Document Risk Normalization Using Corpus-Derived Power Scoring
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Solution Overview
Problem
Existing systems fail to detect and mitigate emotional responses to published content before the audience takes action, missing opportunities for corrective measures.
Innovation Solution
A server computing device analyzes a corpus of documents to determine risk factors, calculates a normalized risk score using cosine similarity and power transformations, and sends real-time alerts when the risk exceeds a threshold, enabling proactive responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional emotional response detection methods are used, then detection can occur after audience action, but the opportunity for corrective measures is lost
Solution Approach 1:
The system performs preliminary analysis of documents against a trained corpus to predict emotional responses before they occur. By pre-processing and pre-analyzing content using the risk perception model, the system generates risk scores in advance, enabling stakeholders to take corrective actions before negative emotional responses manifest in the audience.
Solution Approach 2:
The system implements continuous feedback loops where document risk scores are generated, compared against thresholds, and trigger alerts that feed back into the publishing workflow. This closed-loop feedback mechanism enables real-time monitoring and immediate corrective actions based on predicted emotional responses.
2Measurement precision
If a large corpus of documents is analyzed to improve normalization accuracy, then measurement precision improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary training offline using a large corpus of documents to establish baseline statistics (mean, standard deviation) and power risk relationships. This pre-computation phase creates normalized risk models that can then be applied quickly to new documents without requiring real-time analysis of the entire corpus, thus maintaining precision while reducing operational complexity.
Solution Approach 2:
The system transforms raw risk scores into normalized risk scores using power transformations (raising to the power of 0.39) and statistical normalization based on corpus-derived parameters. This parameter transformation approach simplifies the interpretation of risk scores and enables comparison across different document types and contexts, reducing the complexity of risk assessment.
Data Source
AI summary
A system includes at least one processor to ingest a corpus of a plurality of documents that comprises training data, parse each document in the corpus of the plurality of documents to determine a word count and a raw risk for each document, determine a normalized risk for each document in the corpus of the plurality of documents using the word count and the raw risk based on an expected mean and an expected standard deviation based on a power risk that equals raw risk0.39 for the corpus of the plurality of documents, receive a new document, parse the new document to determine a word count and a raw risk, and determine a normalized risk for the new document based on the expected mean and the expected standard deviation based on the power risk that equals raw risk0.39 for the corpus of the plurality of documents.