•Predicting users’ openness from text using a topic-emotion-openness mixture model.•Predicting openness in a data-driven manner via Maximum-A-Posteriori estimation.•Topic and emotional intensity are identified from text for openness prediction. Openness to experience, one of the essential individual characteristics, is of great theoretical and practical value in psychological and behavioral domains. Although typical machine learning methods can be utilized to extract individuals’ openness to experience from the large-scale textual data like the unprecedented massive user generated contents (UGCs), they are often regarded as “black boxes” because they are unable to provide knowledge about the influential factors of openness to experience. This is of no help for us to investigate why a particular level of openness to experience is predicted for an individual. In addition, high dimensionality and sparseness of textual data impairs the performance of the typical machine learning method in extracting individuals’ characteristics. In this study, we propose an interpretable data-driven mixture method for qualified modeling and predicting individuals’ openness to experience. The proposed method extends the latent Dirichlet allocation (LDA) to overcome the problem of high dimensionality and sparseness in modeling the textual data, and can effectively extract two influential variables, namely, the topic preference and the expressed emotional intensity, to make an accurate prediction and to help us fully understand individuals’ openness to experience lurking in the textual data. Experimental results indicate the effectiveness of the proposed method in drawing individuals’ openness to experience, and also validate the predictive ability of topic preference and expressed emotional intensity which are indicated in psychological literature to be influential factors of openness to experience.