As financial markets getting faster and more complex, it is difficult for market participants to manage the information overload. Sentiment analysis is a useful text mining method to process textual content and filter the results with analysis methods to relevant and meaningful information. The paper in hand introduces a new method for sentiment analysis in financial markets which combines word associations and lexical resources. Based on stock market news from January 2000 to February 2014 we analyzed documents on different levels. The results are presented and evaluated in this paper.