Stock Sentiment Analysis using News Headlines

About the problem and the dataset used:

• The data set in consideration is a combination of the world news and stock price shifts available on Kaggle.
• There are 25 columns of top news headlines for each day in the dataframe.
• Data ranges from 2008 to 2016 and the data from 2000 to 2008 was scrapped from Yahoo finance.
• Labels are based on the Dow Jones Industrial Average stock index.
• Class 1 - the stock price increased.
• Class 0 - the stock price stayed the same or decreased.

About the approach:

• Used TF-IDF and Bag of Words for extracting featues from the headlines.
• Used Random Forest Classifier, Multinational Naive Bayes and Passive Aggressive Classifier for analysis.