Date of Award

Spring 6-13-2025

Document Type

Masters Thesis

Degree Name

M.S. in Computer Science

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science

First Advisor

Stephen Hutt

Second Advisor

Mohammad Mahoor

Third Advisor

Nathan Evans

Fourth Advisor

Maria Calbi

Copyright Statement / License for Reuse

All Rights Reserved
All Rights Reserved.

Keywords

Machine learning, Natural language processing, Predictive modeling, Sentiment analysis, Stock price prediction, Social media

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.

Copyright Date

6-2025

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Damilare Olaniyan

Provenance

Received from ProQuest

File Format

application/pdf

Language

English (eng)

Extent

123 pgs

File Size

4.8 MB

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