S yn G raph: A Dynamic Graph- LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling (2025)

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 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.

## introduction user reviews on e-commerce platforms provide valuable insights into customer preferences, product performance, and user satisfaction. these reviews reflect users' momentary sentiments and exhibit temporal dynamics as users interact with products over time. traditional sentiment analysis methods, however, predominantly operate in static contexts, where individual reviews are treated as independent data points @xcite @xcite @xcite . this paradigm overlooks the inherent temporal dependencies and evolving nature of user sentiments, which are crucial for capturing the full spectrum of user behavior in realworld scenarios. for instance, as shown in figure 1 , a user's sentiment rating may initially be highly positive (e.g., "i really enjoyed this book") but timeline i really enjoyed this book. the flow was very good and the writing was well done. i'm also glad that the majority of the book was set years after the "dark times", in which crime was nearly allconsuming. 20/04/2015 i was surprised that i liked this book as much as i did. i listened to the audio version of frances fuller, her reading speed was really good; i like a fast reader. ## syngraph framework interpolation of different categories long-tail scenario extreme scenario mid-tail scenario categorization of sparse users extreme scenario long-tail scenario mid-tail scenario step1: local & global user secondorder relationships understanding. step2: global product secondorder relationships understanding. step3: long-tail data synthesis. local bipartite graph global bipartite graph second-order connectivity for user and product local and global graph understanding three key components high-order relation understanding profile generation review understanding for profile generation step1: user profile summarization. step2: product second-order relationships understanding. step3: mid-tail data synthesis. step2: top product choosing. step3: extreme data synthesis. llm-augmented data synthesis ... ... ... ... ... ... ... analysis on streaming reviews by leveraging llm-augmented dynamic graphs. the syn-graph framework consists of three key stages: ## related work data sparsity is a persistent challenge in ecommerce recommendation, where traditional methods enhance preference representations by leveraging user attributes @xcite , integrating social networks @xcite , or transferring knowledge across domains @xcite @xcite . few-shot learning techniques further mitigate sparsity by leveraging semantic associations in graph-structured data @xcite @xcite or employing meta-learning to enhance adaptability 2 https://www.nltk.org/ across domains @xcite @xcite . however, these approaches treat sparsity as a static issue, failing to model the temporal evolution of user interactions and the structural complexity of dynamic streaming data. recent advances in llms offer new opportunities to address sparsity by leveraging their reasoning abilities and extensive pretrained knowledge. prompt engineering has been explored for mathematical qa @xcite , chain-ofthought reasoning @xcite , symbolic data generation @xcite , and hybrid human-llm annotation strategies @xcite . in recommendation systems, llms have been employed to enrich sparse user data by integrating first-order neighbor information @xcite or inferring user profiles from textual descriptions @xcite . however, these methods often overlook the structural complexity of graphs, limiting their effectiveness in leveraging higher-order relationships. moreover, temporal dependencies are frequently ignored, leading to suboptimal modeling of evolving user preferences in streaming environments. despite these advancements, an effective framework that jointly models temporal dynamics, highorder structural dependencies, and llm-based augmentation remains underexplored. our work fills this gap by introducing a unified approach that systematically integrates these factors, offering a more comprehensive solution to the challenges in sentiment analysis on streaming user reviews. ## conclusion we propose syngraph, a dynamic graph-based framework that mitigates data sparsity in sentiment analysis on streaming reviews. by integrating llm-augmented enhancements, it effectively models local and global structures, high-order relationships, and supplementary data. it adapts to different sparsity scenarios through a flexible decomposition and recombination mechanism. experiments on three real-world amazon datasets demonstrate its effectiveness in improving sentiment analysis on streaming reviews under sparse conditions. dation manchester research excellence award (re/24/130017). we also acknowledge the csf3 at the university of manchester for providing gpu resources. xin is supported by the uom-csc joint scholarship.