  Efficient Market Hypothesis is the popular theory about stock prediction.
With its failure much research has been carried in the area of prediction of
stocks. This project is about taking non quantifiable data such as financial
news articles about a company and predicting its future stock trend with news
sentiment classification. Assuming that news articles have impact on stock
market, this is an attempt to study relationship between news and stock trend.
To show this, we created three different classification models which depict
polarity of news articles being positive or negative. Observations show that RF
and SVM perform well in all types of testing. Na\"ive Bayes gives good result
but not compared to the other two. Experiments are conducted to evaluate
various aspects of the proposed model and encouraging results are obtained in
all of the experiments. The accuracy of the prediction model is more than 80%
and in comparison with news random labeling with 50% of accuracy; the model has
increased the accuracy by 30%.
