Mood for thought

There is a radio station in Ireland that kicks off its afternoon drive time program with the presenter waffling on about a daily ‘happiness index’. I always thought this was a bit bland and a gimmick. After all, who keeps or compiles this happiness index and how could it possibly mean anything?

I then read something which put me thinking. Researchers at the University of Manchester and Indiana University have put some science behind this idea of  ‘sentiment tracking’.

They looked at how global emotion and mood, as measured via something like Twitter, could predict stock market activity. They investigated whether measurements of collective mood states derived from large scale Twitter feeds, correlated to the value of the Dow Jones Industrial Average (DJIA) over time. They analyzed the text content of daily Twitter feeds by two mood tracking tools, OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS)

Their results indicated that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions. If you want to predict closing prices on the Dow Jones, have an eye on the Twitter feed.

This raises some interesting questions about how we look at social media data or activity. It may justify a more qualitative approach. When it comes to social media marketing, we may need to look at how our followers feel, rather than just counting them. 

This type of index should make us stop and think about how we interpret the holy grail of Big Data. Does this happiness index change with the weather?, with a regions sporting success? and if so does that affect stock markets or consumer spending? Which is cause and which is effect? Plenty for the self proclaimed big data scientists to ponder there.