  Research at the intersection of personality psychology, computer science, and
linguistics has recently focused increasingly on modeling and predicting
personality from language use. We report two major improvements in predicting
personality traits from text data: (1) to our knowledge, the most comprehensive
set of theory-based psycholinguistic features and (2) hybrid models that
integrate a pre-trained Transformer Language Model BERT and Bidirectional Long
Short-Term Memory (BLSTM) networks trained on within-text distributions ('text
contours') of psycholinguistic features. We experiment with BLSTM models (with
and without Attention) and with two techniques for applying pre-trained
language representations from the transformer model - 'feature-based' and
'fine-tuning'. We evaluate the performance of the models we built on two
benchmark datasets that target the two dominant theoretical models of
personality: the Big Five Essay dataset and the MBTI Kaggle dataset. Our
results are encouraging as our models outperform existing work on the same
datasets. More specifically, our models achieve improvement in classification
accuracy by 2.9% on the Essay dataset and 8.28% on the Kaggle MBTI dataset. In
addition, we perform ablation experiments to quantify the impact of different
categories of psycholinguistic features in the respective personality
prediction models.
