Sentiment Analysis and Topic Detection on Post-Pandemic Healthcare Challenges: A Comparative Study of Twitter Data in the US and Indonesia

George Morris William Tangka; Ibrena Reghuella Chrisanti; Jacquline Waworundeng; Raissa Camilla Maringka; Green Arther Sandag

doi:10.31154/cogito.v10i2.819.561-579

Authors

George Morris William Tangka Universitas Klabat
Ibrena Reghuella Chrisanti PT. Vfirst Komunikasi Indonesia
Jacquline Waworundeng Universitas Klabat
Raissa Camilla Maringka Universitas Klabat
Green Arther Sandag Universitas Klabat

DOI:

https://doi.org/10.31154/cogito.v10i2.819.561-579

Keywords:

Sentiment Analysis, Topic Modeling, Social Media, Omicron, COVID-19 Vaccine

Abstract

This study examines public sentiment and key topics in Twitter discussions regarding the COVID-19 vaccine and the Omicron variant in the US and Indonesia. The importance of this research lies in understanding people's changing views on vaccination, especially in light of new virus variants. Using sentiment analysis with VADER and topic modeling with Latent Dirichlet Allocation (LDA), this research analyzes 637,367 tweets from the US and 91,679 tweets from Indonesia collected over two months from January 21 to February 21, 2022. The results reveal that US discussions on vaccines are predominantly positive, while those on Omicron are mostly negative. In contrast, discussions in Indonesia are largely neutral, followed by positive sentiment. Additionally, five main topics were identified for each country, with the US showing a broader range of vaccine-related discussions. These findings suggest that while the vaccine is seen as a source of hope in both countries, factors such as literacy, socioeconomic status, and education contribute to negative sentiment and vaccine resistance.

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