用动态图结构+大模型缓解电商评论中稀疏情感数据问题
SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling
- 构建动态图结构，按用户活跃度分层处理
- 结合大模型增强，提升稀疏场景下情感建模效果
- 适合做实时评论情感分析的研究者和工程师
电商平台的用户评论呈现受时间与上下文影响的动态情感特征。传统情感分析方法仅关注静态评论，难以捕捉用户评分与文本内容之间的时序演变关系。针对流式评论的情感分析虽能建模情感演化，但面临数据稀疏问题，表现为时间、空间及混合形式。本文提出SynGraph框架，通过将用户分为中尾部、长尾部和极端场景，并在动态图结构中引入大模型增强，有效缓解数据稀疏性。在真实数据集上的实验表明，该方法显著提升了流式评论中的情感建模能力。
原文摘要 · Abstract (English)
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 LLM-augmented enhancements within a dynamic graph-based structure. Experiments on real-world datasets demonstrate its effectiveness in addressing sparsity and improving sentiment modeling in streaming reviews.
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