# Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing

arXiv: https://arxiv.org/abs/2105.11798 · HTML: https://mlsanity.com/paper/2105.11798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 23 of 23)

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- [Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models](https://mlsanity.com/paper/1907.06333/) (2019)
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- [Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling](https://mlsanity.com/paper/2007.04571/) (2020)
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- [A Limited-Memory Quasi-Newton Algorithm for Bound-Constrained Nonsmooth Optimization](https://mlsanity.com/paper/1612.07350/) (2016)
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- [Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study](https://mlsanity.com/paper/1902.03639/) (2019)
- [Matching Theory and Data with Personal-ITY: What a Corpus of Italian YouTube Comments Reveals About Personality](https://mlsanity.com/paper/2011.07009/) (2020)
- [Inferred vs traditional personality assessment: are we predicting the same thing?](https://mlsanity.com/paper/2103.09632/) (2021)
- [On the connection between the conjugate gradient method and quasi-Newton methods on quadratic problems](https://mlsanity.com/paper/1407.1268/) (2014)
- [Improving the data quality in the research information systems](https://mlsanity.com/paper/1901.07388/) (2019)
- [Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification?](https://mlsanity.com/paper/2101.09009/) (2021)

## Abstract (arXiv)

A data-centric approach with Natural Language Processing (NLP) to predict personality types based on the MBTI (an introspective self-assessment questionnaire that indicates different psychological preferences about how people perceive the world and make decisions) through systematic enrichment of text representation, based on the domain of the area, under the generation of features based on three types of analysis: sentimental, grammatical and aspects. The experimentation had a robust baseline of stacked models, with premature optimization of hyperparameters through grid search, with gradual feedback, for each of the four classifiers (dichotomies) of MBTI. The results showed that attention to the data iteration loop focused on quality, explanatory power and representativeness for the abstraction of more relevant/important resources for the studied phenomenon made it possible to improve the evaluation metrics results more quickly and less costly than complex models such as the LSTM or state of the art ones as BERT, as well as the importance of these results by comparisons made from various perspectives. In addition, the study demonstrated a broad spectrum for the evolution and deepening of the task and possible approaches for a greater extension of the abstraction of personality types.