title: Financial news headers classification to positive/negative classes
body: <p>I'm doing a small research project where I should try to split financial news articles headers to positive and negative classes.For classification I'm using SVM approach.The main problem which I see now it that not a lot of features can be produced for ML. News articles contains a lot of Named Entities and other "garbage" elements (from my point of view of course).</p>
  
  <p>Could you please suggest ML features which can be used for ML training? Current results are: precision =0.6, recall=0.8</p>
  
  <p>Thanks</p>