textualized seattle music scene
After throwing the text at the freest supercomputational resources available on the internet (textalyzer), we have discovered a few shocking trends. First, the bigger your band, the less text you’re likely to provide to the directory. Next, there’s no shortage of rock, indie, or indie rock acts. If you put a little effort into it, you might even run into an experimental hardcore act (n = 124). Finally, local bands are pretty into the internet.
A pictograph of the top 100 words appears below the jump.
methods: the text (19063 words) of the “Bands” section of Stranger Musicians’ Directory was fed into textalyzer. Words were text strings of 3 or more characters, the “English Stoplist” was used and numbers were ignored (bands hate maths) and the top 100 words were printed in alphabetical order. Font size is roughly proportional to word frequency using a sizing system chosen to exaggerate differences among the less frequent words.
For reference, a list of the twenty-five most common words is provided below.
| Word | Occurrences |
| com | 1708 |
| rock | 1646 |
| www | 772 |
| indie | 715 |
| seattle | 454 |
| ave | 245 |
| punk | 240 |
| hotmail | 192 |
| yahoo | 189 |
| hardcore | 154 |
| net | 150 |
| experimental | 138 |
| box | 135 |
| alternative | 124 |
| folk | 117 |
| electronic | 111 |
| emo | 109 |
| jazz | 108 |
| hiphop | 107 |
| band | 105 |
| pop | 103 |
| blues | 97 |
| music | 88 |
| country | 77 |
A secondary analysis restricted to band names did not reveal any significant patterns. 1462 unique words were present in the list of 1726 non-filtered words; suggesting a high-degree of differentiation in the naming practices of local bands. The most commonly occurring words were band (n=17), soul (7), day (6), and black (6).
Hey nice analysis. How did you create the graphic view of the results. That looks like what Flickr and others are doing to display highly used tags.
I want to do the same thing with this data… http://communitysteps.org/happyhour/
Thanks. I actually did the graphic view pretty much by hand (excel + web browser + photoshop) though there’s probably an easier way.
I’m not really sure how you’d use it for your happy hour site.
Here is what I’m thinking of… http://www.technorati.com/tag/ and http://www.flickr.com/photos/tags/
Here is an example if to try for del.icio.us data http://kevan.org/extispicious/
Be nice to just feed in data to textalyzer and then organize it with one of these methods.
yeah. it would be nice, but I don’t know how their visualization tools work.