Deep Learning Sentiment Analysis for Stock Forecasting

Abstract

The review focuses on the effectiveness of deep learning based social media sentiment analysis in predicting stock performance within the finance sector. It finds that such approaches generally improve forecasting accuracy by capturing market sentiment nuances. However challenges remain in data noise management and model interpretability. Overall integrating sentiment signals enhances stock prediction models’ robustness and timeliness. This review synthesizes research on "Effectiveness of deep learning driven social media sentiment analysis in forecasting stock performance in the finance sector" to address challenges in integrating heterogeneous data and improving predictive accuracy amid market volatility. The review aimed to evaluate deep learning architectures applied to social media sentiment for stock forecasting, benchmark integration with financial indicators, analyze sentiment extraction methods, compare model effectiveness in capturing temporal and sentiment dynamics and identify limitations in handling market uncertainty. A systematic analysis of global studies employing LSTM, CNN, transformer and hybrid models revealed consistent accuracy improvements when combining sentiment with technical and macroeconomic data, with transformer based and multi modal approaches achieving up to 90% accuracy. Domain specific sentiment models enhanced contextual relevance, though ambiguity and data noise remain obstacles. Integration of sentiment and financial indicators improved forecasting robustness but introduced complexity and challenges in feature fusion. Explainable AI methods emerged to increase model transparency, yet interpretability is generally limited. Overall deep learning driven sentiment analysis demonstrates significant potential to enhance stock performance prediction, though standardization in evaluation and improved handling of volatility and data heterogeneity are needed. These findings inform future research directions and practical applications in financial forecasting and investment strategy development.