There is no doubt that Mood News is a cool idea. I've been tracking it for a while as a member of the backstage community (from the beeb) - it was recently picked up by Infosthetics. However, it also illustrates the difficulty of developing this type of feature. The current most negatively ranked news is:
An Algerian man accused of involvement with bombings in Paris in 1995 goes on trial in France.
I would think that this was actually positive news. However, as the system uses keyword spotting to determine what is good and what is bad (bomb==bad), the orientation can escape the system. In addition, there is an issue of point of view. To whom is it good/bad news? Take a look at the current highly ranked positive news:
An EU aid package worth 120m euros is hailed by Palestinians and the US but condemned by Israel.
Sounds like bad news for Israel at least.
In analysing social media in the context of marketing intelligence, I tend to think of a number of categories of textual features, including:
- Sentiment: evaluative statements (I like it, It sucked)
- Objective Favourable/Unfavourable statements (It is broken)
- Textual features: these are signals in the text that indicate some sort of mental state, especially emotion/affect (I can't wait to see it - indicating anticipation/excitement)
Generally, what we need to deliver is some mixture of these. The question raised by mood news is: what is the mixture appropriate for journalism? and, of course, what is of value to the user?
There is another set of considerations from the research side which needs to connect:
- Analysis that is useful to a customer/user of the output
- Psychological models of emotion/affect, etc.
- What is achievable with text analytics
That is a story for another post.