A Methodology for Stock Movement Prediction Using Sentiment Analysis on Twitter and StockTwits Data

Application of Machine Learning (ML) and sentiment analysis on data from microblogging services has become a common approach for stock market prediction. In this paper, we propose a methodology using sentiment analysis on Twitter and StockTwits data for Stock movement prediction. The methodology was evaluated by analyzing stock movement and sentiment data. We present a case study focusing on Microsoft stock. We collected tweets from Twitter and StockTwits along with financial data extracted from Finance Yahoo. Sentiment analysis was applied on tweets, and two ML models namely SVM and Logistic Regression were implemented. Best results were achieved when using tweets from Twitter with VADER and SVM. Top F-score was 76.3% and top Area Under Curve (AUC) was 67%. SVM also achieves the greatest accuracy equal to 65.8%, when using StockTwits with TextBlob on this imbalanced data set.