SSAS Framework Addresses LLM Inconsistency for Enterprise Sentiment Analysis

A newly introduced framework, known as Syntactic & Semantic Context Assessment Summarization (SSAS), addresses the difficulties of employing Large Language Models for dependable enterprise analytics, particularly in sentiment prediction. The core challenge stems from the clash between the stochastic, non-deterministic characteristics of LLMs and the need for analytical consistency. This inconsistency, alongside the presence of noisy contemporary datasets, renders sentiment predictions unreliable for strategic business choices. SSAS creates context via an advanced data pre-processing system that imposes a bounded attention mechanism on LLMs. It features a hierarchical classification system comprising Themes, Stories, and Clusters, and utilizes an iterative Summary-of-Summaries (SoS) context computation architecture. This method transforms raw text into high-signal, sentiment-rich prompts. The research was published on arXiv with the identifier 2604.15547v1 as part of a cross announcement.