列数据严重匮乏的问题提供解决方案。
