Public Discourse on Waste Sorting Policy Sentiment, Opinion Expression, and Topic Analysis of YouTube Comments
Abstract
Waste-sorting policies require not only public participation but also effective communication and adequate implementation support. This study examines public discourse surrounding Jakarta’s waste-sorting policy through 413 YouTube comments, combining sentiment analysis, opinion-expression analysis, and topic modelling. Sentiment was classified using IndoBERT and SentiStrength, while human coding was used to validate model performance. Opinion structure and expression mode were examined using Liu’s framework, and Latent Dirichlet Allocation (LDA) was applied to identify major discussion topics. The results show that negative sentiment dominated the discourse, with IndoBERT classifying 62.0% of comments as negative. IndoBERT also showed stronger agreement with human coders than SentiStrength. Most opinions were expressed through regular-direct structures and explicit expressions, indicating that users generally communicated their evaluations openly, although model disagreement remained across different forms of expression. LDA identified three main topics: Waste Sorting Infrastructure and Collection Practices (37.8%), Government Performance and Policy Accountability (36.1%), and Waste Disposal Behavior and Environmental Awareness (26.2%). Negative sentiment was particularly prevalent in discussions of infrastructure and collection practices (65.4%) and government performance and accountability (63.8%). These findings suggest that negative sentiment should not be interpreted simply as opposition to waste sorting, but also as public concern about institutional and operational constraints. From a communication perspective, the findings highlight the importance of treating social media discourse as public feedback and developing two-way policy communication that addresses implementation barriers, clarifies institutional responsibilities, and responds to citizens’ concerns.
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