A huge amount of information is available online, in particular regarding financial news. Current research indicate that stock news have a strong correlation to market variables such as trade volumes, volatility, stock prices and firm earnings. Here, we investigate a Sentiment Analysis problem for financial news. Our goal is to classify financial news as favorable or unfavorable to Petrobras, an oil and gas company with stocks in the Stock Exchange market. We explore Natural Language Processing techniques in a way to improve the sentiment classification accuracy of a classical bag of words approach. We filter on topic phrases for each Petrobras related news and build syntactic and stylistic input features. For sentiment classification, Support Vector Machines algorithm is used. Moreover we apply four feature selection methods and build a committee of SVM models. Additionally, we introduce Petronews, a Portuguese financial news annotated corpus about Petrobras. It is composed by a collection of one thousand and fifty online financial news from 06/02/2006 to 01/29/2010. Our experiments indicate that our method is 5.29 per cent better than a standard bag-of-words approach, reaching 87.14 per cent accuracy rate for this domain.