This document discusses using sentiment analysis on Twitter data to predict stock market movements. It proposes collecting tweets related to various companies, analyzing the sentiment polarity of the tweets, extracting features using n-grams, and training machine learning models like logistic regression and decision trees to predict stock price changes. The models would be trained on the sentiment scores of tweets along with market data to analyze correlations and enable stock price predictions. The goal is to see if analyzing public sentiment on Twitter can help predict company stock performance and guide investment decisions.