A Systematic Review of Graph Neural Networks and Social Network Analysis Techniques for Public Sentiment Uncovering

The rapid growth of social media has produced large-scale, highly interconnected user-generated data, creating the need for analytical approaches that can capture both textual meaning and relational structure. This systematic literature review examines the integration of Graph Neural Networks (GNNs) and Social Network Analysis (SNA) for public sentiment uncovering in social media. Following a PRISMA-based review process, 75 studies were selected from ScienceDirect and IEEE Xplore. The synthesis shows that recent research has expanded beyond direct sentiment classification to include closely related tasks that improve sentiment reliability, including misinformation detection, rumor analysis, bot detection, anomaly detection, and recommendation personalization. Within the reviewed sentiment-oriented studies, the thematic distribution indicates that 32% focus on direct sentiment or emotion analysis, 29% on misinformation or rumor detection, 24% on malicious-user, bot, or anomaly detection, and 15% on community detection or link prediction. Hybrid models consistently reported strong empirical gains, including 95.25% accuracy for GNN–LSTM sentiment classification, improvements of more than 5% over baseline in heterogeneous neural network and language-model integration, and up to 98.4% accuracy/F1 in bot detection settings. The review also identifies key limitations related to scalability, noisy and incomplete data, interpretability, class imbalance, and cross-platform generalization. In response, it proposes future research directions centered on real-time graph learning, multilingual adaptation, emotion-aware graph representations, fairness-aware evaluation, and human-in-the-loop explainability. These findings provide a clearer methodological foundation for researchers and practitioners seeking to build more robust, explainable, and socially aware sentiment analysis systems.