Predicting Public Sentiment to Proposed Laws: E-waste Management Laws as a Use Case

Machine learning has been successfully used to predict sentiment for general-purpose short-text sentences with relative accuracy. However, challenges around predicting legislation acceptance call for deeper analysis of longer, more interconnected text paragraphs. The E-waste management domain has a pressing need for insights into public discourse on legislation. This work presents a comprehensive approach to analyzing public discourse surrounding e-waste legislation by integrating social media data from Twitter and legislative data from LegiScan. Graph Machine Learning (GraphML) is utilized to build the prediction engine. A heterogeneous graph is constructed to capture the relationships between tweets and legislative bills using API-based data collection, preprocessing techniques, and pre-trained sentiment models. The ML pipeline includes robust preprocessing of text data, feature extraction using RoBERTa embeddings, dimensionality reduction via PCA, and the use of various Graph Neural Network (GNN) architectures (Graph Convolutional Network (GCN), Graph Attention Network (GAT), and GraphSAGE) for model training and evaluation. The final model achieved a test accuracy of 0.777, a test F1-score of 0.745, and a test ROC AUC of 0.819, demonstrating the efficacy of graph-based sentiment analysis in this context. This work's findings have significant implications for policymakers, stakeholders, and researchers in the e-waste management domain. Policymakers can use the model's sentiment analysis insights to gauge public support for specific legislative actions, providing informed and responsive decision-making. E-waste management Stakeholders can understand public opinion around e-waste legislation and develop effective communication strategies. Additionally, the proposed methodology provides an end-to-end framework for researchers to explore further applications of graph-based sentiment analysis.