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A data-centric approach with Natural Language Processing (NLP) to predictpersonality types based on the MBTI (an introspective self-assessmentquestionnaire that indicates different psychological preferences about howpeople perceive the world and make decisions) through systematic enrichment oftext representation, based on the domain of the area, under the generation offeatures based on three types of analysis: sentimental, grammatical andaspects. The experimentation had a robust baseline of stacked models, withpremature optimization of hyperparameters through grid search, with gradualfeedback, for each of the four classifiers (dichotomies) of MBTI. The resultsshowed that attention to the data iteration loop focused on quality,explanatory power and representativeness for the abstraction of morerelevant/important resources for the studied phenomenon made it possible toimprove the evaluation metrics results more quickly and less costly thancomplex models such as the LSTM or state of the art ones as BERT, as well asthe importance of these results by comparisons made from various perspectives.In addition, the study demonstrated a broad spectrum for the evolution anddeepening of the task and possible approaches for a greater extension of theabstraction of personality types.

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