Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Science

Program

Computer Science

Collaborative Specialization

Artificial Intelligence

Supervisor

Robert E. Mercer

Abstract

Recent NLP advancements have improved the state-of-the-art in well-known datasets and are appealing more attention day by day. However, as the models become more complicated, the ability to provide interpretable and understandable results is becoming harder so the trade-off between accuracy and interpretability is a concern that is yet to be addressed. In this project, the aim is to utilize state-of-the-art NLP models to provide meaningful insight from psychological real-world documents that contain complex structures. The project involves two main chapters each including a different dataset. The first chapter is related to binary classification on a personality detection dataset, while the second one is about sentiment analysis and Topic Modeling of sleep-related reports.

Summary for Lay Audience

Recent advancements in artificial intelligence models that accept textual inputs are becoming more and more accurate. However, because of the differences between the nature of the artificial intelligence models and human functioning, understanding the AI outputs are becoming harder for humans. In this project, the aim is to utilize top AI models in the field of natural language processing to provide meaningful insight from psychological real-world documents that contain complex structures. The project involves two main chapters each including a different dataset. The first chapter is related to binary classification on a personality detection dataset, while the second one is about sentiment analysis and Topic Modeling of sleep-related reports.

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