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

Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS) | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/consistency-analysis-of-sentiment-predictions-using-syntactic-semantic-context-assessment-summarization-ssas

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-20
Score updated: 2026-04-20
Score fresh until: 2026-05-20
References: 0
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID consistency-analysis-of-sentiment-predictions-using-syntactic-semantic-context-assessment-summarization-ssas | Route /signal-canvas/consistency-analysis-of-sentiment-predictions-using-syntactic-semantic-context-assessment-summarization-ssas

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/consistency-analysis-of-sentiment-predictions-using-syntactic-semantic-context-assessment-summarization-ssas

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 6

References: Pending verification

Proof: Verification pending

Freshness state: computing

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

PDF: https://arxiv.org/pdf/2604.15547v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-20T20:24:32.314Z

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Watch and verify: Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS)

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

Verdict

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No public code linked for this paper yet.

Claim map

Strong 6Mixed 0Weak 0

Evidencepartial
Sharookh Daruwalla1, Nitin Mayande1, Shreeya Verma Kathuria1, Nitin Joglekar2, and Charles Weber3 1Tellagence Inc.∗ 2Villanova School of Business
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
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
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
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
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
[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
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
[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
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
[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
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial

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

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