SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling

Abstract

This paper addresses the challenge of data sparsity in sentiment analysis for streaming user reviews on e-commerce platforms. Traditional methods often treat reviews as static, independent data points, failing to capture the temporal evolution of user sentiments and the relationships between users and products. The authors identify three types of data sparsity that hinder sentiment modeling in streaming environments: temporal sparsity, spatial sparsity, and combined sparsity. To address this, we propose SynGraph, a dynamic Graph-LLM synthesis framework. The framework classifies users into three categories—mid-tail, long-tail, and extreme—based on their interaction patterns and engagement levels to better address their specific sparsity challenges. SynGraph employs a Continuous-Time Dynamic Graph (CTDG) to represent the evolving sequence of user-product interactions and incorporates Large Language Models (LLMs) to synthesize richer user and product representations, helping to alleviate the data gaps inherent in sparse scenarios.

Qiyu Wei 魏启宇

Ph.D student.

My research interests include AI in Semiconductor, LLM agents and Bayesian Optimisation.