Sentiment Analysis for Financial Market Prediction

Abstract

Financial markets are highly sensitive to investor sentiment, news events, social media discussions, and macroeconomic announcements. Traditional market prediction models primarily rely on historical price and volume data, often overlooking the behavioral aspects that significantly influence market movements. This paper presents a comprehensive sentiment analysis framework for financial market prediction using natural language processing and machine learning techniques. Financial news articles, social media posts, and corporate announcements are collected and preprocessed using tokenization, stop-word removal, stemming, and feature extraction methods such as TF-IDF and word embeddings. Sentiment scores are generated using lexicon-based and deep learning approaches, and these scores are integrated with market indicators to predict stock price direction. Experimental evaluation demonstrates that combining textual sentiment with technical indicators improves prediction performance compared with price-based models alone. The proposed approach achieves higher accuracy, precision, and recall in forecasting short-term market trends. The study highlights the importance of investor psychology in financial forecasting and demonstrates how sentiment-driven models can assist traders, portfolio managers, and financial institutions in making informed investment decisions. The proposed framework provides a scalable and data-driven solution for intelligent financial market prediction.