The evaluation of personality traits allows the study of human behavior in different environments, but it is not a trivial task. In this sense, the Five-Factor Model (FFM) allows, in a global way, the assessment of personality traits of individuals using textual data. However, there is a scarcity of lexical resources for languages other than English, which generated the main research question of this work: "Can models trained to predict FFM personality traits using English textual data show satisfactory results when applied to textual data in other languages?". Therefore, this work aims to answer: (i) Whether Word Embeddings techniques could be used to solve low resources languages problems in FFM personality traits prediction; and (ii) Whether is feasible to train a traditional Machine Learning algorithm with English language textual data and evaluate its performance with Brazilian Portuguese language textual data for FFM personality traits prediction. Thus, the work aims to present an approach in which the models can be used to learn the highest level of abstraction. As results, we observed that the difference in performance between the models trained for personality recognition in English is minimal when used to predict FFM personality traits in Brazilian Portuguese texts. In this task, the Stochastic Gradient Descent model presents the best average results among the FFM personality traits of the models analyzed.