Pandemic perceptions: analyzing sentiment in COVID-19 tweets

Social media, particularly Twitter, became the center of public discourse during the COVID-19 global crisis, shaping narratives and perceptions. Recognizing the critical need for a detailed examination of this digital interaction, our research dives into the mechanics of pandemic-related Twitter conversations. This study seeks to understand the many dynamics and effects at work in disseminating COVID-19 information by analyzing and comparing the response patterns displayed by tweets from influential individuals and organizational accounts. To meet the research goals, we gathered a large dataset of COVID-19-related Tweets during the pandemic, which was then meticulously manually annotated. In this work, task-specific transformers and LLM models are used to provide tools for analyzing the digital effects of COVID-19 on sentiment analysis. By leveraging domain-specific models RoBERTa[Twitter] fine-tuned on social media data, this research improved performance in critical task of sentiment analysis. Investigation demonstrates individuals express subjective feelings more frequently compared to organizations. Organizations, however, disseminate more pandemic content in general.

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher

Colorado State University. Libraries

Year dc:date.issued

2023

Authors dc:creator

• Bashir, Shadaab Kawnain, author
• Ray, Indrakshi, advisor
• Shirazi, Hossein, advisor
• Wang, Haonan, committee member

• sentiment analysis
• COVID-19

Statement dc:rights

• Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.

Language dc:language.iso

eng, English

Identifier URI

https://doi.org/10.25675/3.02297

OAI identifier oai:identifier

oai:mountainscholar.org:10217/237360