In a former life, I worked for Thomson Financial in Boston - First Call to be exact. Over the years there, I learned to enjoy the ebb and flow of earnings season - watching the markets move based on quantitative analysis. There was little analysis of research and text back then. Boy, things have changed. Today, Lexalytics is working with the grown-up version of Thomson Financial now known as ThomsonReuters. Our software is used in their algorithmic trading platform and incorporates sentiment into the trading process. The sentiment derived from the aggregated content flowing through a trading system has proven to be extremely useful in that environment. We also recently announced our relationship with First Coverage, which has a web service called "The Community". It essentially provides a collaboration platform for the buy-side and sell-side to help money managers filter out the noise and focus on the data that matter most to their holdings. It's very cool, especially if you are a quant-type, but more importantly it reinforces the shift from strictly numbers driven analysis and trading to a healthy mix of numbers and text analysis. We've known for a while that information stored in unstructured data can be helpful to monitoring corporate brands and reputation. In the PR/Marketing world there is pressure to be "exact" regarding sentiment for every document, but as Jeff noted last week in a Q&A session with PRWeek: "My belief is that over time the PR industry will begin to look at the aggregate statistics and when they do so, automated sentiment will be a perfect fit because it's consistent and accurate across large blocks of content." In the world of market trading and research, text and sentiment analysis technology provides the insight and measurement capabilities needed by financial services organizations to discover, react, and respond to market opinions. This is just one of the expanding markets we've seen in the past year embracing text analytics capabilities.