Date of Graduation

5-2021

Document Type

Thesis

Degree Name

Bachelor of Science

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Zhan, Justin

Committee Member/Reader

Patitz, Matthew

Committee Member/Second Reader

Primack, Brian

Abstract

Twitter is a microblogging website where any user can publicly release a message, called a tweet, expressing their feelings about current events or their own lives. This candid, unfiltered feedback is valuable in the spaces of healthcare and public health communications, where it may be difficult for cancer patients to divulge personal information to healthcare teams, and randomly selected patients may decline participation in surveys about their experiences. In this thesis, BERTweet, a state-of-the-art natural language processing (NLP) model, was used to predict sentiment and emotion labels for cancer-related tweets collected in 2019 and 2020. In longitudinal plots, trends in these emotions and sentiment values can be clearly linked to popular cancer awareness events, the beginning of stay-at-home mandates related to COVID-19, and the relative mortality rates of different cancer diagnoses. This thesis demonstrates the accuracy and viability of using state-of-the-art NLP techniques to advance the field of public health communications analysis.

Keywords

bertweet, transformers, twitter, cancer, nlp, sentiment

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