Evaluating Regression and Neural Networks for Five Trait Text-Based Personality Prediction

The aim of this study is to evaluate the effectiveness of several predictive modeling techniques in mapping the five major personality traits (extraversion, neuroticism, agreeableness, conscientiousness, and openness) from text-based data. The dataset consists of text-based features extracted from publicly available social media posts, providing a realistic basis for personality prediction. Performance was measured using mean absolute error (MAE), mean squared error (MSE), and R² score to evaluate prediction accuracy and generalization quality, along with training time for computational efficiency. The research compares linear regression, ridge regression, random forest, and neural networks implemented in PyTorch. Results indicate that ridge regression and random forest outperform linear regression and neural networks in error metrics and explained variance, with ridge regression offering a favorable balance between accuracy and training time. Random forest achieves slightly better accuracy but with significantly longer training duration, reducing its practicality for real-time use. Despite theoretical advantages in modeling non-linear relationships, neural networks showed suboptimal results, likely due to limited hyperparameter tuning and dataset size. These findings highlight trade-offs among model complexity, accuracy, and efficiency, suggesting ridge regression as a pragmatic choice for current personality prediction from text while encouraging future research on advanced neural architectures and enhanced datasets