Abstract
Abstract: The Stocks uses the sentiment analysis in predicting stock market movements by analyzing data from sources like news, social media, and financial reports. Employing a Random Forest machine learning model, the system aggregates sentiment indicators, such as positive and negative emotions, to forecast stock trends and market fluctuations. The results show that sentiment analysis improves prediction accuracy, reduces investment risks, and supports data-driven decision-making. By integrating real-time data, the model adapts to changing market conditions and provides timely forecasts. This approach demonstrates the potential of sentiment-driven analysis to enhance investment strategies and create a proactive tool for financial market analysis.
Published Version
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