  Recently, the automatic prediction of personality traits has received
increasing attention and has emerged as a hot topic within the field of
affective computing. In this work, we present a novel deep learning-based
approach for automated personality detection from text. We leverage state of
the art advances in natural language understanding, namely the BERT language
model to extract contextualized word embeddings from textual data for automated
author personality detection. Our primary goal is to develop a computationally
efficient, high-performance personality prediction model which can be easily
used by a large number of people without access to huge computation resources.
Our extensive experiments with this ideology in mind, led us to develop a novel
model which feeds contextualized embeddings along with psycholinguistic
features toa Bagged-SVM classifier for personality trait prediction. Our model
outperforms the previous state of the art by 1.04% and, at the same time is
significantly more computationally efficient to train. We report our results on
the famous gold standard Essays dataset for personality detection.
