Sentiment Analysis and Topic Modelling Using IndoBERTweet and BERTopic for Public Health Issues

Abstract

Background: Public health challenges in Indonesia continue to expand across areas such as mental health, chronic diseases, vaccination debates, and environmental issues. Specific background: The rapid use of platform X provides large-scale public discourse that can be analyzed to understand real-time health discussions. Knowledge gap: Limited studies integrate advanced sentiment and topic modeling tailored to informal Indonesian social media language. Aim: This study analyzes public health conversations on platform X using IndoBERTweet for sentiment classification and BERTopic for topic extraction. Results: From 6,740 processed tweets, neutral sentiment dominated public discussions, while topic modeling produced 44 themes, with mental well-being, vaccination debates, chronic disease concerns, and regional disease reports emerging as key issues. IndoBERTweet demonstrated reliable performance (Weighted F1-Score 0.7822), and BERTopic produced coherent and diverse topics. Novelty: This research combines IndoBERTweet and BERTopic to generate a contextual, adaptive, and real-time mapping of public health discourse in Indonesia. Implications: The findings support data-driven health policymaking, enabling authorities to monitor public perceptions, strengthen communication strategies, and design region-specific interventions. Highlights • Public conversations emphasize mental health and lifestyle-related issues. • Topic modeling identifies diverse clusters, including vaccination debates and endemic diseases. • Integrated sentiment–topic analysis enables real-time mapping of health discussions in Indonesia. Keywords Public Health, Social Media Analysis, IndoBERTweet, BERTopic, Sentiment Classification