Lexalytics Moves to Boston to Exploit New Market for Sentiment Analysis

3/29/10Follow @wroush

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Cisco, and we are now the recognized leader in that spot. I’d love to say it was really well thought-out and reasoned, but at the time we were just thinking, ‘What would be a cool feature to add?’”

Unlike Cambridge, MA-based Crimson Hexagon, Watertown, MA-based Cymfony, and a cluster of sentiment analysis startups in Seattle like Appature and Evri, Lexalytics doesn’t directly serve companies who want to know what people are saying about them on the Web. But the 20-employee startup does sell its software libraries to many of the firms that do this, including Cymfony and ScoutLabs. “A lot of those vendors use us under the hood to provide their sentiment analysis and entity extraction,” say Catlin.

Entity extraction is the process of tagging a digital document to identify key people, places, companies, products, e-mail addresses, themes, and messages. Once that’s done, Lexalytics’ software can also parse a document’s grammar, word order, and vocabulary to determine who’s speaking about whom, then score the emotional tone of each statement.

“Behind the scenes we have dictionaries of tonal phrases—typically, adjective-noun or adverb phrases—so that [the software] knows when it sees ‘horrible disaster’ or ‘wonderful day’ that those are sentiments, and who they belong to,” says Catlin.

Documents processed by Lexalytics’ software, called Salience, come back as XML files riddled with new metadata that companies can use to draw inferences or soup up their search results with related information. “You give us a document that’s a foot long, and we give you back one that’s three feet long,” says Catlin. “The best applications are with search vendors like FAST and Endeca who use the technology to make their solutions better. Google is great if you know what you are looking for, but if you have no idea what you are looking for, you need the data to tell you what’s going on. The metadata lets you start digging through that.”

Catlin gives a hypothetical example. “Say you want to know who is hot in the news today and who they are related to. It turns out Bill Gates is hot. You click on that and get the concept and the sentiments and the other people and companies that are mentioned around it, and may you find out that it’s about some energy company that he’s funding. You don’t have to have a great question at the start to find that out.”

Crucially, the software can pursue connections like this automatically, without a human involved. Which is why Lexalytics’ technology is also attractive to clients like Thomson Reuters, the financial news and services giant. Catlin says the organization is using Salient to tailor the input for algorithmic trading software that attempts to get ahead of the market by … Next Page »

Wade Roush is a contributing editor at Xconomy. Follow @wroush

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