AI RESEARCH
Graph-Augmented Retrieval for Cross-Entity Financial Sentiment Analysis: A Comparative Study
arXiv CS.CL
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ArXi:2606.00062v1 Announce Type: new Retrieval-Augmented Generation (RAG) has become foundational for grounding large language models in domain-specific corpora, yet conventional vector-based RAG systems are fundamentally limited in their ability to capture the structured, multi-entity relationships that underpin financial market analysis. This paper presents a comprehensive comparative study of a novel two-hop Graph-RAG architecture versus a standard vector-only baseline for cross-entity financial sentiment analysis.