We’re going to be presenting our work on Valuing Electronic Music at the Digital Music Research Network (DMRN) workshop at Queen Mary, University of London, on Tuesday 16th December. This is the 9th running of this popular workshop and we’re very pleased to be able to discuss our work with this audience.
Sometimes we just want to get a simple overview of the types of things people are saying. In the case of our SoundCloud analysis, we want to know what people are saying about each other’s tracks.
We’ve made use of http://www.wordle.net/ word clouds to get an overview: what words are people typically using in comments on SoundCloud? Are they positive? descriptive? critical? irrelevant?
We’ve just presented the Valuing Electronic Music project at the 5th International Conference on Computational Creativity, in Ljubljana, Slovenia.
The talk was streamed live and is now available as a video:
We have been exploring how visualisations can illustrate over time how users comment on tracks in SoundCloud. Commenting has been highlighted in our qualitative research as a way of building relationships and showing appreciation of other musicians’ work. In fact, initial inspection of a sample set of comments is showing that most comments in our samples tend to be positive or constructive, rather than overly critical or negative.
We have created two visualisations:
As >our qualitative research starts, so does our quantitative research. We have been collecting data from SoundCloud’s API (Application Programming Interface) which is the gateway to access SoundCloud’s data. We have also been updating the IF analysis code written by Daniel Allington for analysing network actions in Interactive Fiction communities, to make it useful for analysing what happens between users on SoundCloud. SoundCloud has made available an SDK (Software Development Kit) for Python and other programming languages, which is a set of functions and programs that we can use in our code to do things with SoundCloud data.