Continuous Output Personality Detection Models via Mixed Strategy Training

TL;DR

This paper introduces a novel mixed strategy training method for personality detection models that produce continuous trait scores, leveraging a large Reddit dataset and fine-tuned RoBERTa models to outperform traditional binary classifiers.

Contribution

It presents a new approach for training personality detection models to output continuous scores, improving accuracy and applicability over traditional binary methods.

Findings

Models predict Big Five traits with high accuracy

Significant performance improvement over binary classification

Enhanced applications in multiple fields

Abstract

The traditional personality models only yield binary results. This paper presents a novel approach for training personality detection models that produce continuous output values, using mixed strategies. By leveraging the PANDORA dataset, which includes extensive personality labeling of Reddit comments, we developed models that predict the Big Five personality traits with high accuracy. Our approach involves fine-tuning a RoBERTa-base model with various strategies such as Multi-Layer Perceptron (MLP) integration, and hyperparameter tuning. The results demonstrate that our models significantly outperform traditional binary classification methods, offering precise continuous outputs for personality traits, thus enhancing applications in AI, psychology, human resources, marketing and health care fields.