Hybrid Sentiment Analysis of Public Perception on Indonesia’s Role in the Board of Peace Using Inset Lexicon and Support Vector Machine - Rahmat Hidayat (Journal of Applied Informatics and Computing, 2026)
Although sentiment analysis is increasingly utilised in Indonesian social media, there is a paucity of rigorous studies assessing hybrid lexicon–machine learning frameworks in the realm of foreign policy debate. This study fills this gap by presenting a hybrid sentiment analysis model that combines the domain-specific InSet Lexicon with Support Vector Machine (SVM) classification to analyse public perception of Indonesia’s involvement in the Board of Peace program—a United States-led international peace initiative in the Middle East region. This research utilises computational sentiment modelling in international peace diplomacy, a largely neglected area in Indonesian text mining, in contrast to prior studies that primarily concentrate on product reviews or local policy issues. A dataset comprising 1,454 Twitter (X) postings was collected and subjected to systematic preprocessing including case folding, tokenisation, slang normalisation, stopword removal, and Sastrawi-based stemming. The preprocessed data was automatically annotated utilising the InSet lexicon to produce pseudo-labels and subsequently classified employing SVM with two feature representation methodologies: TF-IDF and Word2Vec. Experimental findings indicate that the TF-IDF-based hybrid model attained the highest classification accuracy of 84% on the testing dataset, correctly predicting 1,221 out of 1,454 instances, surpassing the Word2Vec method (69%). Detailed per-class evaluation using precision, recall, and F1-score revealed strong negative-class performance (F1 = 0.91) while the neutral class remained most challenging due to class imbalance, with a macro-F1 of 0.70. A single 60:40 train-test split was applied; the pseudo-labelled nature of the dataset is acknowledged as a limitation requiring future manual validation. The results indicate that statistical term-weighting techniques are more resilient than semantic embedding representations in the context of domain-specific Indonesian policy discourse. This study methodologically contributes by empirically comparing feature representation options within a hybrid lexicon–SVM framework and substantively by offering computational evidence of polarised public attitude toward Indonesia’s diplomatic engagement. The findings underscore the significance of domain-specific lexicons in enhancing sentiment classification efficacy in low-resource language settings.