User-generated data in blogs and social networks have recently become a valuable resource for sentiment analysis in the financial domain, since they have been shown to be extremely significant to marketing research companies and public opinion organizations. In order to identify bullish and bearish sentiments associated with companies and stocks, we propose a fine-grained approach that returns a continuous score in the range. Our supervised approach leverages a frame-based ontological resource which produces feature sets such as lexical features, semantic features and their combination. One of the outcome of our analysis suggests that the frame-based ontological resource we have used might be successfully applied for sentiment analysis within the financial domain achieving better results than traditional sentiment analysis methods that do not embody semantics. We also show the higher performance of a fine-grained approach based solely on the evaluation of specific substrings of the message, rather than on features extracted from the whole text of a financial microblog message through the frame-based ontological resource. We have also compared our system with semi-supervised and unsupervised approaches and results indicate that our approach outperforms the others. Last but not the least, our approach is general and can be applied on top of any existing supervised method of polarity detection.