<![CDATA[The Free Nutritious Meal Program (MBG) has generated extensive public discussion on platform X. This study develops a reliability-aware framework for mapping public issue priorities by integrating sentiment analysis, probability calibration, and topic modeling. A final dataset of 1,641 public X posts was de-identified, preprocessed, relevance-filtered, manually labeled, validated, and proportionally split. TF-IDF + SVM was used as a classical baseline, while IndoBERTweet was fine-tuned and calibrated using temperature scaling. BERTopic was applied to generate topics, followed by manual interpretation into substantive issue groups. TF-IDF + SVM outperformed IndoBERTweet, achieving 0.8138 accuracy and 0.7999 Macro-F1, while IndoBERTweet achieved 0.7611 accuracy and 0.7142 Macro-F1. IndoBERTweet was retained because it provides probability-based confidence scores for calibration and priority mapping. Calibration modestly reduced NLL from 0.5239 to 0.5199 and ECE from 0.0700 to 0.0682. BERTopic produced 19 non-noise topics with a coherence score of 0.4552. The highest-priority public discussion theme concerned food safety, poisoning-related discourse, and consumption quality. This framework provides initial public-opinion monitoring input, not definitive policy evaluation, and requires external validation before formal policy use.]]>
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