This paper provides a new TB-APR approach, using a projective test to build a corpus. The research of Personality Computing shows that it is possible to recognize personality automatically from texts (TB-APR), using paradigms of supervised learning. As concerns these paradigms, texts need to be labeled by psychometric instruments and, in order to realize this task, personality inventories are used. Personality inventories display great facilities for application and correction, but they do not evince efficient ways of controlling intentional or non-conscious omissions of undesired personality characteristics by the individual, which may explain the low correlations found in literature regarding TB-APR models. In this article, we propose the labeling of a textual corpus using the Z-test projective instrument, in order to mitigate the limitations of inventories, since it is very sensitive and offers the possibility of collective application. The proposed model used bag-of-words techniques, with some state of art machine learning inductors. The results are promising, with AUC-ROC on 0.85 average.