  With the advent of fast-paced information dissemination and retrieval, it has
become inherently important to resort to automated means of predicting stock
market prices. In this paper, we propose Taureau, a framework that leverages
Twitter sentiment analysis for predicting stock market movement. The aim of our
research is to determine whether Twitter, which is assumed to be representative
of the general public, can give insight into the public perception of a
particular company and has any correlation to that company's stock price
movement. We intend to utilize this correlation to predict stock price
movement. We first utilize Tweepy and getOldTweets to obtain historical tweets
indicating public opinions for a set of top companies during periods of major
events. We filter and label the tweets using standard programming libraries. We
then vectorize and generate word embedding from the obtained tweets. Afterward,
we leverage TextBlob, a state-of-the-art sentiment analytics engine, to assess
and quantify the users' moods based on the tweets. Next, we correlate the
temporal dimensions of the obtained sentiment scores with monthly stock price
movement data. Finally, we design and evaluate a predictive model to forecast
stock price movement from lagged sentiment scores. We evaluate our framework
using actual stock price movement data to assess its ability to predict
movement direction.
