Enhancing Multi-Step Stock Price Forecasting with Social Media Sentiment and Engagement Metrics

Abstract

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including social media sentiment modestly improves predictive performance, particularly in short-term horizons, and that sentiment signals are often most predictive around major company events. However, cross-company generalization remains limited, underscoring the importance of firm-specific tuning. The findings highlight both the potential and the boundaries of using social sentiment in financial modeling and point to future opportunities in multi-platform integration, richer feature extraction, and adaptive learning strategies.