Aspect-Based Sentiment Analysis of Public Opinion on the Free Nutritious Meal Program using BERTopic on X - Carmen Emanuela Dwiva Lisapaly; Luther Alexander Latumakulita; Rillya Arundaa (Journal of Artificial Intelligence and Technology Information (JAITI), 2026)
This study aims to analyze public opinion on the Free Nutritious Meal (MBG) Program on the X platform using an Aspect-Based Sentiment Analysis (ABSA) approach with BERTopic-based aspect extraction. Unlike previous studies that primarily perform sentiment classification at the overall text level, this study identifies specific aspects within public discussions to provide more fine-grained insights. Twitter data were collected and preprocessed, followed by topic modeling using BERTopic to extract topics that were subsequently defined as aspects. Topic quality was evaluated using topic coherence (c_v) and topic diversity metrics. The modeling process initially produced 36 topics with a coherence score of 0.4446 and a diversity score of 0.8541. After relevance-based selection, 18 topics were retained as aspects, with the coherence score increasing from 0.4446 to 0.5370 and the diversity score increasing from 0.8541 to 0.8611. Sentiment labeling was then performed using the Twitter-XLM-RoBERTa model to determine the distribution of positive, negative, and neutral sentiments across each aspect. The results demonstrate that the proposed ABSA approach with BERTopic-based aspect extraction provides a more structured and insightful mapping of public opinion, enabling the identification of aspects with the highest levels of support and indications of opposition toward the MBG Program. These findings are expected to serve as a basis for consideration in data-driven policy evaluation and support more informed decision-making.