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ARXIV:2604.15547 · LLM ANALYTICS · SUBMITTED 20 APR · 20:24 UTC · FRESHNESS STALE
ARXIV:2604.15547LLM ANALYTICSSUBMITTED 20 APR · 20:24 UTCFRESHNESS STALESharookh Daruwalla · Nitin Mayande · Shreeya Verma Kathuria · Nitin Joglekar · Charles Weber · arXiv
A framework to improve LLM sentiment prediction consistency by up to 30% through context summarization and bounded attention.
Opportunity summary
Pain A framework to improve LLM sentiment prediction consistency by up to 30% through context summarization and bounded attention.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework to improve LLM sentiment prediction consistency by up to 30% through context summarization and bounded attention. The LLM inconsistency, coupled with the noisy nature of chaotic modern datasets, renders sentiment predictions too…
The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflict between the LLMs' inherent stochasticity (generative, non-deterministic nature) and the analytical requirement for consistency.…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. It achieves this by applying a hierarchical classification structure (Themes, Stories, Clusters) and an iterative Summary-of-Summaries (SoS) based context computation architecture. Code availability is…
LLM Analytics moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
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A framework to improve LLM sentiment prediction consistency by up to 30% through context summarization and bounded attention.
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10.48550/arXiv.2604.15547A framework to improve LLM sentiment prediction consistency by up to 30% through context summarization and bounded attention.
Abstract
The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflict between the LLMs' inherent stochasticity (generative, non-deterministic nature) and the analytical requirement for consistency. The LLM inconsistency, coupled with the noisy nature of chaotic modern datasets, renders sentiment predictions too volatile for strategic business decisions. To resolve this, we present a Syntactic & Semantic Context Assessment Summarization (SSAS) framework for establishing context. Context established by SSAS functions as a sophisticated data pre-processing framework that enforces a bounded attention mechanism on LLMs. It achieves this by applying a hierarchical classification structure (Themes, Stories, Clusters) and an iterative Summary-of-Summaries (SoS) based context computation architecture. This endows the raw text with high-signal, sentiment-dense prompts, that effectively mitigate both irrelevant data and analytical variance. We empirically evaluated the efficacy of SSAS, using Gemini 2.0 Flash Lite, against a direct-LLM approach across three industry-standard datasets - Amazon Product Reviews, Google Business Reviews, Goodreads Book Reviews - and multiple robustness scenarios. Our results show that our SSAS framework is capable of significantly improving data quality, up to 30%, through a combination of noise removal and improvement in the estimation of sentiment prediction. Ultimately, consistency in our context-estimation capabilities provides a stable and reliable evidence base for decision-making.
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PROBLEM
A framework to improve LLM sentiment prediction consistency by up to 30% through context summarization and bounded attention. The LLM inconsistency, coupled with the noisy nature of chaotic modern datasets, renders sentiment predictions too volatile for strategic business decisi...
METHOD
The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflict between the LLMs' inherent stochasticity (generative, non-deterministic nature) and the analytical requirement for consistency....
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. It achieves this by applying a hierarchical classification structure (Themes, Stories, Clusters) and an iterative Summary-of-Summaries (SoS) based context computation architecture. Code availability is fl...
WHY NOW
LLM Analytics moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Sharookh Daruwalla1, Nitin Mayande1, Shreeya Verma Kathuria1, Nitin Joglekar2, and Charles Weber3 1Tellagence Inc.∗ 2Villanova School of Business
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Net Cons. Data Cond. Total Improv. Net Cons. Data Cond. Total Improv. Net Cons. Data Cond. Total Improv. Base ALL ALL Amazon155745 149823 116102 3.6% 0.0% 3.6% 3.5% 3.8% 7.3% 2.5% 25.5% 28.0% Appendix A.1
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conditioning through the filtering of irrelevant and outlier data. The business implications are significant - we’ve implemented SSAS-based prediction mechanisms in multiple domains including marketing [ 25] and supply c
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[8] Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. Large Language Models are Few-Shot Clinical Information Extractors, November 2022. arXiv:2205.12689 [cs]. URL: http://arxiv.org/ abs/2205
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[10] Xiang Deng, Vasilisa Bashlovkina, Feng Han, Simon Baumgartner, and Michael Bendersky. LLMs to the Moon? Reddit Market Sentiment Analysis with Large Language Models, April 2023. URL: https://dl.acm.org/ doi/10
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[14] Sara Rosenthal, Kathy McKeown, and Apoorv Agarwal. Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media, 2014. URL: http://aclweb.org/anthology/S14-2031, doi: 10.3115/v1/S14-2031
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A framework to improve LLM sentiment prediction consistency by up to 30% through context summarization and bounded attention.
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LLM Analytics
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