Analysis of Stock Price Flow Based on Social Media Sentiments

Abstract

Prediction of mood uses the sentiment word lists obtained in various sources where general state of mood can be found using such word list or emotion tokens. With the number of messages posted on Stock Twits, it is believed that the general state of mood can be predicted with certain statistical significance. This paper explores the relationship between Stock Twits messages relationship with stock market movement, and how well, sentiment extracted from these feeds can be related to the shifts in stock prices. For this case we chose Apple Inc to perform the analysis, and estimate its accuracy.