Artificial intelligence prediction of stock prices using social media

Twitter is a microblogging and social media platform that allows users to communicate via short messages (280 characters) known as tweets [1, 2, 3]. It enables millions of users to express their opinions on a daily basis on a variety of different topics ranging from reviews on products and services to users' political and religious views, making Twitter a potent tool for gauging public sentiment [4]. Thus, it manifestly follows that twitter data can be regarded as a corpus, forming the basis on which predictions can be made, and researchers have indeed exploited this fact to seek trends by performing numerous and varied analyses. A characteristic feature of the stock market is volatility and there is no general equation describing the prediction of stock prices, which is a complex function of a range of different factors. The methods of stock market prediction can be broadly classified into Technical Analysis and Fundamental Analysis [5]. The latter involves the consideration of macroeconomic factors as well as industry specific news and events to guide investment strategies [5]. The analysis of public sentiment via tweets performed in this project can be regarded as an aspect of Fundamental Analysis. Although the prediction of stock prices is highly nuanced, the Efficient Market Hypothesis (EMH), propounded by Eugene Farma in the 1960's, suggested a relation between public opinion and stock prices [6]. The semi-strong form of the EMH implies that current events and new public information have a significant bearing on market trends [1, 6].