Personality Mining from Biographical Data with the "Adjectival Marker" Technique.

The last decade has witnessed significant work in personality mining from lexical cues in social media data. Not much work has yet been undertaken in extracting these lexical cues from biographical data populating social media. Most of this work involves a large crowd of researchers leveraging dictionary-based approaches such as LIWC (which primarily focus on function words). By means of this paper we intend to introduce a novel method of personality mining from social media data called “Adjectival-marker Technique”. This method involves extracting lexical features from descriptive texts (e.g. biographical data) to train a learning model, so as to predict the respective personality traits of the subject. Conceptually, it draws heavily from the last 78 years of work in lexical psychology and the Big Five personality test. However, it is not only a computational variant of the primordial theories of lexical psychology, but is also competent in conferring a substantial accuracy of personality prediction, matching that obtained by psychometric tests. In this study, we propose a variant of the Lexical Hypothesis from psychology. This modified hypothesis is validated by the computational results of personality prediction achieved by the Adjectival Marker Technique discussed below. The paper also discusses some insights illustrating the coherence of people's judgments about the subject's personality (virtual personality). The average accuracy (i.e. matching that achieved by psychometric tests for Big 5) for prediction approximated to Extraversion 82.82% Agreeableness 89.62%, Conscientiousness 92.48% and Imaginativeness/Intellect 81.67%.