This paper addresses the problem of sentiment analysis in an informal setting in multiple domains and in two languages. We explore the influence of using background knowledge in the form of different sentiment lexicons, as well as the influence of various lexical surface features. We show that the improvement resulting from using a two-layer model, sentiment lexicons, surface features and feature scaling is most notable on social media datasets in both English and Spanish. For English, we are also able to demonstrate improvement on the news domain using sentiment lexicons and a large improvement on the social media domain. We also demonstrate that domain-specific lexicons bring comparable performance to generalpurpose lexicons.