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

Researchers introduce SSAS, a framework that improves LLM consistency for sentiment analysis by applying hierarchical classification and iterative summarization to enforce bounded attention on raw text. Testing on three standard datasets shows the method reduces analytical variance by up to 30%, addressing the fundamental challenge of using non-deterministic LLMs for enterprise-grade analytics.

This research addresses a critical gap in enterprise AI deployment: the tension between LLMs' generative flexibility and business analytics' demand for reproducibility. While large language models excel at nuanced text understanding, their stochastic nature creates unpredictable outputs that undermine confidence in high-stakes decisions. The SSAS framework tackles this by imposing structure before LLM processing rather than attempting to constrain the models themselves.

The approach reflects a broader industry trend toward hybrid systems that combine LLM capabilities with deterministic preprocessing. By organizing raw text into hierarchical layers—themes, stories, and clusters—followed by iterative summary compression, SSAS creates information-dense prompts that guide models toward consistent interpretations. This mirrors similar developments in prompt engineering and retrieval-augmented generation, where external structure compensates for model unpredictability.

For business applications, the 30% improvement in data quality has meaningful implications. Sentiment analysis drives decisions across customer service, market research, and brand management; unreliable predictions waste resources on misidentified trends or sentiment shifts. Organizations currently hesitant to deploy LLM-based analytics due to consistency concerns may find SSAS-like approaches viable, accelerating AI adoption in conservative sectors like finance and healthcare.

The research validates Gemini 2.0 Flash Lite against production datasets, suggesting immediate practical applicability. However, the real test lies in performance on proprietary, domain-specific data where preprocessing assumptions may fail. Future work should examine how the framework adapts to emerging model architectures and whether consistency gains hold under adversarial or deliberately manipulated inputs.

• →SSAS reduces sentiment prediction variance by up to 30% through hierarchical preprocessing and iterative summarization.
• →Framework enforces bounded attention on LLMs without modifying model weights, making it compatible with any large language model.
• →Addresses the fundamental enterprise AI challenge of balancing LLM generative power with analytical consistency requirements.
• →Testing on Amazon, Google, and Goodreads reviews demonstrates robustness across diverse review types and noise levels.
• →Context-driven preprocessing approach aligns with broader industry trend toward hybrid systems combining external structure with LLM inference.