gathering trend information and Web buzz (what traders call “alpha”) faster than people can.
“Because they are Thomson Reuters, they have a very real-time stream of news, and they can dump that information into their trade execution system before anybody else can get it,” says Catlin. “They’ve found they can do 60 to 70 basis points better than the market when scoring sentiment on news.” (A basis point is a hundredth of a percent.)
Lexalytics hasn’t taken any venture cash, and is operating at a profit. The startup shares sales and marketing expenses with UK-based Infonic, a document management company with which it has formed a joint venture. When I interviewed Catlin and Lexalytics marketing vice president Christine Sierra, it was the very first time they’d used the phone in their new office on Congress Street, just a stone’s throw from Thomson Financial.
Moving to Boston not only makes recruiting and growth easier, Catlin says, it also brings the company closer to other major customers like Endeca, Northern Light, and FAST, now a division of Microsoft. “The market seems to be quite receptive to this technology right now, and it’s easier to grow a business in a metro area like Boston than in Amherst,” says Catlin, who will split his time between the old Amherst office and the new Boston headquarters.
To make its technology accessible to companies outside the narrow fields of enterprise search or financial services, Lexalytics introduced a new Web-based service last week called Lexascope. It’s not a full-blown reputation monitoring platform—Catlin says the company has toyed with launching such a service, but doesn’t want to compete directly with its own customers. Rather, it’s an application programming interface, or API, that lets any organization plug their own Web monitoring applications into an online version of the Salient engine, which will trawl through articles, blogs, tweets, surveys, forums, and other documents and extract the major entities, themes, and sentiments. Aimed at marketers and content management specialists, the “freemium” service is available at no cost for up to 1,000 documents per day. For a $400 monthly fee, users can process up to 50,000 documents a day.
Many of the natural language processing, machine learning, and statistical modeling techniques behind sentiment extraction have been around for a decade or more. But as with Lexalytics itself, it’s only in the last couple of years that the parts have begun to mesh well, says Catlin.
“Back in 2003 or 2004, a lot of people were talking about it, but for the most part they couldn’t do it,” he says. “But as time goes by, hardware gets better and research comes out modifying the older techniques. We have glued a bunch of technology ideas together to come up with a whole that is more complete than any of the parts. You don’t have to stand on one foot and wave a rubber chicken counterclockwise to make it work. We are far enough downstream now so the stuff really works to solve real problems.”
