A Multi-Algorithm Model Selector for Sentiment Classification in Public Evaluations of Government Performance Using GovBERTic

Abstract

Social media has become a dynamic arena through which citizens express evaluations of government performance, yet the scale, heterogeneity, and informality of online discourse complicate systematic issue detection and sentiment interpretation. This study aims to develop GovBERTic (Government BERTopic), a reproducible computational pipeline that operationalizes public opinion analysis by linking semantic topic discovery, multi-algorithm sentiment model selection, and topic-level sentiment aggregation within a single workflow. The analysis used 7,877 tweets collected from X concerning the Prabowo–Gibran administration, which were cleaned and filtered to produce 6,569 valid tweets. BERTopic, supported by multilingual sentence embeddings, UMAP, HDBSCAN, and class-based TF-IDF, was applied to identify dominant discourse themes, while CatBoost, Multi-Layer Perceptron, and Complement Naïve Bayes were compared through a model selector for three-class sentiment classification. BERTopic identified eight main topics with a Topic Diversity score of 0.9125, covering government policy, development, food security, education, drug eradication, Papua, and Palestine. Complement Naïve Bayes achieved the best relative performance, with 64.61% accuracy and a 59.21% F1-score, indicating a moderate but computationally efficient baseline rather than definitive high-accuracy sentiment monitoring. Topic-level sentiment analysis showed that negative sentiment dominated four topics, neutral sentiment dominated three topics, and positive sentiment appeared in one topic. These findings suggest that GovBERTic can support exploratory, data-informed government communication analysis by connecting salient policy issues with their associated public sentiment patterns.