Utilizing Machine Learning to Analyze Investor Sentiment and Detect Market Anomalies

This research paper's goal is to examine machine learning techniques and how they are used to gauge investor sentiment and identify market abnormalities. Strong machine learning methods are used in this research project to assess sentiment trends and how they affect consumer behavior. Large datasets drawn from trade activity, social media, and financial news are used to carry out this review. The purpose of this study is to identify emotional patterns in investors that are associated with anomalous market movements, like abrupt price changes or volatility spikes. This study's goal is to try and pinpoint these trends. This study assesses several machine learning algorithms to find out how effectively they can forecast these anomalies. Neural networks, Random Forests, and Support Vector Machines (SVM) were some of these techniques. The sentiment analysis module performed incredibly well; the SVM classifier accurately predicted. Consequently, the research provides valuable perspectives on the real-world implementation of these algorithms in the financial markets. The results enhance our comprehension of the connection between market dynamics and investor mood, which benefits the creation of more efficient risk management strategies and better-informed decision-making by financial institutions and investors alike. The results of this study show that machine learning has the potential to be a useful tool for enhancing market analysis and identifying early warning signs of market disruptions. Machine learning is capable of serving as this kind of tool.