A Review of Sentiment Analysis Tools in Predicting Stock Market Volatility: AI and Text Mining Approaches

Abstract

Abstract: This review article explores the use of sentiment analysis tools in predicting stock market volatility, focusing on artificial intelligence (AI) and text mining approaches. The objective is to provide a comprehensive analysis of the tools and methodologies used in sentiment analysis and their application in financial market predictions. The review employs a descriptive analysis of existing literature, highlighting key tools such as Google’s TensorFlow, IBM Watson, and lexicon-based models. The data for this study was gathered through a systematic review of academic databases and industry reports, focusing on sentiment analysis methods applied in stock market prediction. AI techniques such as machine learning and deep learning models were analyzed alongside text mining methods like natural language processing (NLP) and tokenization. The findings indicate that sentiment analysis tools significantly enhance the ability to predict stock market volatility by capturing the emotional and psychological factors influencing investor behavior. AI models offer powerful solutions for analyzing large volumes of text data, while text mining techniques provide structure and meaning to unstructured financial sentiment data. However, challenges related to accuracy, data quality, and computational complexity persist, as do biases and noise in sentiment data, which can affect the reliability of predictions. The integration of sentiment data with other financial indicators, such as technical analysis, offers opportunities for more accurate and robust predictions. The review concludes that while sentiment analysis tools have made substantial progress, further advancements in AI, NLP, and data integration are necessary to overcome current limitations. Future research should focus on improving model transparency and addressing the ethical implications of using AI in financial market predictions.