ViewScore - Consumer Facing Social Media Analytics

TechCrunch points to ViewScore, a site which aggregates reviews and review metrics. The cool thing is that for reviews that don't contain a metric (e.g. a star rating), the site infers one based on 'semantic analysis'.

ViewScore has developed an innovative search engine and product ranking system to simplify the complexity of product evaluation. First, ViewScore's crawler (Site Owners) searches the Internet and identifies product reviews by distinguishing them from text in which the product is simply mentioned or referred to. ViewScore's filed patent-pending technology, TextScore™, then takes the stream of unstructured and often incomparable product review data, elicited from hundreds of data sources, and converts it into a single, consistent and coherent product recommendations score. TextScore's artificial-intelligence and NLP (natural language processing) technology parses each product review and assigns it a normalized numerical value that represents the author's opinion of the product under review. These individual scores are weighted by the relevance of the review and the reliability of its source and then aggregated to yield a single product score for each product. The system then ranks the products by their final product score and presents final recommendations to the user.

This is doubly interesting to me as it combines sentiment analysis with a consumer facing social media search application. We are going to see more and more of these over the next few years as the quality of these interpritive technologies increases, and the volume and influence of social media continues to rise.

Note that I haven't yet explored the site in any detail and don't yet have any clear idea of the quality of the results. For example this review of the Canon EOS 30D gets a ViewScore of 98 out of 100. However, the text of the review ends with

As always, I strongly recommend trying the EOS-30D and its competitors before you drop the big bucks on a digital SLR!

This sounds cautionary in tone, whereas 98/100 sounds like a very strong recommendation. Other reviews (like this one) get a score of 100/100.

There are some very interesting challenges in automating this problem. CNet's review system (see here for a review of the Canon Powershot A610) allows users to comment and give their own feedback. The Canon got an average of 9.0 from 59 users who commented on the camera, but ViewScore gives a 74/100 score.

Personally, when I've been looking at reviews of products with the intention of determining which out of a few to go for I've been disappointed by the variance in reviews. One might see 3 reviews - 2 good and 1 very bad. Statistically, this information is not helpful. However, there are ways to weight the data. 2 good reviews from trusted and consistent reviewers should easily counteract 1 bad review from a one off user. On the other hand, if that one of reviewer never writes reviews and has been so disappointed in a product that they have clambered over many barriers to post the review...

Kurt Schrader of Squishr points out that he's been there before:

This is actually a bit of deja vu for me, as we originally had our technology working on gadgets, but we moved to music because we found that gadget reviews just didn't end up that useful in the grand scheme of things. People tended to say the same thing over and over again in their gadget reviews (good/bad interface, good/bad battery life, etc), and let's face it, if you're going to buy an mp3 player it's going to be an iPod.

I do agree with Marshall when he says that there's room for a lot of entries into this space.  Things should be interesting.

What I love about his post is that there is a comment which reads:

I don't buy iPod because it's crap.

Now, if you think about the aggregation of that type of content - unsolicited opinions - and its analysis: that is what we do.

[See other posts on sentiment mining.]

Comments

[Max Kalehoff]

You've got to wonder. What role can these aggregations play in the world of consumer reports and JD Power. Threat? Complement?

[Kurt Schrader]

Max,

We started out doing some unstructured analysis, but, as you know, it's an extremely hard problem to solve, so we moved to just using published scores for now. If you look at the ViewScore analysis, many of scores just seem arbitrary. I wonder how they're planning on explaining to people why 'Review X' translates into a score of '78.' We found that it only takes a person seeing one review that they feel is incorrectly scored to turn them off to your service forever.

Squishr is back up now, by the way. A perfect storm of circumstances brought it down for 12 hours while all of this was going on.

[DJ]

i didn't do a complete test to their technology but the ranking in digital camera is making a lot of sense to me
and this show me that they are parsing the text correctly and generating usable ranking

PS - i am professional photographer

[Jeremy Foro]

ViewScore make a good effort, but is not as credible and useful as alaTEST.com. On this site that gets review summaries from hundreds of magazines and publishers, you get a very credible globally calculated quality rating. Guess best results are achieved when comparing all these sites (hey, another business model ;-) )