Critical review of text mining and sentiment analysis for stock market prediction

The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research

Customer and Employee Social Media Comments/Feedback and Stock Purchasing Decisions Enhanced by Sentiment Analysis

The U.S. Securities and Exchange Commission (SEC) warns professional investors that sentiment analysis tools may lead to impulsive investment decision-making. This warning comes despite evidence showing that aided social sentiment investment decision tools can increase accurate investment decision-making by 18%. Using Fama\u27s theory of efficient market hypothesis, the purpose of this quantitative correlational study was to examine whether customer Twitter comments and employee Glassdoor feedback sentiment predicted successful investing decisions measured by business stock prices. Two thousand records from 3 archival U.S. public NASDAQ 100 datasets from March 28, 2016, to June 15, 2016 (79 days) of 53 companies with over 100 comments were analyzed using multiple linear regression. The multiple regression analysis results indicated no significant predictability for successful investing decisions, F(10, 2993) = .295, p = .982, R2 = .001. The results indicated that the sentiment from both Twitter and Glassdoor was not necessarily an indicator for investors to make successful investment decisions for the 79 days in 2016. The knowledge about Artificial Intelligence (AI) sentiment usage may help professional investors gain profit or prevent losses. A recommendation to investors is to heed warnings from the SEC about tools for sentiment analysis investment decisions. Implications for positive social change include preventing an investor from using a risky sentiment tool for investment decision-making that may lead to losing capital