New SSAS framework tackles LLM inconsistency problem to make AI sentiment analysis reliable enough for business decisions

Large Language Models suffer from inherent stochasticity that makes sentiment predictions too volatile and unreliable for enterprise analytics

Syntactic & Semantic Context Assessment Summarization (SSAS) framework uses hierarchical classification (Themes, Stories, Clusters) to pre-process data and enforce bounded attention on LLMs

Iterative Summary-of-Summaries architecture creates sentiment-dense context from raw text, improving consistency and signal quality for strategic business use cases