Sentiment Analysis for Financial Markets

Abstract

Abstract: Predicting movements in the stock market is a novel use of sentiment analysis's growing body of knowledge. The purpose of this study is to investigate the potential of NLP for predicting stock market movements by analyzing textual data sources such as news articles, social media posts, as well as earnings reports. The research examines current approaches, applications, and difficulties in sentiment analysis by drawing on extensive surveys and reviews [1], [2]. It also investigates the use of pre-trained models in NLP and the potential biases of such models [6]. Important research findings [3], [17] suggest that NLP-based sentiment analysis models, especially those employing deep learning architectures, show promising results in financial forecasting. There are, however, several difficulties associated with these models. These include the requirement of huge datasets and the elimination of biases. This study has far-reaching ramifications. One benefit is a more nuanced comprehension of the potential and pitfalls of natural language processing for sentiment analysis in the financial markets, which is useful for both analysts and investors. Second, it provides opportunities for more study to enhance the precision and dependability of such models, which ultimately aids in the development of more steady and well-informed monetary judgements.