Correlation based data unification for personality trait prediction

Radha Divi, Chandra Sekhar Potala


The data points created by users with their online behavior are the primary source of data analysis in various research studies. One of such studies that even more contributes directly or indirectly to other application domains is personality trait analysis. There are well-defined models witnessed to be strong enough to characterize the individual personalities. Some prominent models are the Big Five, Interpersonal Circumplex, and DISC. To make it possible the model should always be fed with a huge amount of data. Here comes the limitation is that the user's online behavior is spread across various platforms resulting in various forms of data points. The segregated form of the data limits the model performance, which is the primary focus of the proposed work. Among these, the big five personality traits model has become so familiar because of its simplicity and dominance. The proposed work illustrated the mapping of various data forms namely measures of the source dataset (MBTI) and Interpersonal Circumplex data to Big five data.


Correlation; Data consolidation; Heterogeneous; Neuroticism; Personality traits; Prediction

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