Harvesting social media sentiment analysis to enhance stock market prediction using deep learning

Abstract

Information gathering has become an integral part of assessing people’s behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public’s views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data mining techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company’s stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naïve Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology.

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

“…Hao et al (2021) analyzed financial news and stock price data of Taiwan-based companies to predict the stock price trends using fuzzy SVMs and traditional SVMs, where hybrid fuzzy SVMs reported superior prediction accuracy. Mehta et al (2021) utilized financial news as input data into their machine learning and deep learning models, and their results reported more than 80% accuracy, validating the success of the proposed methodology. Additionally, researchers have incorporated sentiment analysis into prediction models to improve prediction accuracy, a topic that has gained widespread interest (Maknickiene et al 2018).…”

“…Even though most of the studies have analyzed stocks from developed countries, equity stock markets from emerging nations are also considered in some works. From Asia, these include: the Korean stock market (Baek and Kim 2018;Oh and Kim 2002); the Chinese stock market (Baek and Kim 2018;Cao et al 2011;Chen et al 2018;Oh and Kim 2002); the Indian stock market (Bisoi and Dash 2014;Mehta et al 2021); the Malaysian stock market (Sagir and Sathasivan 2017); the Thailand stock market (Inthachot et al 2016); the Taiwan stock market (Hao et al 2021;Wei and Cheng 2012); the Philippines stock market ; the Indonesian stock market (Situngkir and Surya 2004); and the Bangladesh stock market (Mahmud and Meesad 2016). From Latin America, this includes the Brazilian stock market (De Oliveira et al 2013;De Souza et al 2012).…”

Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak

“…Mehta et al developed and deployed a technique for predicting the accuracy of stock prices that takes public opinion into account in addition to other characteristics. To estimate future stock prices, the suggested algorithm takes into account public sentiment, opinions, news, and past stock prices (Mehta et al 2021 ).…”

Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods

“…Stock market forecasting refers to the actions made to provide interested parties, such as investors and customers, with a predictable picture of the future direction and variation of the object price. Investors could make successful decisions or prevent losses if they could accurately forecast future stock prices (Singh et al 2019(Singh et al , 2021Sunny et al 2020;Lin et al 2020;Shynkevich et al 2017;Mehta et al 2021;Zhuo et al 2021).…”