Ok, so that isn’t an actual picture of the new Microsoft Blews news aggregator that was announced by Microsoft Research today, but tell me that the screen shot (see below) doesn’t bring back memories of eating Rocket Pops on the beach as a child (or wherever you ate them).

But back to Blews. It’s a news aggregator (see Techmeme and about 45 others, including this gem), but it goes beyond mere clustering of stories to show what’s important right now based on who’s linking to what in near real time. Blews, which is only looking at political news, also tells you the bias of the links in to a story:

BLEWS uses political blogs to categorize news stories according to their reception in the conservative and liberal blogospheres. It visualizes information about which stories are linked to from conservative and liberal blogs, and it indicates the level of emotional charge in the discussion of the news story or topic at hand in both political camps. BLEWS also offers a “see the view from the other side” functionality, enabling a reader to compare different views on the same story from different sides of the political spectrum. BLEWS achieves this goal by digesting and analyzing a real-time feed of political-blog posts provided by the Live Labs Social Media platform, adding both link analysis and text analysis of the blog posts.

Here’s what all that looks like:

Liberal links are blue (rasberry) and on the left, conservative links are red (cherry) and on the right. The middle is the story itself in white (lemon). The dots around the edges suggest the emotional charge of the commentary, which can drip off of the Rocket Pop in very hot weather.

I note that no one on the team (Michael Gamon, Sumit Basu, Dmitriy Blenko, Danyel Fisher, Matthew Hurst and Christian Konig) is a user interface specialist or web designer.

Putting aside the UI, which is hard to do, the artificial intelligence behind Blews could be interesting. It is very hard to get a machine to decipher emotion and meaning from raw text unless they are doing mere keyword searches (see, for example, Powerset). Microsoft is calling this hard bit “detecting emotional charge.” If they’ve got it right, or are close, there are an unlimited number of potential applications for the technology.

As an aside, this somewhat reminds me of ScoutLabs, a startup we wrote about last December. Scout Labs helps brand marketers track commentary on their brands, and tries to decipher emotion towards that brand as well.

Scout Labs, a San Francisco based company, is in my opinion one of the more interesting startups to launch this year. It isn’t consumer focused. Rather, it helps companies make sense of the mass of positive and negative things that are said about their brands in blogs, user generated videos, and images.

The company has been in private beta for months with 40 or so companies. I have list of participants, which is impressive but mostly confidential. A few that have given permission to be named are CBS, eBay, AvenueA/Razorfish and BBDO West. Today they are opening up the private beta to additional users who request an invitation on their site.

Brand managers start by creating “scouts” for each brand they want to track. This can be anything that people might talk about – a thing (coca cola), a person (Hillary Clinton), a slogan (Just Do It), etc.

Scout Labs then creates the “scout,” going back in time to January 2007 when they first started populating their database of user generated content from blog search engines.

Any mention of the brand is cataloged. Users can then see each blog post, video or image that has to do with the brand, and the interface shows how mentions increase and decrease over time. But that’s when the difficult part begins. Scout Labs then analyzes each blog post and decides if it’s, broadly speaking, negative, positive or neutral to the brand. They also rank the source of the content to highlight more influential sources.

Users view the information on a single page dashboard. I’m including a few screen shots, although founder Jennifer Zeszut says they are in the process of redesigning the interface based on feedback from current beta testers.

Any data source can be clicked on and read via a pop up window, where it can be bookmarked, sent to others, marked for follow up, etc. Scout Labs will also facilitate commenting on the blog post if the brand manager feels that they need to respond.

The company created a Scout for both TechCrunch and Michael Arrington and let me review it last week. For the most part they got the hard part right – determining if a blog post was positive, negative or neutral. They don’t deal with irony well, though. One blog post about me called me “high and mighty.” Scout Labs thought it was positive when it was in fact highly critical. But I hit a button and switched the post to negative in a click. The company uses this feedback to train its system towards better future results.

The really cool part of the product is the fact that it looks at historical data, too (as I said, back to January 2007). So a brand manager can create a new scout and within moments see how the number of daily, weekly or monthly mentions increases or decreases, and how they fluctuate from negative or positive over time. I could easily see how periods where we were heavily criticized for something led to an increase in negative posts, and vice versa.

Zezsut gave me a number of examples of how beta testers have used this to help manage brands. In one case a company tracked a celebrity employee to help them decide if a contract renewal should be given (it was). Any startup could use this to easily track feedback on their new product, and respond to that feedback.

Scout Labs is pursuing a Salesforce-type business mode and charging per user per month. Beta testers are not being charged, and the company is still figuring out the appropriate fee structure.

Competition

Brand analysis was once done almost exclusively through human labor – consultants would clip stories, read them for opnions and aggregate the data. It was time consuming, trailed real time by weeks or months, and very costly. Using software to do this work speeds things up and is far cheaper. But correctly identifying opinions and categorizing them is very, very hard.

A number of companies try to automate part or all of the process. Key competitors include Umbria, BuzzLogic and Nielsen BuzzMetrics.

The company is part of the Minor Ventures incubator, which also launched Grand Central (acquired by Google for a rumored $50 million), 8020 Publishing, Swivel, Open DNS and others. Minor Ventures was founded by CNET cofounder Halsey Minor and is run by Ron Palmeri.

Look for a full public launch of Scout Labs in February or March 2008. The company has been through two rounds of financing with Minor Ventures (October 2006 and December 2007).