In this paper, different models are introduced and evaluated in terms of their capability in understanding psychological context for personality detection which also resulted in a new state-of-the-art in this field.
Our more computationally efficient CNN-based multitask model achieves the state-of-the-art performance across multiple famous personality and emotion datasets, even outperforming Language Model based models.
A state-of-the-art novel deep learning-based model which integrates traditional psycholinguistic features with language model embeddings to predict personality from the Essays dataset for Big-Five and Kaggle dataset for MBTI.
A novel model which feeds contextualized embeddings along with psycholinguistic features to a Bagged-SVM classifier for personality trait prediction. This model outperforms the previous state of the art by 1.04% and, at the same time is significantly more computationally efficient to train.