通过共同利益群体揭示音乐流派结构
Unveiling music genre structure through common-interest communities
摘要：
我们使用了一个由9,000多名用户在15年内撰写的90,000多篇金属音乐评论的数据集，在评论文本信息的帮助下分析了金属音乐的流派结构。我们使用面向用户的网络，基于书面评论的体裁之间的关系建模。然后，我们进行社区发现，并采用网络“平均”的方法，以获得稳定的体裁集群，以分析集群的结构都在本地每个集群和全球范围内的整个网络。除了识别聚类外，我们还使用依存句法分析和改进的词频-逆文档频率来提取每个聚类的重要和独特的特征。这些结构和评论文本信息可以让我们了解音乐观众（粉丝）如何感知相似和不同的流派，并有助于对共享共同兴趣的用户社区的不同流派进行分类，从而为音乐流派的分组提供更客观的方法。此外，分类还可以帮助推荐引擎提供更有针对性的音乐建议，并潜在地帮助音乐家为他们的音乐选择流派标签，并基于先前的评论设计音乐以更好地迎合他们的观众的偏好。
英文摘要：
Using a dataset of more than 90,000 metal music reviews written by over 9,000 users in a period of 15 years, we analyse the genre structure of metal music with the aid of review text information. We model the relationships between genres using a user-oriented network, based on the written reviews. We then perform community detection and employ a network "averaging" method to obtain stable genre clusters, in order to analyse the structures of clusters both locally within each cluster and globally over the entire network. In addition to identifying the clusters, we use Dependency Parsing and modified Term Frequency - Inverse Document Frequency to extract significant and unique features of each cluster. These structures and review text information can allow us to understand how music audience (fans) perceive similar and different genres, and also assist in classifying different genres which share common-interest user communities, offering a more objective way in grouping music genres. Furthermore, the classification can also help recommendation engines provide more targeted suggestions of music, and potentially help musicians to select genre labels for their music, and design music to better cater to preferences of their audiences based on previous reviews.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
