MultiSent-RAG: A retrieval and memory-augmented system for multilingual sentiment processing

Multilingual sentiment analysis remains challenging due to limited labeled data and strong cross-lingual variation. More specifically, the same expression may signal praise in one language but criticism in another, making sentiment detection highly context-dependent. At the same time, large language models (LLMs) often struggle to maintain stable classifications in low-resource and zero-shot settings. Therefore, we introduce MultiSent-RAG, a training-free retrieval-augmented framework that integrates structured sentiment corpora with unstructured multilingual evidence, and MultiSent-RAG-Cache, a semantic memory module that reuses prior label inferences based on embedding similarity. Our study uses 80,000 labeled instances across 12 languages, including low-resource and zero-shot settings. Retrieval augmentation leads to substantial improvements, with F1 gains of up to +0.65 points, and relative gains exceeding 70%–110% over strong LLM baselines. The semantic cache further reveals when memory reinforces consistent classifications and when it risks propagating errors. Beyond improving accuracy, our analysis reveals how retrieval strengthens contextual grounding across languages, while the semantic cache exposes a trade-off between classification stability and error propagation. These findings provide practical insights for designing robust and interpretable multilingual information processing systems.