This study investigates the performance of two deep learning models, RoBERTa and Bi-LSTM, in predicting the Big Five personality traits and capturing the correlations among personality traits from text data. The models’ performance was evaluated on two datasets, PAN 2015 and PANDORA, using RMSE and R² values. The study found that RoBERTa outperformed Bi-LSTM in predicting personality traits on both datasets. However, both models demonstrated varying performances across the two datasets, highlighting the influence of data diversity on model performance. The study also examined the correlations among predicted personality traits and found that the models could capture the sign of the correlations present in the original datasets. However, divergences in the direction of correlations were observed in instances of weak correlations. Besides, the correlations of original datasets and predictions may not align with the findings in psychological studies, which address the importance of annotations when researching the correlations of predicted personality traits.