Personality Prediction System Using Topic Modeling and DISC Profiling
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Solution Overview
Problem
Current systems for predicting human personality from text data struggle to identify the most prominent and less significant traits and fail to efficiently correlate information from multiple sources, lacking deep analysis and automation for business needs.
Innovation Solution
A method and system that utilize topic modeling algorithms to cluster data based on topics of interest, predict high-level personality traits using the Big Five model, and classify them into granular traits using the DISC profiling technique, with a processor-driven approach for efficient data processing and user response learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current systems predict personality traits separately from different data sources, then prediction coverage is broad, but correlation accuracy between multiple personality traits deteriorates
Solution Approach 1:
The patent segments the personality prediction process into distinct modules: data collection from multiple sources, topic modeling for data organization, personality trait prediction using Big Five model, and trait classification using DISC profiling. This segmentation allows each module to specialize in specific tasks while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces topic modeling as an intermediary layer between raw data collection and personality trait prediction. This intermediary organizes and structures data from diverse sources into coherent topics, enabling accurate correlation of personality traits across multiple data sources without direct integration challenges.
2Measurement precision
If systems perform deep analysis of texts based on different topics, then personality trait identification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent divides the complex analysis process into sequential stages: data collection, topic modeling, personality prediction, and trait classification. Each stage processes specific aspects of the data independently, reducing overall processing complexity while maintaining high identification accuracy through specialized analysis at each stage.
Solution Approach 2:
The patent performs topic modeling as a preliminary action before personality trait prediction. This pre-processing step organizes data into meaningful topics and structures information in advance, making the subsequent personality analysis more efficient and accurate without requiring complex real-time processing.
3Productivity
If systems automate personality prediction processing, then productivity increases, but measurement precision of personality traits deteriorates
Solution Approach 1:
The patent replaces manual personality analysis with automated computational models including topic modeling algorithms, Big Five personality prediction models, and DISC profiling classifiers. These automated systems maintain high measurement precision through sophisticated algorithms while dramatically increasing processing efficiency and productivity.
Solution Approach 2:
The patent introduces multiple intermediary processing layers (topic modeling, personality prediction models, trait classification) between raw data and final personality assessment. These intermediaries ensure that automated processing maintains accuracy by systematically transforming data through validated psychological models at each stage.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media for human personality prediction by analyzing information collected from different sources such as social media, call detail record (CDR), email etc. using DISC (dominance, inducement, submission, and compliance) profiling and Big Five personality techniques (openness, conscientiousness, extraversion, agreeableness, and neuroticism). Embodiments in accordance with the present disclosure are further capable of using a self-learning model which learns from user response to the prediction.