<![CDATA[Timor-Leste’s ASEAN integration triggers diverse public psychological responses. Grounded in Orange Technology, which leverages technology for social well-being, this study utilizes Natural Language Processing (NLP) and Machine Learning to evaluate public optimism and anxiety regarding this geopolitical shift. Public comments were harvested from YouTube, preprocessed, and classified using a Support Vector Machine (SVM) model. Positive sentiments were mapped as indicators of public optimism, while negative sentiments represented public anxiety. The experimental results revealed that 42.97% of the digital discourse exhibited positive sentiment, signaling collective optimism toward regional integration. Conversely, 16.10% negative sentiment highlighted specific anxieties regarding labor market competition and economic readiness. This study demonstrates how single-classifier NLP systems serve as effective emotional-sensing tools, providing actionable media intelligence for governments to design supportive communication strategies that foster public reassurance and happiness during major national transitions. This study highlights the potential of sentiment analysis to support evidence-based policy communication and strengthen public trust during Timor-Leste’s ASEAN integration process.]]>
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