Social networking, blogs and other forms of Internet content are generating a tidal wave of opinions on almost every subject, but most of these viewpoints are ignored.

Analytics software, however, can aggregate and evaluate this data to provide banks with insights on how the world perceives specific companies, people and products.

Seth Grimes, the founder of the consulting firm Alta Plana, said "sentiment analysis" technology uses sophisticated algorithms to listen to consumers, understand what they're saying and in what context. "For brand and reputational management this is a real growth area," he said. In the case of financial services "there's a lot of opinions about sales and bonuses, and an organization might want to factor that into how they present themselves."

Larry Levy, the chief executive of Jodange LLC, a Yonkers, N.Y., provider of sentiment analysis technology, said "a bank is a business and should always be looking at how they're being perceived in the marketplace … who is saying what about your brand, competitors, executives, products and marketing. You need to be monitoring social media as well as traditional media, and those lines are blurring."

Levy said Jodange can assess the buzz surrounding a company, monitor every mention of its name and identify the key opinion holders.

To assess buzz, Jodange evaluates mountains of data to determine an aggregate "opinion momentum." For example, it analyzed all the mentions of Countrywide Financial Corp. over two years, drawing on hundreds of thousands of mentions in blogs, tweets, news articles, television and radio.

Crunching all the numbers took about a day; the results enabled Jodange to correctly predict the direction of Countrywide's stock the next day 70% of the time, before its 2008 sale to Bank of America Corp.

Jodange can also isolate major influencers to see, for instance, what publications have the most effect on certain stocks. When the online magazine Seeking Alpha opined on Countrywide, the stock reliably popped in one direction or another, but that was not the case with other stocks, such as Fannie Mae.

Still, there's plenty of skepticism around sentiment analysis. For example, how can an algorithm divine all the subtleties of language? Consider the sentence: "I just love it when my bank charges me $30 in overdraft protection fees to buy a $2 cup of coffee." Algorithms do not get sarcasm, said Marc DeCastro, a senior analyst at Financial Insights. "I'm skeptical. It's mind-boggling what there is to monitor."

But Jeff Catlin, the CEO of Lexalytics Inc., an Amherst, Mass., provider of sentiment analysis technology, said people should not focus on individual cases where the technology fails.

The point is to crunch so much data that you get an aggregate direction. "If we're right 75% to 80% of the time, we don't care about any single story," he said.

Several hedge funds already use Lexalytics to gauge the direction of stocks, he said. And Cartesian Trading, a company that provides data to day traders, makes daily recommendations on about a dozen stocks using its technology.

Financial Times also uses Lexalytics' technology to power its Newssift service; visitors can enter the name of a bank or bank executive on its Web site, and the tool creates a pie chart with a sentiment breakdown: positive, negative and neutral sentiment.

On Sept. 14, for instance, a B of A search generated 5,801 sources of sentiment from blogs, online news, newspapers, magazines, TV, radio and research. The Newssift chart showed 30% were positive, 29% negative and 41% neutral, with the company trending neutral after an up-and-down 2009.

Many financial companies use sentiment analysis to evaluate customer data. SAS Institute Inc., a technology vendor in Cary, N.C., has a large bank client that uses its sentiment analysis manager to assess customer calls, e-mails and faxes pertaining to its loan modification program. By evaluating the language used by customers in each exchange, the bank can filter out people who do not qualify for loan modifications, and rank the customers it must process first to prevent attrition.

"It's a way to handle the sheer volume of inquiries they're getting," said Manya Mayes, SAS' chief text mining strategist.