Pete Ashton, was ‘in charge’ of the twitter account @supersonicfest – this was used over the weekend to interact with the festival goers.  As well as the account, the hashtag #supersonic was used.

Over on ash10.com Pete has conducted some preliminary analysis, focussing on the numbers and proportion of tweets sent, which contained one of:

• @supersonicfest,
• #supersonic
• supersonic festival

I offered up to do some quick analysis, and thought It might be interesting to look at text analysis.

Using the service by IBM, called Many Eye’s, I uploaded the dataset that Pete provided (plain extract and on Many Eye’s) and did some very quick analysis.

A wordle, is a simple map of common words, in this particular example I’ve removed off the ‘common English words’ as well as the keywords identified above.

Supersonic Wordle

For all the above, you can click on the image and interact with them on Many Eye’s.

So, let’s see if we can see any relationships using a ‘phrase net’

Supersonic Phrase Net

I’ve not excluded any words in the above, but we can see that ‘Goblin’ still shows up as a popular word.  With phrase nets, the idea is that you can see the relationships between words.

A better way to drilldown into the patterns, is to use word trees.  In this case below, I’ve focussed on the phrases that include ‘rhubarbradio’, which covered the event (Listen Again on Rhubarb Radio).

Rhubarbradio & Supersonic Word Tree

Finally,  ignore what was actually said.  Data is about patterns, therefore I suppose that the number of tweets including #supersonic exponentially increased over the weekend. This is in the form of a steamgraph, and is drawn directly from the Twitter search engine.

Supersonic Steamgraph

The good thing about the steamgraph is that we can see some key words at the peaks, (if you click the graph you will be able to input your own search and then see the actual tweets that included the keyword)

This was a whistle stop tour of some text/trend analysis tools.  I’d highly recommend having a play on Many Eye’s yourself.  Also take a look at this post on Juice Analytics regarding text analysis.

In the second part of the analysis, I’ll be looking into the date, time and people trends.  So check it out soon.