Goal of this project is to forecast how the market will behave in the future via sentiment analysis on a set of tweets over the past few days, as well as to examine if the theory of contrarian investing is applicable
The idea is to use this information of correlation, recognize the pattern and use it to predict the stock future behavior. In this paper we collect the tweets, preprocess them, analyze its sentiment and segregate them into three categories i.e. positive, negative and neutral using sentiment analysis python library. We then found out that there is a marginal correlation between the negative tweets and stock closing price. Finally, we merged the stock and tweeter data, applied machine learning algorithms and compared its error for stock closing price prediction.