Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

J. Edward Swan II

Committee Member

Shahram Rahimi

Committee Member

Sujan Ranjan Anreddy

Date of Degree

8-6-2021

Original embargo terms

Visible to MSU only for 6 months

Document Type

Graduate Thesis - Campus Access Only

Major

Computer Science

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Throughout the COVID-19 pandemic, people have turned to social media to share their experiences with the coronavirus and their feelings regarding subjects like social distancing, mask-wearing, COVID-19 vaccines, and other related topics. The publicly available nature of these social media posts provides researchers the chance to obtain a consensus on an array of issues, topics, people, and entities. For the COVID-19 pandemic, this is valuable information that can prepare communities and governing bodies for future epidemics or events of a similar magnitude. However, clearly defining such a consensus can be difficult, especially if researchers want to limit the amount of bias they introduce. The process of sentiment analysis helps to address this need by categorizing text sources into one of three distinct polarities. Namely, those polarities are often positive, neutral, and negative. While sentiment analysis can take form as a completely manual task, this becomes incredibly burdensome for projects that involve substantial amounts of data. This thesis attempts to overcome this challenge by programmatically classifying the sentiment of COVID-19 posts from 10 social media and web-based forums using a multinomial Naive Bayes classifier. The unique and contrasting qualities of the social networks being analyzed provide a robust take on the public's perception of the pandemic that has not yet been offered up to the present.

Sponsorship

National Science Foundation (Award #2031246)

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