Sentiment Analysis of Public Perception on Environmental Health Policies
A Text Mining Study Utilizing Hierarchical Clustering in Orange and VOSviewer
DOI:
https://doi.org/10.59680/ishel.v3i1.2255Keywords:
Bibliometric Analysis, CART Decision Tree, Environmental Governance, Environmental Health Policy, Hierarchical ClusteringAbstract
Environmental health policies play a vital role in reducing exposure to environmental hazards and advancing sustainable public health. Their effectiveness, however, depends not only on regulatory design but also on public perception, trust, and acceptance. This study applies a hybrid analytical framework combining bibliometric mapping with VOSviewer, thematic evolution analysis using SciMAT, hierarchical clustering through Orange data mining software, and a Classification and Regression Tree (CART) model to investigate sentiment patterns in public discourse on environmental health policies. A bibliometric dataset of Scopus-indexed publications from 2000 to 2024 was analyzed to identify thematic structures, intellectual development, and knowledge clusters. Simultaneously, a corpus of publicly available textual data related to environmental health regulations was processed using text preprocessing, TF-IDF feature extraction, hierarchical clustering, and sentiment classification techniques.The bibliometric analysis reveals three dominant thematic clusters: air pollution and health risk regulation, climate-related environmental governance, and community engagement with risk communication. Thematic evolution shows a transition from exposure-based epidemiological studies toward governance-oriented, equity-focused, and participatory frameworks. Hierarchical clustering distinguishes sentiment groups characterized by trust-oriented positive narratives, neutral informational discourse, and critical or distrust-driven perspectives. The CART model identifies trust-related lexical indicators, perceived economic burden, and transparency-related terms as the strongest predictors of sentiment polarity. Overall, this integrated scientometric and machine learning approach provides evidence-based insights into public opinion dynamics, offering strategic guidance for policymakers to enhance communication, strengthen trust, and improve environmental health policy acceptance and effectiveness.
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