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

arXiv: https://arxiv.org/abs/2207.04476 · HTML: https://mlsanity.com/paper/2207.04476/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 10 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Semi-supervised sequence tagging with bidirectional language models](https://mlsanity.com/paper/1705.00108/) (2017)
- [Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models](https://mlsanity.com/paper/1907.06333/) (2019)
- [Knowledge Distillation for BERT Unsupervised Domain Adaptation](https://mlsanity.com/paper/2010.11478/) (2020)
- [A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts](https://mlsanity.com/paper/1610.04345/) (2016)
- [Author2Vec: A Framework for Generating User Embedding](https://mlsanity.com/paper/2003.11627/) (2020)
- [Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning](https://mlsanity.com/paper/2107.02759/) (2021)

## Built on by (most cited first, 1 of 1)

- [Who Are You Behind the Screen? Implicit MBTI and Gender Detection Using Artificial Intelligence](https://mlsanity.com/paper/2503.09853/) (2025)

## Abstract (arXiv)

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.