Degree Name

MS (Master of Science)


Computer and Information Sciences

Date of Award


Committee Chair or Co-Chairs

Brian Bennett

Committee Members

Ahmad Al Doulat, Ghaith Hussum Husari


The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model’s ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not contain sentiment analysis-related features. The results indicated that sentiment containing datasets were typically better predictors, with improved model accuracy. However, the results did not reflect the improvements shown by similar research and will require further research to determine the nature of the relationship between sentiment and higher model performance.

Document Type

Thesis - unrestricted


Copyright 2023 by Ian Grisham All Rights Reserved