The vast amount of online opinionated text has driven the interest of an active research community that exploits this user-generated content to gather market information and create business intelligence applications. State-of-the-art Natural Language Processing techniques can provide a level of text interpretation that might be adequate for certain tasks, but there is room for improvement over the current methods which are based on pre-existing knowledge, such as prior polarity lexicons and domain ontologies. The crucial question is how much resource-intensive linguistic processing is needed to understand what people are talking about, and how do they feel about it. A principled combination of symbolic and stochastic approaches that is guided by bootstrapping existing and extensive Web 2.0 resources seems to be a good compromise when full text interpretation is not available or practical.