ORCID Identifier(s)

0000-0003-3744-4847

Graduation Semester and Year

2021

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

A Ramez Elmasri

Abstract

Leaders generally include government officials, politicians, etc. Their statements can highly affect people’s decisions in many ways. Currently, in the pandemic situation, many statements were being passed every hour and day, which showed an impact on the spread of corona virus cases at certain location. So, this paper proposes a supervised model to analyze the variations of COVID-19 data based upon the leader’s statements passed at certain time and location. The proposed methodology consists of sentiment and emotion analysis for the leader’s statements to determine the true intentions of the leader. The leader’s statements are a collection of data obtained by scraping webpages of popular news channels like CNN. And the COVID-19 data has been extracted from the largest collection, which is accumulated by John Hopkins University, every day. Then, NLP text processing techniques were used to prepare the dataset and pre-process the text, through which we obtain a labelled dataset. From this, our methodology includes analyzing the obtained dataset.

Keywords

COVID-19, NLP text processing, Sentiment and emotion

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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