Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles

TL;DR

This paper introduces a novel, efficient personality detection model using BERT embeddings combined with psycholinguistic features and Bagged SVM, outperforming previous methods on the Essays dataset.

Contribution

The work presents a new hybrid model that integrates BERT embeddings with psycholinguistic features for improved personality trait prediction.

Findings

Outperforms previous state-of-the-art by 1.04%

More computationally efficient to train than existing models

Effective on the Essays dataset for personality detection

Abstract

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.