My students do this as their class project. A few teams hit the 70%s for accuracy, with pretty small samples, which ain't bad.
Let's say you have some data like this:
Return Symbol News Text
-4% DELL Centegra and Dell Services recognized with Outsourcing Center's...
7% MSFT Rising Service Revenues Benefit VMWare
1% CSCO Cisco Systems (CSCO) Receives 5 Star Strong Buy Rating From S&P
4% GOOG Summary Box: Google eyes more government deals
7% AAPL Sohu says 2nd-quarter net income rises 10 percent on higher...
You want to predict the return based on the text.
This is called Text Mining.
What you do ultimately is create an enormous matrix like this:
Return Centegra Rising Services Recognized...
-4% 0.23 0 0.11 0.34
7% 0 0.1 0.23 0
That has one column for every unique word, and one row for each return, and a weighted score for each word. The score is often the TFIDF score, or relative frequency of the word in the doc.
Then you run a regression and see if you can predict which words predict the return. You'll probably need to use PCA first.
Book: Fundamentals of Predictive Text Mining, Weiss
Software: RapidMiner with Text Plugin or R
You should also do a search on Google Scholar and read up on the ins and outs.
You can see my series of text mining videos here