A Comprehensive Review of Stock Market Price Prediction through News Sentiment Analysis and Machine Learning Approaches
Keywords:
Stock Market Prediction, News Sentiment Analysis, Machine Learning, Deep Learning, Financial ForecastingAbstract
Stock market prediction has long been an area of interest for researchers, investors, and policymakers due to its potential to minimize risks and optimize decision-making. Traditional forecasting methods, relying on fundamental and technical analysis, have proven insufficient in capturing the dynamic, non-linear, and sentiment-driven nature of modern financial markets. With the increasing availability of unstructured data from financial news, press releases, and social media platforms, news sentiment analysis has emerged as a crucial tool for understanding investor psychology and its influence on price fluctuations. Simultaneously, advancements in machine learning (ML) and deep learning have enabled the development of models capable of processing high-dimensional datasets and identifying hidden patterns that conventional methods often overlook.
This review provides a comprehensive synthesis of studies that integrate sentiment analysis with machine learning techniques for stock price forecasting. It discusses the effectiveness of classical algorithms, ensemble methods, and deep learning architectures such as LSTM, CNN, and transformers in improving predictive accuracy. Furthermore, the review identifies key challenges—including data quality, scalability, overfitting, and model interpretability—while outlining promising directions such as multimodal data fusion, real-time sentiment streaming, explainable AI, and cross-market applicability. By critically evaluating existing literature, this study highlights the transformative potential of sentiment-driven machine learning models in shaping the future of financial forecasting.
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