Predictor Proficiency Scoring via Automated Sentiment Analysis
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Solution Overview
Problem
Individuals lack objective criteria to assess the reliability of opinions and predictions, as they are often subjective and not tied to measurable criteria, making it difficult to determine the proficiency of predictors and the sentiment of a community regarding an item.
Innovation Solution
A system and method that scores items based on user sentiment and determines the proficiency of predictors by gathering and analyzing predictions from a community, using a sentiment rating module and user proficiency module to provide community sentiment scores and proficiency rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If subjective opinions and predictions are gathered from users, then the quantity of information available increases, but the reliability and objectivity of the information decreases
Solution Approach 1:
The patent replaces subjective human judgment with an automated computer system that objectively measures and ranks predictors based on their historical accuracy. The system automatically processes predictions, compares them against actual outcomes, and generates reliability scores without human intervention, thereby maintaining high reliability while processing large quantities of information.
Solution Approach 2:
The system implements feedback mechanisms where predictors are continuously evaluated based on their prediction accuracy, and their reliability scores are updated in real-time. This feedback loop allows the system to dynamically adjust the weight and trust level assigned to different predictors, ensuring that only reliable information sources maintain high status while unreliable ones are downgraded.
2Ease of operation
If no objective criteria are used to evaluate predictors, then the ease of operation increases, but the measurement precision of predictor proficiency decreases
Solution Approach 1:
The patent transforms subjective predictor evaluations into objective numerical parameters. The system assigns quantitative reliability scores to predictors based on their historical accuracy rates, converting qualitative assessments into measurable data. This parameter transformation enables precise measurement of predictor proficiency while maintaining ease of operation through automated scoring.
Solution Approach 2:
The system substitutes manual evaluation processes with automated computer-based measurement. The computer automatically calculates predictor reliability based on objective criteria such as accuracy rates, eliminating the need for subjective human judgment while providing precise, quantifiable measurements of predictor performance.
3Productivity
If individual predictions are analyzed without community context, then the productivity of analysis increases, but the reliability of the assessment decreases
Solution Approach 1:
The patent merges individual predictor assessments with community-wide context by analyzing predictions within the broader ecosystem of multiple predictors and items. The system evaluates how individual predictors perform relative to others and adjusts reliability assessments based on community trends and patterns, thereby enhancing the reliability of individual assessments while maintaining high productivity through automated processing.
Data Source
AI summary
The present invention provides systems, methods, computer program products, and combinations and subcombinations thereof for scoring items based on user sentiment and for aiding an investment decision on an item by an individual. The invention includes one or more user devices and a prediction system server having a sentiment rating module, a user proficiency ranking module, a content creation module, and a database. Devices access the prediction system server directly via a communications medium or indirectly through links provided on a third party server.