Personality Profile Prediction via Ensemble ML and Scientific Ontology
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Solution Overview
Problem
Marketers face challenges in effectively matching consumers with brand offerings due to increasing consumer skepticism towards online advertising, ad-blocking, and the authenticity of influencer endorsements, necessitating improved techniques for determining personality profiles based on online social speech.
Innovation Solution
A system that combines a machine learning model with a scientific personality model using an ensemble approach to predict personality profiles from online social speech data, incorporating supervised learning and ontology-based analysis to enhance prediction accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to determine personality profiles from online social speech, then productivity and scalability of psychometric analysis are improved, but measurement precision and reliability deteriorate due to biases and noise in data-driven predictions
Solution Approach 1:
The patent introduces scientific personality models as intermediary systems that mediate between raw online social speech data and final personality profile predictions. These scientific models encode established psychological theories and serve as a bridge that filters and refines the data-driven predictions from machine learning models, reducing biases and noise while maintaining scalability.
Solution Approach 2:
The patent merges two different approaches: data-driven machine learning models that provide scalability and productivity, and theory-driven scientific personality models that ensure measurement precision and reliability. By combining these complementary approaches, the system achieves both high productivity in processing online social speech and high measurement precision in personality profile determination.
2Measurement precision
If scientific personality models are used alone, then measurement precision and reliability are improved, but productivity and scalability worsen due to manual analysis requirements
Solution Approach 1:
The patent creates a universal system that performs multiple functions: the machine learning component handles large-scale data processing and pattern recognition to ensure productivity, while thescientific personality model component handles theoretical validation and precision measurement. This multi-functional architecture allows the system to simultaneously achieve both high productivity and high measurement precision.
3Measurement precision
If ensemble models combining machine learning andscientific personality models are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the personality profile determination system into distinct modular components: a machine learning model component that processes online social speech data, and ascientific personality model component that applies theoretical frameworks. This segmentation allows each component to specialize in its strength while working together through an ensemble architecture, improving measurement precision without creating unmanageable complexity.
Data Source
AI summary
A method for determining a personality profile of an online user is disclosed. Social speech content data associated with an online user is stored. A machine learning model is used to determine a first personality profile of the online user based at least in part on the social speech content data associated with the online user. A second personality profile of the online user is determined based on the social speech content data using a scientific personality model encoded in an ontology, wherein the ontology encodes statistical relationships between a plurality of words and a plurality of personality traits based on one or more scientific research studies. An ensemble model is applied to determine a third personality profile of the online user based at least in part on the first personality profile and the second personality profile.