Bottom-Up and Top-Down: Predicting Personality with Psycholinguistic and Language Model Features

State-of-the-art personality prediction with text data mostly relies on bottom up, automated feature generation as part of the deep learning process. More traditional models rely on hand-crafted, theory-based text-feature categories. We propose a 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. With this approach we achieve state-of-the-art model performance. Additionally, we use interpretable machine learning to visualize and quantify the impact of various language features in the respective personality prediction models. We conclude with a discussion on the potential this work has for computational modeling and psychological science alike.