A Critical Review Of Machine Learning Approaches To Sentiment Analysis For Stock Market Prediction

Abstract

The rapid evolution of machine learning (ML) technologies and their transformative impact on numerous industries has garnered significant interest in their potential for financial market analysis. Given stock markets' volatility and economic significance, understanding and predicting their behaviour is a crucial yet challenging task. This study critically examines the various ML approaches to sentiment analysis for stock market prediction. The primary objective is to synthesise research findings to assess the efficacy of ML models in this domain. While ML models show promise, their accuracy in predicting market movements varies significantly depending on data quality, model complexity, and contextual factors. It also discusses the limitations of current approaches and the need for more robust and adaptable models. The findings suggest that advancements in ML algorithms and data preprocessing techniques could significantly enhance predictive accuracy. This synthesis aims to guide future research towards addressing these gaps and improving sentiment analysis for financial market predictions.