
Twitter is a great resource but it's just so full of noise that sometimes I
question its value to me.  In between the awesome information I get from
it I have to wade through check-ins, mealtimes and childcare woes.  I'm
sorry but what you had for lunch really isn't that interesting to me
unless it's some amazing new restaurant right near me.

I wanted an easy way to see just the relevant bits so this weekend I
knocked up a quick Seesmic plugin that uses a Bayesian filter to analyze
each Tweet and score it for relevance.  You can right click on any Tweet
to say "Relevant" or "Not relevant" and it soon learns to categorize
tweets using a red - green color dot and a score.  There's a cut-off
value too below which it simply hides the Tweet (but for the screen-shot
I turned this off).

The Seesmic API was easy to learn (if somewhat lacking in documentation
and complete working examples).  The [Visual Studio
Templates](http://timheuer.com/blog/archive/2010/09/09/seesmic-desktop-launches-start-writing-plugins-with-silverlight.aspx)
provided by Tim Heuer came in handy too.  The Managed Extensibility
Framework made it very easy to write the plug-in - I just wish Seesmic
could auto re-load it each time you drop a new version in there.
 Restarting Seesmic each time was annoying and caused Twitter to
rate-limit me more than once.

Information about what you like and don't like is stored in
IsolatedStorage in a simple flat file.  The file is reloaded on startup
and the probabilities for each word are recalculated.

If I get time and if there's sufficient interest I might take this a bit
further but for now I'm happy to have less to read.

*[Apologies to anyone who is red-dotted in the screen-shot, I'm sure you
have lots of good Tweets too which is why I'm following you, I just
happened upon this one which is noise to me.]*


The Blog of Ian Mercer.

Topic: bayesian

A Bayesian Spam / Relevance Filter for Seesmic / Twitter