In recent years, a number of studies have used linear models for personality prediction based on text. In this paper, we empirically analyze and compare the lexical signals captured in such models. We identify lexical cues for each dimension of the M
BTI personality scheme in several different ways, considering different datasets, feature sets, and learning algorithms. We conduct a series of correlation analyses between the resulting MBTI data and explore their connection to other signals, such as for Big-5 traits, emotion, sentiment, age, and gender. The analysis shows intriguing correlation patterns between different personality dimensions and other traits, and also provides evidence for the robustness of the data.
