From the New York Times, this piece about using analysis of unstructured data in automated trading:

Math-loving traders are using powerful computers to speed-read news reports, editorials, company Web sites, blog posts and even Twitter messages — and then letting the machines decide what it all means for the markets.

The development goes far beyond standard digital fare like most-read and e-mailed lists. In some cases, the computers are actually parsing writers' words, sentence structure, even the odd emoticon. A wink and a smile — 😉 — for instance, just might mean things are looking up for the markets. Then, often without human intervention, the programs are interpreting that news and trading on it.

Given the volatility in the markets and concern that computerized trading exaggerates the ups and downs, the notion that Wall Street is engineering news-bots might sound like an investor's nightmare….

Many of the robo-readers look beyond the numbers and try to analyze market sentiment, that intuitive feeling investors have about the markets. Like the latest economic figures, news and social media buzz — "unstructured data," as it is known — can shift the mood from exuberance to despondency.

Tech-savvy traders have been scraping data out of new reports, press releases and corporate Web sites for years. But new, linguistics-based software goes well beyond that. News agencies like Bloomberg, Dow Jones and Thomson Reuters have adopted the idea, offering services that supposedly help their Wall Street customers sift through news automatically.

Some of these programs hardly seem like rocket science. Working with academics at Columbia University and the University of Notre Dame, Dow Jones compiled a dictionary of about 3,700 words that can signal changes in sentiment. Feel-good words include obvious ones like "ingenuity," "strength" and "winner." Feel-bad ones include "litigious," "colludes" and "risk."