Myers-Briggs personality classification from social media text using pre-trained language models

Abstract

In Natural Language Processing, the use of pre-trained language models has been shown to obtain state-of-the-art results in many downstream tasks such as sentiment analysis, author identification and others. In this work, we address the use of these methods for personality classification from text. Focusing on the Myers-Briggs (MBTI) personality model, we describe a series of experiments in which the well-known Bidirectional Encoder Representations from Transformers (BERT) model is fine-tuned to perform MBTI classification. Our main findings suggest that the current approach significantly outperforms well-known text classification models based on bag-of-words and static word embeddings alike across multiple evaluation scenarios, and generally outperforms previous work in the field.

arXiv comment19 pages

References in corpus (5)

• XGBoost: A Scalable Tree Boosting System
• NLTK: The Natural Language Toolkit
• Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models
• Author2Vec: A Framework for Generating User Embedding
• Knowledge Distillation for BERT Unsupervised Domain Adaptation