Continuous Output Personality Detection Models via Mixed Strategy Training
This work addresses the need for more nuanced personality assessment in applications like AI, psychology, and marketing, though it appears incremental as it builds on existing datasets and model architectures.
This paper tackles the problem of personality detection models producing only binary outputs by developing a novel approach that generates continuous output values using mixed strategy training on the PANDORA dataset. The results show that their models significantly outperform traditional binary classification methods, offering precise continuous outputs for Big Five personality traits.
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.