I used two different types of classifers to determine whether a given news article is good for a stock or bad. 

The first computes the tfidf value for each document and the finds the cosine similarity between
all the documents being obverved. It chooses the largest similarity value to find the most "similar"
file in our data set. From this information it decides whether it is good or bad. This results in an 
accuracy of about 56%.

The second model is far more simple and more accurate. It is modeled after the Naïve Bayes Classifier.
I compare the conditional probabilities of having a "good" news arcticle given a set of words in each article
v.s. it being a "bad" news article. This model gives an accuracy of around 81%.