  Due to the sparsity of user data, sentiment analysis on user reviews in
e-commerce platforms often suffers from poor performance, especially when faced
with extremely sparse user data or long-tail labels. Recently, the emergence of
LLMs has introduced new solutions to such problems by leveraging graph
structures to generate supplementary user profiles. However, previous
approaches have not fully utilized the graph understanding capabilities of LLMs
and have struggled to adapt to complex streaming data environments. In this
work, we propose a fine-grained streaming data synthesis framework that
categorizes sparse users into three categories: Mid-tail, Long-tail, and
Extreme. Specifically, we design LLMs to comprehensively understand three key
graph elements in streaming data, including Local-global Graph Understanding,
Second-Order Relationship Extraction, and Product Attribute Understanding,
which enables the generation of high-quality synthetic data to effectively
address sparsity across different categories. Experimental results on three
real datasets demonstrate significant performance improvements, with
synthesized data contributing to MSE reductions of 45.85%, 3.16%, and 62.21%,
respectively.
