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Data mining

I'm a fool. Seeking inspiration for today's blog post, I thought "I know! I could produce some more stats about my morning pages. Maybe I could try to see whether my word count varies with mood?" This is a probably not a terribly promising subject for anyone else but I liked the idea, so fired up Excel and got to work. An hour and a half later, and I have some neat formulae, some questionable metrics, a graph that doesn't seem to be very enlightening, and about 5 different ideas on how to improve or extend this investigation in different directions. I've been working in data analysis for over three years now; I should have realised that this was the only possible outcome. Nevertheless, I failed to apply my ability to extrapolate trends to my own behaviour. Isn't it ironic, don't you think?

In many ways this is a positive outcome. Sure, I was just tooling around, but this is all indicative of a regained ability to focus on tasks (and even better, to enjoy doing so). Maybe it's the SAD lamp, maybe it's another factor, but it's certainly a relief. I guess this evening has taught me something about my mood after all... Anyway, I spent too long making the damn graph not to share it with you, though I concede that I haven't given it the most promising build-up:

  • 'Mood' is measured quite crudely, as positive words/1000 - negative words/1000.
  • Positive words are 'good', 'not bad' (and variants), 'cheerful', 'chirpy' and 'happy'.
  • Negative words are: 'bad', 'not good' (and variants), 'sad', 'miserable' and 'unhappy'.

    • Methodological criticisms are welcome...


( 1 comment — Leave a comment )
Nov. 24th, 2011 09:43 am (UTC)
I'd like to see a test of strength of association, but on the face of it you may be privileging mood after the manner of Chekhov's maxim. This may in turn be (a) a free choice; (b) an unconscious choice; (c) you are Russian and I claim my five pounds.
( 1 comment — Leave a comment )


Cute overload
Drifting in and out of consciousness

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