SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling
Year: 2025
Type: conference-paper
Abstract: User reviews on e-commerce platforms exhibit dynamic sentiment patterns driven by temporal and contextual factors.Traditional sentiment analysis methods focus on static reviews, failing to capture the evolving temporal relationship between user sentiment rating and textual content.Sentiment analysis on streaming reviews addresses this limitation by modeling and predicting the temporal evolution of user sentiments.However, it suffers from data sparsity, manifesting in temporal, spatial, and combined forms.In this paper, we introduce SYNGRAPH, a novel framework designed to address data sparsity in sentiment analysis on streaming reviews.SYNGRAPH alleviates data sparsity by categorizing users into mid-tail, long-tail, and extreme scenarios and incorporating LLMaugmented enhancements within a dynamic graph-based structure.Experiments on realworld datasets demonstrate its effectiveness in addressing sparsity and improving sentiment modeling in streaming reviews.
Authors: Xin Zhang, Qiyu Wei, Yingjie Zhu, Linhai Zhang, Deyu Zhou, Sophia Ananiadou
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412887965