With the advances in automated text analysis, unstructured data sources such as a financial news and social media are becoming an effective source of trading signal to supplement existing structured data feeds. An important component of such analysis is to characterize the sentiment expressed in online text about specific publicly-traded companies and commodities. Sentiment Analysis focuses on this task of automatically identifying whether a piece of text expresses a positive or negative opinion about the subject matter. Such expression of sentiment tends to be very domain specific, and we present recent advances in Machine Learning that help us adapt sentiment analysis to a target domain, such as Finance. We describe an approach to trading on sentiment expressed in text, and demonstrate the effectiveness of this approach on back-testing results over several years of financial news. We discuss alternative sources of user-generated content, such as blogs and Twitter, which can provide additional trading signals. In addition to sentiment analysis, we provide an overview of other aspects of text and social network mining that become relevant when incorporating such social media sources.