  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.
