Changing Aspects: Examining Financial Market Forecasting via Textual Representation - A Critical Evaluation

Social media news distribution has a big impact on the financial markets and causes noticeable adjustments. Deep learning-based natural language processing (NLP) methods have recently demonstrated remarkable potential in the analysis of these variations. However, comprehensive textual data must be integrated with financial market information in order to conduct an effective analysis of investor behavior. This research examines more than 150 publications on behavioral finance with an emphasis on combining NLP methods with market data analysis to support financial decision-making. This analysis, in contrast to others, focuses on computer science and artificial intelligence applications, emphasizing the integration of diverse data to assess investor behavior. The paper looks into different approaches to text representation, information retrieval, and sentiment analysis using a variety of data streams. It also looks at the routes that research in text mining and deep learning applications for financial markets is taking right now and in the future. These applications include forecasting, correlation analysis, and recommendation algorithms for a variety of asset classes, such as equities, Forex, and cryptocurrencies. The paper highlights the potential of integrating market data with advanced natural language processing (NLP) to augment our comprehension of investor behavior and streamline financial decision-making procedures by outlining several tactics and methodologies.