Using the Projective Themathic Apperception Test for Automatic Personality Recognition in Texts

Personality Computing is a field related to artificial intelligence that can recognize personality automatically from texts (TB-APR). To accomplish this task, personality inventories are used to build a corpus for supervised machine learning. Although personality inventories are easy options for responding to and rating personality, they lack efficient mechanisms to control omissions (proposital or non-conscient) and adulterations of personality characteristics. In our literature review, we found that state-of-art models have low to moderate correlations between texts and personality scales; we think the flaws of personality inventories can explain that. The present study provides a new TB-APR approach, using a projective technique to build corpus, for avoiding personality inventories’ biases. For this task, we chose the Thematic Apperception Test, rated by the Revised Morvalian System (RMS), producing an appropriate quantity of texts during the application time. The RMS, which is a revision of a qualitative system developed in the 1980’s, provides not yet explored personality dimensions for artificial intelligence and TB-APR models. The proposed model used the TF-IDF technique with some state-of-art classification algorithms. The results are promising, with an AUC-ROC of average 0.853, suggesting strong correlations between the texts and the scales proposed by the RMS.