Stock Market Sentiment Analysis

Abstract

Now, more than ever, data mining is finding its way into practical business usage. Data mining and data visualization techniques are creating pathways to solve complex issues and are empowering decision-makers in determining best business practice and strategy. Business managers and leaders can use data mining and data visualization to generate insights and create value (including financial value) in numerous ways. Two of the major applications involve textual data mining and behavioral analytics. Textual data mining involves a type of analysis in which valuable information is derived from high volumes of text-based data. Whereas, behavioral analytics allows data scientists to derive meaning from customer behavioral data and answer questions like why one product is preferred over a similar product. The analysis in this study revolves largely around sentiment analysis which is both textual and behavioral. In particular, there was great interest in determining whether any value could be derived through sentiment analysis regarding success within the stock market. Various aspects of human behavioral traits were utilized including categories like confidence level, goals/motivations, and strategy.