Hybrid Sentiment and Deep Graph Learning for Stock Trend Analysis

Abstract

Stock trend analysis plays a crucial role in financial forecasting and investment decision-making. However, accurately predicting stock market movements remains a challenging task due to market volatility, nonlinear dependencies, investor sentiment fluctuations, and the complex interactions among financial entities. Traditional forecasting models often rely solely on historical price information and fail to capture the influence of market sentiment and inter-stock relationships. Recent advances in sentiment analytics, graph neural networks, and deep learning have created opportunities for developing intelligent financial prediction frameworks capable of improving trend forecasting performance. This research proposes a Hybrid Sentiment and Deep Graph Learning Framework for Stock Trend Analysis that integrates financial data analytics, sentiment extraction, graph-based relationship modeling, deep feature learning, and intelligent trend forecasting into a unified architecture. The framework analyzes stock prices, financial indicators, news sentiment, and social media sentiment while constructing dynamic financial graphs that represent relationships among stocks, sectors, and market entities. A deep graph learning model captures structural dependencies within the financial network and combines them with sentiment-aware representations to generate accurate stock trend predictions.