Sentiment of the Union: Analyzing Tone in Presidential State of the Union Addresses

Rydeen, Chase (2018) Sentiment of the Union: Analyzing Tone in Presidential State of the Union Addresses. Undergraduate thesis, under the direction of Dawn Wilkins from Computer Science, University of Mississippi.

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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.

Item Type: Thesis (Undergraduate)
Additional Information: Natural Language Processing, Machine Learning, Naive Bayes, Neural Network, Python, Data Science
Uncontrolled Keywords: Natural Language Processing, Machine Learning, Naive Bayes, Neural Network, Python, Data Science
Creators: Rydeen, Chase
Student's Degree Program(s): B.S. Computer Science
Thesis Advisor: Dawn Wilkins
Thesis Advisor's Department: Computer Science
Institution: University of Mississippi
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Depositing User: Mr. Chase Rydeen
Date Deposited: 09 May 2018 16:44
Last Modified: 09 May 2018 16:44
URI: http://thesis.honors.olemiss.edu/id/eprint/1083

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