Sarcouncil Journal of Applied Sciences Aims & Scope

Abstract: The announcements of declaring tariffs and other trade policies are some of the most significant economic data whose impacts are massive on the financial markets. The effects of such announcements will be an immediate investment response anchored in the perceived economic shock triggered by collective public sentiment and media coverage. The current trends in profound sentiment categorization of policy events relating to tariffs, i.e., the combination of natural language processing, machine learning models, and time series forecasting, are taken into account in this literature review. The generalization of the paper is the results of different datasets, structures, and financial conditions, which demonstrate the benefits of sentiment analysis to transform the market into a more predictive and less risky environment. It also concerns the macroeconomic consequences of tariffs in the perspective of the shift in sentiment, and attributes of working and ethical features of sentiment-based forecasting instruments. The description of emotion where the interlocutor between financial economics and artificial intelligence creates the general picture of how the existing computational tools are used to inform the behavior of the investors and in the price and policy consideration of the assets in the paper.