Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS)

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Nitin Joglekar and Charles Weber introduce the Syntactic & Semantic Context Assessment Summarization (SSAS) framework, designed to enhance the consistency and reliability of Large Language Model (LLM) sentiment predictions for enterprise analytics. The framework addresses the inherent stochasticity of LLMs and the noise in modern datasets by enforcing a bounded attention mechanism. SSAS employs a hierarchical classification structure (Themes, Stories, Clusters) and an iterative Summary-of-Summaries (SoS) architecture to pre-process raw text into high-signal, sentiment-dense prompts. Empirical evaluation using Gemini 2.0 Flash Lite against a direct-LLM approach across three industry-standard datasets—Amazon Product Reviews, Google Business Reviews, and Goodreads Book Reviews—demonstrated that SSAS significantly improves data quality by up to 30% through noise removal and better sentiment estimation. The methodology consistently yielded 20% improvement over the baseline across six robustness scenarios.

Key takeaway

For AI Architects and Machine Learning Engineers building enterprise-grade sentiment analysis systems, the SSAS framework offers a robust solution to the inconsistency and noise challenges of LLMs. By adopting its hierarchical classification and Summary-of-Summaries approach, you can significantly improve the reliability and quality of sentiment predictions, making them suitable for strategic business decisions. This method reduces the technical burden of managing LLM stochasticity, allowing your teams to focus on deriving actionable insights from large, complex datasets.

Key insights

SSAS improves LLM sentiment prediction consistency by pre-processing noisy data into structured, high-signal prompts.

Principles

• LLM stochasticity conflicts with analytical consistency.
• Noise in datasets degrades LLM performance.
• Bounded attention mechanisms enhance LLM reliability.

Method

SSAS uses hierarchical classification (Themes, Stories, Clusters) and a Summary-of-Summaries (SoS) architecture to filter noise and generate sentiment-dense prompts, guiding LLM attention for consistent output.

In practice

• Classify data into Themes, Stories, and Clusters.
• Implement a Summary-of-Summaries (SoS) aggregation.
• Filter irrelevant and outlier data points.

Topics

• Syntactic & Semantic Context Assessment Summarization
• Large Language Models
• Sentiment Prediction Consistency
• Hierarchical Data Classification
• Noise Reduction

Best for: AI Architect, AI Engineer, Machine Learning Engineer, AI Scientist, NLP Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.