Case-book-1927-murray-1st_edition

Extracting Places with Python

Ok, a quick note to remind myself – I was interested in learning how to use this: https://pypi.python.org/pypi/geograpy/0.3.7  Installation was a bit complicated; lots of dependencies. The following pages helped sort me out: https://docs.python.org/2/install/ http://stackoverflow.com/questions/4867197/failed-loading-english-pickle-with-nltk-data-load AND ultimately, I had to open … Continue reading Extracting Places with Python

Grabbing data from Open Context

This morning, on Twitter, there was a conversation about site diaries and the possibilities of topic modeling for extracting insight from them. Open Context has 2618 diaries – here’s one of them. Eric, who runs Open Context, has an excellent API for all that kind of data. Append .json on the end of a file name, and *poof*, lots of data. Here’s the json version of that same diary.  So, I wanted all of those diaries – this URL (click & then note where the .json lives; delete the .json to see the regular html) has ’em all. I copied … Continue reading Grabbing data from Open Context

a quick note on visualizing topic models as self organizing map

I wanted to visualize topic models as a self-organizing map. This code snippet was helpful. (Here’s its blog post). In my standard topic modeling script in R, I added this: which gives something like this: Things to be desired: I don’t know which circle represents what document. Each pie slice represents a topic. If you have more than around 10 topics, you get a graph in the circle instead of a pie slice. I was colouring in areas by main pie slice colour in inkscape, but then the whole thing crashed on me. Still, a move in the right direction … Continue reading a quick note on visualizing topic models as self organizing map