Honors Theses
Date of Award
2018
Document Type
Undergraduate Thesis
Department
Computer and Information Science
First Advisor
Dawn Wilkins
Relational Format
Dissertation/Thesis
Abstract
As the machine learning and data science craze sweeps the nation, the implications and implementations are vast. This paper takes a look at both of them through the lens of a topic of national importance, at the very least for the United States. This topic is the words used by past Presidents of the United States, which are being pulled from their State of the Union Addresses. The focus of this research is on Natural Language Processing (NLP) and it's applied processes. Natural Language Processing allows for effective analysis of text-based data. Using NLP, a sentiment analysis was conducted on the Addresses to gain further insight into the tone used by Presidents over the course of history. This sentiment analysis ultimately resulted in a set of sentiment scores pertaining to major topics in the United States. These sentiment score sets were then input in to several different learning algorithms in an attempt to utilize Presidential Sentiment to predict political party affiliation. This paper shares the methodology used to conduct this sentiment analysis and discusses the tools created for the analysis and visualizations.
Recommended Citation
Rydeen, Chase, "Sentiment of the Union: Analyzing Tone in Presidential State of the Union Addresses" (2018). Honors Theses. 186.
https://egrove.olemiss.edu/hon_thesis/186
Accessibility Status
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