Emotion Analysis of News and Social Media Text for Stock Price Prediction using SVM-LSTM-GRU Composite Model

Abstract

Researchers are putting forth significant effort to develop accurate methods and techniques for stock market forecasting. It has emerged as a particularly difficult area of investigation. In addition to other cues, stock market prices are highly dependent on the mood of the stakeholders, which can be gauged through the analysis of news and social media data, as well as other indicators. A number of aspects of stock price prediction based on new data are discussed in this paper, which also attempts to establish a relationship between the polarity of news and the price of a stock for a given company. The experimental outcomes prove the capability of the proposed technique for predicting the stock prices for a certain point in time. The information used in the analytics has been gathered from seven different sources, including six financial news portals and one social media platform, according to the report. Additionally, various issues such as challenges, shortcomings, and future research directions have been discussed.