A Graph-Based Framework for Temporal and Causal Analysis of Sentiments

This research aims to develop a novel framework that uncovers the causal influence of global events on public sentiment through temporal graph modeling and neural causal inference. Global events, such as pandemics, elections, and economic crises, profoundly affect public sentiments, shaping social behaviors and economic outcomes. Traditional models often fall short in capturing the complex, dynamic, and non-linear relationships between these events and sentiments. This article presents the Neural Temporal Causal Graph Network (NTCGN), a unified framework that integrates temporal graph neural networks with a Causal Attention Network (CAN) to model and interpret these relationships. NTCGN constructs a temporal graph from event data and sentiment-labeled texts, learning dependencies and causal influences through advanced neural architectures. A thorough comparative analysis with state-of-the-art models such as Logistic Regression, SVM, LSTM, and transformer-based models demonstrates NTCGN’s superior performance. Experimental evaluation using the Sentiment140 and Global Database of Events, Language and Tone (GDELT) 2.0 datasets shows NTCGN achieving an accuracy of 0.798 and an F1 score of 0.795, outperforming these baseline models. The model’s causal inference capabilities are validated using the Causal Impact Score (CIS) and Causal Discovery Precision (CDP), highlighting its reliability in identifying true causal links. Visualizations of attention maps and causal pathways enhance interpretability, demonstrating how specific events influence public sentiments. This work provides a robust and interpretable tool for analyzing event-driven sentiment dynamics in real-world applications.