Stock Price Forecasting via Sentiment Analysis on Twitter

Abstract

Stock price forecasting is an important and thriving topic in financial engineering especially since new techniques and approaches on this matter are gaining ground constantly. In the contemporary era, the ceaseless use of social media has reached unprecedented levels, which has led to the belief that the expressed public sentiment could be correlated with the behavior of stock prices. The idea is to recognize patterns which confirm this correlation and use them to predict the future behavior of the various stock prices. With no doubt, though uninteresting individually, tweets can provide a satisfactory reflection of public sentiment when taken in aggregate. In this paper, we develop a system which collects past tweets, processes them further, and examines the effectiveness of various machine learning techniques such as Naive Bayes Bernoulli classification and Support Vector Machine (SVM), for providing a positive or negative sentiment on the tweet corpus. Subsequently, we employ the same machine learning algorithms to analyze how tweets correlate with stock market price behavior. Finally, we examine our prediction's error by comparing our algorithm's outcome with next day's actual close price. Overall, the ultimate 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 final results seem to be promising as we found correlation between sentiment of tweets and stock prices.