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

arXiv: https://arxiv.org/abs/2503.04619 · HTML: https://mlsanity.com/paper/2503.04619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://mlsanity.com/paper/2310.15638/) (2023)
- [CoNet: Collaborative Cross Networks for Cross-Domain Recommendation](https://mlsanity.com/paper/1804.06769/) (2018)
- [DDTCDR: Deep Dual Transfer Cross Domain Recommendation](https://mlsanity.com/paper/1910.05189/) (2019)
- [Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users](https://mlsanity.com/paper/2105.04785/) (2021)
- [Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation](https://mlsanity.com/paper/2310.08395/) (2023)
- [Robust Prompt Optimization for Large Language Models Against Distribution Shifts](https://mlsanity.com/paper/2305.13954/) (2023)
- [Contrastive Collaborative Filtering for Cold-Start Item Recommendation](https://mlsanity.com/paper/2302.02151/) (2023)
- [Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey](https://mlsanity.com/paper/2203.10480/) (2022)
- [Generating Data for Symbolic Language with Large Language Models](https://mlsanity.com/paper/2305.13917/) (2023)
- [Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting](https://mlsanity.com/paper/2310.13297/) (2023)
- [Rethinking Attribute Representation and Injection for Sentiment Classification](https://mlsanity.com/paper/1908.09590/) (2019)
- [Improving Document-Level Sentiment Analysis with User and Product Context](https://mlsanity.com/paper/2011.09210/) (2020)
- [Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity](https://mlsanity.com/paper/2403.06139/) (2024)

## Abstract (arXiv)

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.