Bridging AI and Financial Markets: A Sentiment Analysis Data-Driven Approaches for Stock Market Prediction - Vansh Momaya; Siddharth Hefa; Yash Bhate; Dr. Nilesh Marathe (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
The conventional financial value estimating techniques rely primarily on historical stock data, technical indicators, and fundamental parameters, and frequently ignore the psychological and sentiment-driven characters. The most research efforts in ML-based stock prediction models, focusing on decision fusion techniques, hybrid ensemble learning, deep neural networks, and sentiment-aware financial forecasting models. The recently designed time-series predicting models like RNN, CNN, and LSTM show a certain level of accuracy of the model. However, the market trend is influenced by social media mood, price volatility, investors’ mentality, and global cues of fiscal markets. Also, Hybrid approaches that collect information from social media platforms like Twitter, Reddit, financial news, and technical indicators have verified superior projecting accuracy than the conventional models that rely only on historical datasets. The decision fusion paradigm has gained traction in stock forecasting, allowing researchers to combine multiple prediction models to enhance forecasting precision. The effectiveness of ensemble learning models, including XGBoost, GBM, and AdaBoost, in stock price forecasting has been widely studied. DL models such as Transformer-based NLP models have further advanced sentiment analysis applications in finance. The ability of BERT to contextualize textual data and extract nuanced financial sentiment has led to significant improvements in sentiment-aware forecasting models. Additionally, Aspect-Based Sentiment Analysis has been employed to disaggregate financial news sentiment, enabling a more granular understanding of investor perceptions and economic events. The review identifies key limitations in current stock forecasting models, including overfitting issues, interpretability concerns, and data scarcity for rare financial events. Additionally, multimodal financial forecasting frameworks integrating textual, numerical, and visual data sources can provide more comprehensive market insights. The adoption of reinforcement learning-based trading strategies, coupled with real-time streaming sentiment analysis, holds significant potential for improving algorithmic trading performance. This paper bridges the gap between machine intelligence research and real-world stock market applications, contributing to the expanding field of AI-driven financial analytics.