Teradata Aster demonstrates its graphical "pathing" capabilities very nicely by showing the relationships between tweeters and their tweets at events, like the Teradata Third-Party Influencers Event I attended last week. 

The demonstration shows how to produce some sentiment of the event, but more importantly demonstrates relationships and influence power.  Customer relationships and influence power are becoming part of the set of derived data needed to fully understand a company's customers.  This leads to identifying engagement models and the early identification of patterns of activity that lead to certain events - desired or otherwise.

One important point noted by Stephanie McReynolds, Director of Product Marketing, at Teradata Aster, was that the sphere of relevant influence depends on the situation.  You can retweet hundreds of tweets, many for which you do not even know the tweeter.  However, when buying a car, those who would influence you would be only a handful.

One would need to take some more heed of an influencer's opinion - or that of someone with a relationships to the influencer.  It can become quite a layered analysis and influence power is hard to measure.  Grabbing various digital breadcrumbs is relatively easy, but is it indicative of influence?  Likewise, is a tweetstream indicative of the sentiment of an event?  I'm not sure.  It may not even be indicative of the sentiment of the tweeters.  Digital is all a start.  The worlds of third-party data, real sentiment analysis and possibly sensor data are coming together.