Harmonize the wind

I always liked the idea of the original Ambient Orb -- a device that just sits there, but changes color based on metrics of interest to you. Bad weather approaching? It turns red. Stock market up? Green.

CIO John Halamka famously placed one on Paul Levy's desk, to help the CEO effortlessly monitor the ED waiting room situation.

But colors can only express so much - I think the Ambient Orb could just communicate a few things like "Good" or "Bad" or "Really Bad" on whatever you programmed it to care about.

What really held me back, though, was the idea of spending $150 on a ball that passively monitors some situation, when more "active" monitoring was never more than a few clicks away.

Then came Twitter.

I've given Twitter and its users a lot of grief over the years, even as I've come to spend more time with them than any other social network. But Twitter seems built around the concept of passive monitoring.

Skimming a Twitter feed is a nice way to check in with friends and colleagues, and pick up some news or useful links. I'm getting comfortable with the idea that Tweets are a workable proxy for thoughts, and also starting to accept that software can accurately categorize Tweet content and deduce sentiment.

So maybe a Twitter feed isn't the best way to survey the hive mind.

The Listening Machine (hat tip: the Verge) is a project to follow 500 UK Twitter accounts and figures out the positivity or negativity (or neutrality) of Tweet content, as well as categorize the Tweets into one of eight subjects. The  Tweets are then converted to music.

It seems to me that music might be better than color, to reflect the complexity of the Twitter stream. I've been listening on and off for the past few hours, and can pick up without much difficulty when the overall sentiment turns negative, and when the rate of tweets pick up. I wonder if it's possible to tell if the stream is featuring ponderous topics or light chitchat - or if the current discussion is weighted toward politics, or the arts.

The idea of catching a snippet of music and knowing the mood, engagement, and to some extent, the content of conversations in an area, is very appealing, though I think DJs already make something like this possible, when reading a crowd, picking up a vibe. Twitter analytics will just make the crowd's thoughts and feelings more quantifiable.