Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market

Online stock forums have become a vital investing platform for publishing relevant and valuable user-generated content (UGC) data, such as investment recommendations that allow investors to view the opinions of a large number of users, and the sharing and exchanging of trading ideas. This paper combines text-mining, feature selection and Bayesian Networks to analyze and extract sentiments from stock-related micro-blogging messages called "StockTwits". Here, we investigate whether the power of the collective sentiments of StockTwits might be predicted and how these predicted sentiments might help investors and their peers to make profitable investment decisions in the stock market. Specifically, we build Bayesian Networks from terms identified in the tweets that are selected using wrapper feature selection. We then used textual visualization to provide a better understanding of the predicted relationships among sentiments and their related features.