  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.
