Gauging Mass Opinion: Don't Label it Social Media
In the first generation of the social Web, the marketing groups and public relations teams would develop reports to provide metrics for a particular campaign. They were pretty much the sole users of "social media," technologies.
That's a problem as far as WiseWindow is concerned. Social media is a poor label for describing how comments, blog posts, updates and other opinions can be leveraged to gauge views across social networks and thousands of Web sites.
"Labeling of it as social media has limited its potential up to now," said Marshall Toplansky, president of WiseWindow. "That is why we are calling it mass opinion business intelligence and not social media analytics."
Now, the social Web is a real-time engine. Cloud computing is a reality and a new era is upon us that allows the enterprise to mine the vast sea of comments, reviews, updates and blog posts from millions of people. We're entering an era where the social Web will serve as the main territory for performing predictive analytics.
WiseWindow is using artificial intelligence technology, web crawlers and the processing power of the cloud to get real-time results for enterprise customers. For example, this means that companies may leverage the social Web to make sales forecasts and gauge the opinions of mass society to immediately understand the current opinions about its brand or those of competitors.
WiseWindow calls the product Mass Opinion Business Intelligence, describing it as a service that goes beyond keyword search and click-throughs to predict market movement.
According to WiseWindow, sentiment analysis has failed as a strategic research tool. When matching words, the context is lost. People use words differently to describe their sentiments. The mass amount of data available makes the process overwhelming.
Instead, the WiseWindow web crawler will search for comments and other opinions across thousands of sites that are not blocked by privacy restrictions. The artificial intelligence trains itself to look for a particular topic. It brings back all related opinions. The information is then distilled for the client or made available through a web portal where the data can be analyzed.
Recently, WiseWindow worked with a client from the film industry. WiseWindow used its technology to research 400 films, generating 4.5 million comments from 70,000 sites. They distilled the data to lean what is hot and what is not.
As another example, , WiseWindow did research for the film, Marley and Me, starring Jennifer Anniston and Owen Wilson. The pre-release promotions featured Luke Wilson. But the comments from the Web demonstrated that Anniston had greater appeal than Wilson. As a result, the trailers were changed to feature Anniston more than Wilson.
WiseWindow was founded in 2007 by Rajiv Dulepet. He has an impressive background. He was a visiting scholar at Stanford's School of Management and Engineering, spearheading the development of presidential prediction projects for both the 2004 and 2008 elections.
WiseWindow started developing its technology in 2007 and began working with clients last year. The company has four patents for its web crawling, auto-classifications of opinions, relevance recognition and in statistical language applications.
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Wasn't Owen Wilson the one in Marley & Me?
Not questioning their methodology, however, "mass opinion business intelligence" is only one view - and it's a mouth full!
WiseWindow seem to be tackling the issue of intelligence extraction / production from "social media" in a very intelligent and original way potentially spearheading the emergence of Public Opinion Polling 2.0 and predictive modelling of Public Opinion 2.0.
While these guys seems to have a piece of AI technology that makes it possible to identify trends and predict the likely reactions of an audience or the public opinion in general, I think their criticism of so-called "sentiment analysis" is misguided and pointless. Irrespective of the success and relevance (or not) of sentiment analysis, WiseWindow has a very valuable contribution to make, if it works as well as the article says, which I tend to believe given the quality of this publication.
Wisewindow has a different methodology but am not sure if it would work well or not.
Jeez, saying "sentiment analysis has failed as a strategic research tool" because of the limitations of (quite a few of) the implementations/platforms out there is way off. There are also vendors, mostly in the text analytics world, who do it right. They typically use natural-language processing (NLP) software to get the context and do a good job of identifying and extracting sentiment. Maybe this is WiseWindow's approach. In any case, they certainly do have competition that anyone looking for a solution should investigate.