Personality Predictive Lexical Cues and Their Correlations (2021)

Abstract: 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 MBTI 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.

## introduction the notion of personality refers to an individual's characteristic patterns of thinking, feeling, and behaving @xcite . studies have shown that personality influences an individual's language usage @xcite @xcite . hence, language may reveal subtle cues about an individual's personality. since an individual's personality is known to be fairly stable across long periods of time, the relation between personality and language usage is expected to be analyzable given sufficiently large amounts of textual data. ## lexical cue induction in order to determine which words and n-grams are most correlated with specific personality variables, we assume a supervised learning setup with labeled training data that allows us to train a separate linear model for each target variable and identify salient lexical cues along with their weights. we compare several different variants with different feature representations and learning algorithms. ## lexical cue analysis in the following, we empirically assess lexical cues induced using the aforementioned techniques. ## correlation analyses given the aggregated weighted lexical cues induced for each mbti dimension, we seek to assess correlations with extrinsic lexical data covering a series of different phenomena. ## conclusion we have inferred personality predictive lexical signals, i.e., words and n-grams along with their weights, for each mbti dimension. the data is induced based on several diverse mbti datasets, using a variety of feature sets, weighting schemes, and learning algorithms. our focus here is on identifying correlations with other kinds of cues, including big-5 data, as well as emotion, sentiment, and gender-predictive lexicons. we show that naturally occurring text harbors subtle cues exhibiting correlations that largely accord with findings from psychology on self-reported personality correlations. this provides further evidence for the validity of drawing on such naturally occurring data for automated lexical cue induction.