Author Type

Graduate Student

Date of Award

2023

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Khoshgoftaar, Taghi M.

Abstract

In today’s world, data is generated at an unprecedented rate, and a significant portion of it is unstructured text data. The recent advancements in Natural Language Processing have enabled computers to understand and interpret human language. Data mining techniques were once unable to use text data due to the high dimensionality of text processing models. This limitation was overcome with the ability to represent data as text. This thesis aims to compare the predictive performance of structured versus unstructured text data in two different applications. The first application is in the field of real estate. We compare the performance of tabular real-estate data and unstructured text descriptions of homes to predict the house price. The second application is in translating Electronic Health Records (EHR) tabular data to text data for survival classification of COVID-19 patients. Lastly, we present a range of strategies and perspectives for future research.

Extent

48 p.

Comments

Includes bibliography.|Thesis (MS)--Florida Atlantic University, 2023.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00014220

Format

pdf

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