Analysis of music data from allmusic.com website

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Analysis of music data from allmusic.com website
Analysis of music data from
allmusic.com website
Krzysztof Borowski, Łukasz Jackowski
What is
?
● online music guide service website
● wide range of data
○ basic (names, genres)
○ descriptive content (styles, moods, themes)
○ relational content (similar artists, influences)
○ editorial content (biographies, reviews)
● the biggest digital archive of music (over 6 mln)
Our aims
● focusing on artists, relations between them and
particular genres they represent
○ how does similarity of artists affect theirs genres ?
○ the more similar the more influential?
● creating crawler to extract data from webpage
Technologies used
● Crawler
○ Scala
○ Akka
● Data statistics
○ Neo4j
● Graph analysis
○ Gephi
General statistics after crawling phase
General statistics after crawling phase
Database structure
Similar artists’
commodity groups
Artists’ commodity groups
and genres
Most influential
artists
Artists who have
the largest number
of artists “similar”
to them.
Results and future work
Done:
● crawler with graph database
● big amount of collected data
● analysed and visualised relations between artists
Future work:
● connecting with other services (eg. last.fm or spotify) to
analyse users’ preferences

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