Personality Trait Detection Using Bagged SVM over BERT Word Embedding Ensembles
This work addresses personality prediction for affective computing applications, offering an incremental improvement in accuracy and efficiency.
The authors tackled automated personality detection from text by developing a model that combines BERT embeddings with psycholinguistic features and a Bagged-SVM classifier, achieving a 1.04% improvement over previous state-of-the-art methods while being more computationally efficient.
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.