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

TL;DR

This paper demonstrates that fine-tuning BERT models for Myers-Briggs personality classification from social media text significantly outperforms traditional methods and previous research, advancing NLP applications in personality prediction.

Contribution

It introduces a novel approach of applying pre-trained language models like BERT to classify MBTI personalities from social media text, showing improved accuracy over prior models.

Findings

BERT-based models outperform bag-of-words classifiers.

Fine-tuned BERT achieves state-of-the-art results in MBTI classification.

The approach generalizes well across multiple evaluation scenarios.

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.