MuSe: The Musical Sentiment Dataset

The MuSe (Music Sentiment) dataset contains sentiment information for 90,408 songs. We computed scores for the affective dimensions of valence, dominance and arousal, based on the user-generated tags that are available for each song via Last.fm. In addition, we provide artist and title metadata as well as a Spotify ID and a MusicBrainz ID, which allow researchers to extend the dataset with further metadata, such as genre or year. Though the tags themselves cannot be included in the dataset, we include a jupyter notebook in our accompanying Github repository that demonstrates how to fetch the tags of a given song from the Last.fm API (Last.fm_API.ipynb) We further include a jupyter notebook in the same repository that demonstrates how one might enrich the dataset with audio features using different endpoints of the Spotify API using the included Spotify IDs (spotify_API.ipynb). Please note that in its current form, the dataset only contains tentative spotify IDs for a subset (around 68%) of the songs.MuSe（音乐情感，Music Sentiment）数据集包含90408首歌曲的情感标注信息。我们基于每首歌曲在Last.fm平台上的用户生成标签，计算了效价（valence）、支配性（dominance）与唤醒度（arousal）三个情感维度的得分。此外，数据集还提供了艺术家与曲目元数据，以及Spotify标识符（Spotify ID）和MusicBrainz标识符（MusicBrainz ID），便于研究人员基于这些标识进一步扩展数据集，例如补充音乐流派、发行年份等额外元数据。 尽管原始标签本身无法纳入本数据集，但我们在配套的GitHub仓库中提供了一份Jupyter Notebook（Last.fm_API.ipynb），演示了如何通过Last.fm应用程序编程接口（Last.fm API）获取指定歌曲的用户标签。该仓库中还提供了另一份Jupyter Notebook（spotify_API.ipynb），展示了如何利用数据集附带的Spotify标识符，通过Spotify API的不同接口为数据集补充音频特征。 请注意，当前版本的数据集仅为约68%的歌曲提供了暂用的Spotify标识符。