Interpretable Representation Learning for Personality Detection

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.

Multitask Learning for Emotion and Personality Detection

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.

Bottom-up and top-down: Predicting personality with psycholinguistic and language model features

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.

personality detection using bagged SVM over BERT word embedding ensembles

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.